Global Quantitative and
Derivatives Strategy
16 May 2014
VIX Risk Premia and Volatility
Trading Signals
Global Derivatives Themes
 Twenty years after the introduction of the index, and 10 years after the
launch of futures, trading in VIX products continues to grow. In this
report we provide an overview of developments in VIX markets, provide
detailed analysis of risk premia related to the VIX, and backtest various
systematic strategies and cross-asset and macro VIX signals.
 Despite a reduction in market volatility levels and decreased demand for
tail hedging, VIX products experienced rapid growth over the past two
years. New products such as the short-term VIX, extended trading hours
and interest in systematic strategies that harvest volatility risk premia are
all contributing to this growth. Historically, a strong demand imbalance
that drove VIX risk premia to record levels in 2012 is increasingly being
met with supply from investors seeking to generate yield.
 There are three main risk premia related to VIX products: Futures term
structure, Options Implied-Realized Volatility, and Volatility Skew. All
of these are implicit costs an investor needs to pay to gain exposure to
VIX hedges. Evaluating the richness/cheapness of these measures is
important when designing a VIX hedge or a systematic strategy.
Historically, term structure and implied-realized volatility premia were
significant and roughly equal to one another. This has made systematic
strategies selling VIX options and term structure profitable. The VIX
Skew premium was more fairly priced, and owning out of the money
calls was a relatively attractive hedge. More recently, the term structure
flattened, reducing the attractiveness of the term structure relative to the
implied volatility premium.
 We tested various signals that can potentially be used to forecast the
VIX: VIX Reversion/Trending – using past levels of the VIX to predict
its future behavior; Cross-Asset Volatility Data – using interest rate,
credit, FX, and commodity volatility to predict future levels of the VIX
(or vice versa); Macro-Economic data: potential use of macro data such
as employment, economic activity, consumer and housing data in
predicting asset volatility; Equity Data: using various equity market data
to forecast future VIX levels. Finally we illustrate the potential use of
the VIX to allocate between Equity Risk Premia such as ROE,
Momentum, Dividend yield, and others.
Global Quantitative and
Derivatives Strategy
Marko Kolanovic
AC
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
AC
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Arjun Mehra (AJ)
(1-212) 622-8030
arjun.mehra@jpmorgan.com
J.P. Morgan Securities LLC
Global Quantitative and
Derivatives Strategy
Marko Kolanovic (Global)
(1-212) 272-1438
mkolanovic@jpmorgan.com
J.P.Morgan Securities LLC
Davide Silvestrini (EMEA)
(44-20) 7134-4082
davide.silvestrini@jpmorgan.com
J.P.Morgan Securities plc
Tony Lee (Asia Pacific)
(852) 2800-8857
tony.sk.lee@jpmorgan.com
J.P.Morgan Securities (Asia Pacific) Limited/
J.P. Morgan Broking (Hong Kong) Limited
Dubravko Lakos-Bujas (Quant)
(1-212) 622-3601
dubravko.lakos-bujas@jpmorgan.com
J.P.Morgan Securities LLC
See page 41 for analyst certification and important disclosures.
J.P. Morgan does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the
firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in
making their investment decision. In the United States, this information is available only to persons who have received the proper option risk
disclosure documents. Please contact your J.P. Morgan representative or visit http://www.optionsclearing.com/publications/risks/riskstoc.pdf.
www.jpmorganmarkets.com
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Table of Contents
Recent Developments in VIX Markets.....................................3
Introduction ............................................................................................................3
Volume Growth and Product Evolution ...................................................................4
VIX Risk Premia and Trading Strategies ................................9
VIX Implied Volatility Premium .............................................................................9
VIX Term Structure Premium................................................................................13
VIX Skew Premium ..............................................................................................16
Evaluating Common VIX Trading Strategies .........................................................18
Signals for Trading Volatility.................................................23
Introduction ..........................................................................................................23
VIX Reversion ......................................................................................................25
VIX and Cross-Asset Volatility .............................................................................28
VIX and Macro Signals.........................................................................................33
VIX and Equity Market Signals.............................................................................35
Equity Factor Allocation based on VIX .................................................................38
2
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Recent Developments in VIX Markets
Introduction
Twenty years after the introduction of the index, and 10 years after the launch of futures, trading in VIX products continues
to grow. VIX futures and options experienced the largest growth in the immediate aftermath of the 2008/9 crisis that
prompted many investors to start hedging tail risk with liquid exchange-traded instruments. Prior to the market crisis, the
instrument of choice for trading index volatility was over-the-counter variance swaps. Investors usually sold variance swaps
to collect the premium of equity index implied volatility, or the premium of index volatility over stock volatility via
correlation trades. As short volatility positions suffered losses during the crisis, and liquidity in OTC markets dried up,
many investors switched to using listed VIX products for accessing volatility. Even after declines in volatility and demand
for tail hedging in 2012, usage of VIX products continued to grow. Currently there is a better balance between hedgers who
are buying the VIX and investors selling the VIX term structure roll-down or options. Figures 1 and 2 show the exponential
growth in VIX call open interest and VIX futures volumes during the low volatility regime over the past 2 years. In this
recent time period, VIX usage has grown on the back of macro directional volatility trading, systematic strategies in which
investors harvest volatility risk premia, and continued use of VIX products for hedging.
Figure 1: Open interest on VIX derivatives has grown steadily since
their launch (in March 2004 for futures, Feb 2006 for options)
Figure 2: ~$300Mn of vega notional in VIX derivatives currently
trading each day
3M average notional open interest ($Mn)
3M average notional turnover ($Mn)
800
250
700
Futures
600
Calls
500
Puts
Futures
200
Calls
150
Puts
400
100
300
200
50
100
0
2004
2006
2008
2010
Source: J.P. Morgan, Equity Derivatives Strategy, Bloomberg.
2012
2014
0
2004
2006
2008
2010
2012
2014
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
The main advantages of using the VIX as a hedge are its negative correlation to most risky assets and convex response to
risky asset selloffs. This convexity makes the VIX suitable for tail risk hedging. VIX-based hedges also have an advantage
of performing well regardless of the asset price path. For instance, owning a put option (fixed strike) on the S&P 500 will
become ineffective as a hedge after a market rally, while a VIX call option or future is likely to have the same response
despite the risky asset’s trending price. However, these advantages come at a cost via the term structure roll-down (in case
of VIX futures), and both roll-down and VIX implied volatility premia in the case of VIX options. In fact, demand for VIX
protection can on occasion make this cost excessive and attract investors looking to harvest the volatility premia associated
with short VIX positions. These short volatility products have gained popularity as investors embraced risk since 2012. The
most common strategies involve selling the VIX term structure, either outright or with application of a risk management
method, employing timing signals (e.g., add or reduce exposure based on the slope of the term structure), or using a relative
value approach (e.g., by selecting the point on the term structure with the steepest roll-down).
Much has been written about the VIX over the past several years. A detailed explanation of the VIX index methodology can
be found in the CBOE VIX whitepaper. The original VIX methodology is based on the variance swap replication portfolio
that was introduced in a paper by Demeterfi, Derman, Kamal and Zou (1999). We have written many reports on VIX
markets, hedging strategies, and opportunistic VIX options and futures trade ideas. For instance, in our introduction to
Options on Implied Volatility, we analyzed VIX and VSTOXX option markets. In our 2012 VIX review we discussed the
3
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
market’s growth, impact on term structure, and VIX settlement issues. Use of the VIX to hedge multi-asset portfolios was
discussed in our report Cross-Asset Hedging with VIX, and VIX ETPs in our 2012 ETF Handbook.
There is no one academic model that has gained broad acceptance for pricing and evaluating VIX options and term
structure. While closed-form models possess mathematical rigor, they cannot capture new trends in the demand for
protection (which determines the implied to realized volatility premium), the ability of liquidity providers to sell volatility
into spikes (properties of mean reversion), the level of assets in the products that roll long exposure (which determines the
dynamics of the term structure), and other fundamental drivers governing option and future pricing. In our approach we use
simplified (non-rigorous) models and stay close to market developments that ultimately determine the properties of the VIX
and its volatility surfaces.
Volume Growth and Product Evolution
Continued Growth and Extended Hours Trading
Volatility exposure in VIX products is now a significant part of overall S&P 500 volatility exposure. In Figure 3 we plot the
total volatility exposure of the VIX (futures + delta weighted options) against the S&P 500 options market (SPX + SPY
options). We note the S&P 500 listed option market has ~$2.2Bn vega notional outstanding vs. ~$600Mn for the VIX
market, so the VIX derivatives market in aggregate is ~25% of the size of the S&P 500 option market in terms of volatility
exposure. Only two years ago the VIX market size was less than 10% of the S&P 500 market.
Over the last few years, VIX bid/offer spreads have been trending lower. Figure 4 shows the average closing (screen) bidoffer spreads for VIX futures and options. Futures spreads have been consistently within 5bps, and option bid-offer spreads
within 10bps. In reality, most trades happen within these bid-offer spreads (note that closing bid-offer spreads also tend to
be higher than intraday bid-offers). While futures bid-offers tend to be relatively steady, option bid-offers tend to widen
during periods of high volatility, given the many additional exposures embedded in options (e.g., gamma and vega risks).
Figure 3: The VIX derivatives market is ~1/4 of the size of the S&P
500 options market in terms of volatility exposure
Figure 4: VIX derivatives bid-offer spreads
Bid-Offer (%)
VIX Level
Vega notional ($Mn)
60
2500
0.3
50
2000
1500
VIX Derivatives (Futures
Notional + Option Delta)
1000
S&P 500 Options
40
0.2
VIX Options
Bid/Offer
500
0
2007
VIX
30
20
0.1
10
2008
2009
2010
2011
2012
2013
Source: J.P. Morgan Equity Derivatives Strategy, OptionMetrics, Bloomberg.
2014
0
Aug, 06
VIX Futures Bid/Offer
Nov, 07
Feb, 09
May, 10
Aug, 11
Nov, 12
0
Feb, 14
Source: J.P. Morgan Equity Derivatives Strategy, OptionMetrics, Bloomberg.
These low spreads (e.g., <5bps for futures) demonstrate how volatility investing has become more efficient, and can be
compared with spreads on variance swaps that were often 25-50bps wide prior to the market crisis.
The CBOE extended VIX futures trading hours in 4Q13, adding an after-hours session from 4:30-5:15pm EST on Monday
through Thursday, and a before-market trading session from 3am to 7am EST (Monday through Friday)1. The after-hours
session is considered to be the start of the following business day, and all transactions during this window are considered to
1
Note VIX options trading hours have not changed; they continue to trade 9:30am-4:15pm EST (Mon-Fri)
4
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
have occurred on the following business day e.g., for settlement purposes. The early session coincides with the open of
European equity markets, allowing European investors better access to the VIX during local market hours (and facilitating
relative value trades such as trading VSTOXX-VIX spreads). Historically, a significant portion of VIX volumes have been
executed by Europe based clients. This can be noted from the average ~25% drop in volume during days when European
markets are closed (Figure 5).
Figure 6 plots the intraday volumes on VIX futures in 15-minute buckets, averaged over the last 3 months. From this chart
we note distinct peaks in activity around the US cash market open and close, and relatively weaker volumes in the beforemarket (3:00-9:30AM EST) and after-market (4:30-5:15PM) trading sessions. Volumes during the new extended hours
sessions have been averaging less than 10% of the volume seen during US equity market trading hours (9:30AM-4PM
EST). There are significantly higher volumes in the before-market session (during European market hours) than in afterhours trading— by a factor of ~4x.
300
-5
-4
-3
-2
-1
0
1
2
3
4
5
Business days before/after a European (non-US) holiday
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Apr-14
10,000
5,000
5PM
80
Feb-14
4PM
350
90
Aft-Mkt
0
Dec-13
3PM
100
5,000
15,000
2PM
400
10,000
1PM
110
20,000
12PM
450
Before-Mkt
15,000
11AM
120
20,000
9AM
500
25,000
10AM
Options
8AM
130
3M average volume in 15min bucket (# of contracts)
7AM
550
Futures
6AM
140
Figure 6: Average intraday VIX futures volumes (inset: 1M avg daily
volumes in the pre and post cash mkt VIX futures trading sessions)
5AM
VIX options volume (000s)
4AM
VIX futures volume (000s)
3AM
Figure 5: VIX derivatives volumes drop ~25% on European holidays
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
VIX ETNs – Balanced Supply-Demand for Term Structure
In 2010/11, we noted how the front end of the VIX term structure was being distorted by large investment into systematic
long volatility ETNs like the VXX and TVIX (e.g., see Impact of VXX US hedging, Sep 2010). At the time, the amount of
VIX futures needed to rebalance the VXX’s portfolio was often close to the total market turnover in VIX futures. Since the
product rebalances by buying the second-month futures and selling the front month every day, this caused the VIX futures
term structure to steepen significantly between the first two contracts, increasing the cost of carry.
Since then, several listed and over the counter products have been created to extract the premia from VIX term structure,
creating a more balanced supply-demand picture. In our view, this shift in positioning is a key driver of the recent absolute
and relative flattening (i.e., compared to the prevailing level of volatility) of the VIX term structure (Figure 7)2. Figure 8
shows the exposure-weighted3 net assets in short-term VIX ETNs, by taking the difference between the funds’ shares
outstanding and short interest. This measure tipped negative in late January, and has been generally declining since 2012.
The main drivers of the recent decreased net long exposure to the VIX short-term futures are a significant increase in assets
in short VIX ETNs XIV and SVXY, and decreased assets / increased short interest in VXX. Note these figures do not
include the exposure of several over-the-counter products that also roll short-term structure exposure.
2
3
See our March 4th Volatility Review for more details.
For example, a 2x levered ETN’s AUM is doubled, a 1x inverse VIX ETN’s AUM is multiplied by -1 in the calculation
5
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 7: The VIX futures term structure has flattened significantly in
the last couple of years…
5
4
3
2
1
0
-1
-2
-3
2nd-1st VIX Futures
-4
Spread (10d avg)
-5
2004
2006
2008
2010
2012
2014
Figure 8: …as weighted net ETN exposure to the VIX front futures
declined
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
4000
3000
2000
Exposure-weighted AUM
in VIX Short-Term ETPs
net of Short Interest
1000
0
-1000
-2000
2009
2010
2011
2012
2013
2014
Despite a flattening of positioning in short-term VIX ETNs, volumes have remained strong (Figure 9), suggesting investors
have become increasingly aware of the cost of carry and are increasingly using these securities as trading instruments, rather
than buy and hold investments. For instance, ETN investors were quick to fade the VIX’s (modest by historical standards)
spikes in Oct-13, Dec-13 and Feb-14. Mid-term VIX ETNs’ recent volume is relatively subdued, averaging less than half
its peak levels in 2011, and equal to only ~1% of the volume recorded in short-term VIX ETNs.
The mid-term VIX term structure has been flattening as well. Medium-term VIX ETNs’ (those with exposure to 3rd-7th
month futures) net exposure has been generally declining over the last 3 years, and currently stands close to zero (Figure
10). The decline can be attributed in part to VXZ, which has seen significant outflows (shares outstanding are down ~70%
from the 2011 peak). Additionally, several over the counter products have been opportunistically buying or selling the
medium-term part of the curve, based on its cheapness/richness relative to the short-end. These relative value strategies
ultimately link the short and medium term slopes, and a flattening of the short-term slope is expected to drive some
flattening of the mid-term slope as well.
Figure 9: Despite flattened positioning, volumes in short-term VIX
ETNs remain strong
4000
3500
3000
Figure 10: Net ETN exposure to the mid-term VIX futures has been
declining for the last 3 years
1200
Short-Term VIX
ETNs 1M Avg
Turnover ($Mn)
800
2500
600
2000
400
1500
1000
200
500
0
0
2009
Exposure-weighted AUM
in VIX Mid-term ETPs net
of Short Interest
1000
2010
2011
2012
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
2013
2014
-200
2009
2010
2011
2012
2013
2014
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
6
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
New ‘VIX’ Indices
Last year the CBOE introduced new S&P 500 short-term and medium-term volatility indices, detailed in Table 1 below.
These indices apply the VIX methodology to S&P 500 options that bracket the target maturity. The index that we believe is
most likely to become popular with investors is the short-term VIX index (ticker VXST). This index interpolates the next 2
listed Weekly options to obtain a 9-day volatility estimate. Weekly S&P 500 options experienced fast growth over the past
few years, as investors use these options to position/hedge ahead of events such as Fed announcements, and important
macro data releases related to employment, housing, consumer, etc. The new short-term VIX measure will be closely
watched by a broader market community to gauge risk related to these types of macro data releases. Of the three new ‘VIX'
indices, currently only VXST has listed futures and options trading.
The VVIX (VIX of VIX) Index applies the VIX calculation methodology to a portfolio of VIX options (whereas the VIX
itself applies it to S&P 500 index options) to derive an estimate of the implied volatility of 1-month VIX options. As such,
the VVIX index provides a published measure of short-dated implied vol of vol, which can help investors assess the cost of
short-dated VIX optionality.
Table 1: VIX-related indices: short and medium horizon S&P 500 Volatility and VIX Volatility Indices
(the VIX is listed at the top for comparison)
Futures
Options
Index
Backfilled to
Listed?
Vega Notional
Open Interest
($Mn)
30-day S&P 500 volatility
Jan-90
Y
403.1
Y
930.6
VXST
9-day S&P 500 volatility
Jan-11
Y
0.09
N
0.18
VXV
93-day S&P 500 volatility
Jan-02
N
0.00
N
0.00
VXMT
6-month S&P 500 volatility
Jan-08
N
0.00
N
0.00
VVIX
30-day VIX volatility
Mar-06
N
0.00
N
0.00
Index
Exposure
VIX
Listed?
Vega Notional
Open Interest
($Mn)
Source: J.P. Morgan Equity Derivatives Strategy, CBOE. Data as of May 12, 2014. See http://www.cboe.com/micro/volatility/introduction.aspx for additional details
A number of VIX-like indices have been launched in recent years to track short-term volatility on other indices, regions,
asset classes, and even single stocks. Of all these indices, only VSTOXX (V2X <Index> on Bloomberg) derivatives have
achieved widespread adoption. V2X is often used to express directional views on regional volatility or to trade relative
value effects such as the term structure slope between VIX and V2X. Currently the size of V2X market is less than 10% of
the VIX market.
In Table 2, below, we list these indices, classified by exposure. There are listed futures and options tracking a few of these
VIX-like indices, but apart from V2X, at the time of writing, volumes in these products are negligible. Most volatility
trading activity for these indices, regions and asset classes occur via options on the underlying (which are then used to
construct their respective ‘VIX’ indices). Despite a lack of trading activity in futures on these cross-asset volatility indices,
spot levels can be very useful to assess relative value between various volatility markets (e.g., they can be used as trading
signals for cross-asset volatility, or used to build valuation and trading models for the VIX).
7
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Table 2: VIX-like indices on other countries, regions and asset classes (the VIX is listed at the top for comparison)
Futures
Index
Exposure
Americas Equities
VIX
S&P 500
VXD
DJIA
VXN
NASDAQ 100
VXO
S&P 100
RVX
Russell 2000
QQV
NASDAQ 100 (QQQ ETF)
VIXC
S&P/TSX 60
VIMEX
Mexico IPC
VXEWZ
MSCI Brazil (EWZ ETF)
VXXLE
Energy (XLE ETF)
Europe Equities
VSTOXX
Euro STOXX 50
VFTSE
FTSE 100
VDAX
DAX
VSMI
SMI
VCAC
CAC
VAEX
AEX
Asia Equities
VNKY
Nikkei 225
VHSI
Hang Seng
VKOSPI
KOSPI 200
VXFXI
China (FXI ETF)
INVIXN
India NIFTY 50
AS51VIX
ASX 200
VXJ
Nikkei 225
Multi-Regional Equities
VXEFA
MSCI EAFE (EFA ETF)
VXEEM
MSCI EM (EEM ETF)
VXGDX
Gold Miners (GDX ETF)
Americas Single Stocks
VXAZN
Amazon.com
VXAPL
Apple
VXGS
Goldman Sachs
VXGOG
Google
VXIBM
IBM
Cross-Asset
OVX
WTI Oil (USO ETF)
GVZ
Gold (GLD ETF)
VXSLV
Silver (SLV ETF)
EVZ
EUR/USD (FXE ETF)
VXTYN
10Y Treasury Notes
SRVX
1Y Swaptions on 10Y USD Rates
Options
Average
Index Level
(200D)
Listed?
Vega Notional
Open Interest
(Local Curcy, Mn)
Listed?
Vega Notional
Open Interest
(Local Curcy, Mn)
14.4
13.6
16.0
13.5
19.0
13.8
13.0
19.2
27.9
17.2
Y
Y
Y
N
Y
N
N
N
Y
N
403.1
0.00
0.60
-0.06
---0.03
--
N
N
N
N
Y
N
N
N
Y
N
930.6
---0.54
---0.00
--
17.9
13.8
15.0
14.0
16.0
15.1
Y
N
N
N
N
N
21.76
------
Y
N
N
N
N
N
29.48
------
25.5
16.6
14.2
24.8
21.4
13.5
25.5
Y
Y
N
N
Y
N
N
0.17
0.00
--0.00
---
N
N
N
N
N
N
N
--------
16.3
23.6
40.3
N
Y
N
-0.20
--
N
Y
N
-0.04
--
31.8
26.1
23.5
23.5
19.4
N
N
N
N
N
------
N
N
N
N
N
------
20.5
19.7
32.1
7.4
5.5
93.2
Y
Y
N
N
N
N
0.01
0.00
-----
Y
Y
N
N
N
N
0.03
0.01
-----
Source: J.P. Morgan Equity Derivatives Strategy, CBOE, Bloomberg. Data as of May 12, 2014.
8
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
VIX Risk Premia and Trading Strategies
The performance of a VIX options or futures strategy will in most cases depend on the direction and timing of VIX moves.
This is clearly true for strategies with outright volatility exposure such as holding long VIX calls or selling the VIX term
structure. In many cases, even the strategies that are initially VIX neutral (such as ATM straddles or beta adjusted longshort future spreads) will acquire residual VIX exposure that can end up dominating the strategy’s PnL.
Even if it is not possible to consistently predict directional VIX moves, one can build an edge into a VIX strategy by having
a view on other factors driving the strategy PnL: the richness or cheapness of VIX option implied volatility, steepness or
flatness of VIX term structure, and richness or cheapness of implied volatility skew. These measures are often correlated
to the VIX, but are also significantly influenced by supply-demand pressures.
Similar to S&P 500 options, end-demand for VIX options and futures comes from hedgers. This demand for VIX protection
gives rise to 3 main risk premia4: Implied volatility premium of VIX options, VIX upside skew, and term structure
premium. Based on our Primer on Systematic Strategies Across Asset Classes, we classify these as Volatility Risk Factors
(factor style: Volatility, factor asset: equity volatility, according to the table on p.12). Demand for VIX options causes
option implied volatility to often trade rich relative to subsequent realized volatility of VIX futures. Specifically, demand
for upside VIX calls causes higher strike options to trade at higher implied volatility. Demand for constant long exposure to
VIX futures requires buying longer dated contracts and selling shorter dated contracts, leading to upward sloping term
structure that usually overestimates future levels of the VIX.
Investors can take advantage of these risk premia when deciding on implementation of directional VIX view, e.g., deciding
on VIX calls vs. VIX futures based on relative view on implied-realized spread and term structure slope. Alternatively, VIX
risk premia can be can be harvested via strategies that systematically sell term structure or hedged VIX options. In this
section we will discuss in some detail each of these risk premia, highlight specific systematic strategies that take advantage
of these premia, and also illustrate how to factor in VIX risk premia when implementing directional VIX views.
VIX Implied Volatility Premium
Given that VIX options are used for hedging (similar to S&P 500 put options), one would expect VIX implied volatility to
trade systematically rich to realized VIX futures volatility. VIX option premia should exist for the following reasons:
1.
There is a demand/supply imbalance for VIX options as investors hedging risky assets (liquidity demanders) tend
to be buyers of VIX call options.
2.
VIX option sellers (liquidity providers) are exposed to spike/tail risk, and thus will demand compensation for
taking on this risk in the form of an implied volatility premium.
In order to quantify the richness of VIX implied volatility, we first compare Black model implied volatility to subsequent
realized daily volatility of VIX futures (considerations on the validity of using the Black model to back out VIX implied
volatility are given in the gray technical box below). Over the last 4 years, the average premium of VIX implied to realized
volatility of a constant maturity 1M future was ~13 points. However, one has to keep in mind that VIX implied volatility
should be compared to the volatility of its underlying asset, which is a future of declining maturity that converges into the
VIX spot at option expiry (rather than a constant maturity future). Taking into account the higher daily volatility of the spot
VIX, the average VIX option implied-realized volatility significantly drops (to just ~1 point average/~5 point median level)
but is still positive ~60% of time (Figure 12).
4
S&P 500 implied volatility also trades persistently rich to realized volatility. Since the VIX level is constructed from S&P 500 options, it
embeds this premium. However, VIX strategies do not have exposure to S&P 500 implied to realized premia as futures and options settle to
the spot VIX (S&P 500 implied volatility level, rather than realized volatility level).
9
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Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 11: VIX volatility exhibits more frequent and sharper spikes
than the VIX itself
180
1M VIX ATM Implied Volatility
50
160
VIX Level (RHS)
45
40
140
35
120
30
100
25
80
20
60
15
40
Apr-10
10
Apr-11
Apr-12
Apr-13
Apr-14
Figure 12: VIX volatility traded on average nearly in-line with
subsequent realized over the last 4 years, but at a positive premium
~60% of the time
200
175
150
125
100
75
50
25
0
-25
-50
-75
Apr-10
Implied-Subsequent Realized Premium
1M VIX ATM Implied Volatility
1M Realized Vol of 2W VIX Future
Apr-11
Apr-12
Apr-13
Apr-14
Source: J.P. Morgan Equity Derivatives Strategy.
Source: J.P. Morgan Equity Derivatives Strategy.
However, a comparison of implied volatility to the realized volatility of a future of declining maturity is not a satisfactory
approach in estimating the VIX option Volatility premium either. The reason for this is the non-normality of VIX returns,
and specifically the mean reversion of the VIX. This mean-reversion property will cause significantly higher daily volatility
of the VIX as compared to, e.g., weekly or monthly volatility. From the perspective of a market maker, selection of the
correct measure of implied-realized volatility premium is dependent on the approach one uses in hedging VIX options such
as daily hedging, weekly hedging or simply holding the option to maturity. From the perspective of an option buyer or an
outright option premia seller, we believe the most relevant measure of the richness or cheapness of VIX implied volatility is
the terminal PnL of a short straddle position. Figure 13 shows the PnL of a short 1M straddle position, which exhibits a
positive premium 80% of the time over the past 4 years, averaging 1.4 vega (1.8 median). This confirms the existence
of a persistent risk premium associated with VIX option implied volatility. Table 3 shows historical statistics of the
implied-realized option premium expressed as unhedged straddle PnL as well as expressed as a spread between daily
implied volatility and realized volatility of a 2-week5 constant maturity future.
Figure 13: Premium of 1M VIX straddle to the subsequent VIX move
Table 3: Historical implied volatility premium statistics
15
1M Straddle PnL
10
1M Straddle Premium
0
5th %ile
Last
1Y
-2.3
-5
5
3Q082Q09
-21.5
25th %ile
-0.4
0.3
-1.2
-4.2
-13.3
-4.9
-10
Median
0.9
1.8
1.9
11.6
4.6
11.0
-15
75th %ile
1.9
3.0
5.1
25.2
19.2
20.7
95th %ile
2.7
5.2
14.1
41.9
36.8
34.9
Average
0.6
1.4
0.5
9.3
0.9
5.5
% Positive
69%
79%
68%
69%
57%
71%
-20
-25
Apr-10
Apr-11
Apr-12
Source: J.P. Morgan Equity Derivatives Strategy.
5
Last
4Y
-3.4
1M ATM Implied subsequent realized 2W
future volatility
Last
Last
3Q081Y
4Y
2Q09
-38.4
-47.4
-44.4
Apr-13
Apr-14
Source: J.P. Morgan Equity Derivatives Strategy.
i.e., corresponding to approximately the average time to maturity of the declining maturity future that underlies a 1M VIX option
10
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Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Estimating VIX Implied Volatility
The implied volatility of VIX options is non-trivial to estimate as there is no market-consensus model available to price
VIX options. The standard Black-Scholes option pricing model depends on a couple of key assumptions that do not hold
in practice with the VIX:

The underlying asset follows a geometric Brownian motion with constant volatility, i.e., returns are normally
distributed

It is possible to buy and sell any amount of the underlying and borrow/lend cash at the riskless rate—in other
words, one can construct a cash and carry arbitrage against the forward price. Since the VIX itself is nontradable, one cannot construct an arbitrage against VIX futures, and hence VIX futures prices are driven by
expectations and demand/supply forces rather than an arbitrage relationship.
The second point can be addressed by using the Black model (aka Black-‘76), which is a variant of the Black-Scholes
model that uses the traded forward price in lieu of calculating a no-arbitrage forward level based on the interest rate (and
dividend yield for income generating assets). The Black model thus incorporates the forward expectations built into the
VIX futures term structure. A comparison of the two models’ equations for pricing calls is given below:
Black-Scholes:
=
Black model:
=
( )−
(
(
(
)−
),
(
)),
=
√
=
√
,
=
− √
,
=
− √
where N() is the cumulative normal function, S is the spot price, F is the forward price, K is the strike, r is the risk-free
rate, σ is the implied volatility, and T is the time to maturity.
The assumption of normally distributed returns doesn’t hold for stocks either, as return distributions exhibit much higher
probabilities of extreme returns than priced into a normal distribution (i.e., fatter tails/higher kurtosis). This property is
one of the factors that contributes to an implied volatility skew (rather than a constant level of implied volatility for every
option). However, returns on index options are relatively close to normal outside of extreme events, and thus the
assumption of normality isn't overly problematic in practice and can be compensated for by modifying skew at maturities
where non-normal effects are more pronounced. The same cannot be said for VIX options, as volatility of volatility
exhibits more frequent and sharper spikes than volatility itself (Figure 11), and the volatility of a VIX future is time
dependent (hence clearly non constant)—it increases as the time to maturity declines due to the mean reversion priced
into the VIX futures term structure. Unfortunately there is no simple option model that would capture properties of mean
reversion, persistence and jumps exhibited by the VIX. For this reason, the volatilities we show in this report are
approximated using the Black model discussed above (this approach is more relevant for market makers) or the terminal
PnL of short straddle positions (this approach is more relevant for an outright option investor). We compare nonnormality from the Black model surface to the one we observe in realized volatility properties of VIX spot and futures.
As discussed, the VIX implied-realized volatility premium can be measured by the PnL of unhedged straddle. To better
understand this premium we have tested its dependence on the absolute level of the VIX. Figure 14 shows the average PnL
of a short straddle position, as well as the PnL volatility (standard deviation). One can see that the PnL is generally higher
for higher levels of the VIX, reflecting the positive relationship between the VIX (volatility) and volatility of volatility (that
determines the price of straddle). However, at higher levels of the VIX the PnL of volatility also increases, resulting in a
relatively constant information ratio. As the Sharpe ratio does not capture the kurtosis of returns, and knowing that the
kurtosis tend to be higher at higher levels of the VIX, we think that investors should generally avoid this premium at very
high levels of volatility.
11
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Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 14: Average PnL of a Short Straddle Position, and PnL
Volatility for different levels of VIX at the time of Straddle Sale
Figure 15: Performance of the J.P.Morgan I-Volemont Strategy
150
4
5
4
3
y = 0.0757x + 0.1767
R² = 0.911
2
Average of 1M Straddle PnL
1
0
120
2
110
0
10
15
20
25
30
35
40
Source: J.P. Morgan Equity Derivatives Strategy.
130
3
1
Volatility of 1M Straddle PnL (Right axis)
140
100
90
Jun-07 Jun-08 Jun-09 Jun-10 Jun-11 Jun-12 Jun-13
Source: J.P. Morgan Equity Derivatives Strategy. Note: All price performance excludes
commissions. Past performance is not indicative of future returns.
Another way to capture the VIX implied volatility premium is through selling straddles or strangles on the VIX and deltahedging them. By delta-hedging, an investor gains a more pure exposure to VIX volatility by eliminating most of the
directional exposure to the VIX. For example, the J.P.Morgan I-Volemont index (JPVOLVOL <Index> on Bloomberg)
reflects the performance of a strategy that sells and delta-hedges VIX strangles daily. Each day the strategy initiates a short
100-110% (of the relevant future level) strangle and delta-hedges the position. The straddle notional is sized to 2.5% of the
strategy value each day. The strike selection is also subject to a smoothing mechanism to avoid concentrated positions on
any particular contract. The index sells the front month strangles until 5 days before each expiry, when it switches to the
second month. The back-tested performance of the I-Volemont index is shown in Figure 15. Since inception in June 2007,
the strategy has returned 5.4% on 6.4% annualized volatility, yielding an Information Ratio of 0.84.
In addition to the persistent richness of VIX options, the implied to realized volatility spread shows strong mean reversion
(Figure 12). This means that an investor can look to opportunistically sell options when the premium is rich, and buy
options when the premium is cheap. Note that the investor can often implement the same directional VIX view by either
selling or buying options (e.g., selling a put or buying a call gives long VIX exposure).
To illustrate the persistence of this premium, one can use the spread of VIX implied volatility to trailing realized volatility
(fixed tenor future) to improve the average performance of a short VIX volatility strategy. For example, over the last 4
years, the average premium one captures on VIX 1M ATM volatility to subsequent realized volatility was ~1 point; if we
only sell the VIX volatility when the implied-realized spread is at a (trailing 3M out-of sample) Z-Score above 2, the
average spread to subsequent realized increases to ~14 points. Table 4 below shows the average volatility premium for
differing out-of-sample Z-Score thresholds over the last 4 years.
Table 4: VIX volatility premium by trailing implied-realized spread
3M Z-Score of 1M Implied
to Trailing 1M Realized
Futures Volatility
Any
Average Premium of 1M
VIX Implied to Subsequent
Realized Volatility
1
> 0.0
-1
56%
> 1.0
3
18%
> 1.5
8
10%
> 2.0
14
4%
> 2.5
21
1%
% of Daily
Occurrences
100%
Source: J.P. Morgan Equity Derivatives Strategy. Based on data over the last 4 years.
12
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Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
VIX Term Structure Premium
The term structure of VIX futures is typically upward sloping; indeed it was upward sloping more than 80% of the time over
the last 10 years. The VIX term structure also reflects the upward sloping term structure of S&P 500 spot implied volatility.
VIX term structure has it own dynamics related to the level of volatility. Changes in term structure reflect strong mean
reversion of volatility. When realized volatility is high, the term structure will often invert in anticipation of a decline
(reversion) of volatility. When volatility is low, the term structure will be upward sloping in anticipation of an increase of
volatility (towards long term averages). As volatility spike episodes (high volatility) are often shorter than periods of low
volatility, the term structure is expected to be upward sloping most of the time. In that sense, an upward sloping term
structure (similar to implied to realized spread) reflects the cost of maintaining long volatility exposure that, absent a term
structure premium, would have a positive convex expected payoff (i.e., would be a free option).
With the VIX term structure on average upward sloping, carrying a long VIX exposure results in a significant cost due to
the roll-down of the term structure. Given the negative correlation between volatility and risky asset prices, this negative
carry can be viewed as the implicit cost of the protection provided by the long volatility exposure. We note from Figure 16
below there is a relatively persistent term structure premium in the VIX curve—a 2-month VIX future decayed on
average by ~1.4 points over the next month and experienced a negative slide 75% of the time, during the last 4 years.
An upward sloping term structure in S&P 500 options is often a result of investors’ preference for longer dated S&P 500
hedges. Everything else equal, investors would prefer longer-dated options in order to cross the positive implied-realized
spread the least amount of time during the duration of hedge. The term structure slope on the S&P 500 is therefore closely
related to the level of the implied to realized volatility premium. Similarly, the VIX term structure slope is related to the
VIX option implied to realized volatility premium. Indeed we saw that the average PnL of unhedged straddles (implied
volatility premia) was 1.4 points over the last 4 years, which is in-line with the 1.4 point term structure premium.
The term structure premium behaves similarly to the VIX implied volatility premium, in that it typically prices in a risk
premium in calm periods, but usually isn’t able to predict a significant volatility spike in advance and thus experiences
significant draw-downs when one occurs. However, the term structure premium typically suffers steeper losses than the
implied volatility premium during VIX spikes.
We additionally note the term structure premium has been generally declining over the last 1 year as the usage of VIX ETNs
has shifted from a large structural long to much more balanced exposure (see the VIX ETNs section on p.5 for more
details). This dynamic has reduced the near-term attractiveness of selling the VIX term structure premium. Table 5 lists
historical statistics on the VIX term structure premium.
13
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Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 16: VIX term structure premium
15
Table 5: Historical stats on the VIX term structure premium (1-month
roll-down of 2M futures)
2M-1M VIX Term Structure
Last 1Y
Last 4Y
3Q08-2Q09
5th %ile
-2.1
-5.4
-20.6
25th %ile
-0.3
0.0
-4.8
Median
1.0
1.6
0.5
75th %ile
1.9
3.5
3.2
95th %ile
4.3
6.6
8.1
Average
0.8
1.4
-1.9
% Positive
71%
75%
55%
Subsequent 1M VIX Future Change
10
3M Average Term Structure Premium
5
0
-5
Source: J.P. Morgan Equity Derivatives Strategy.
-10
2007
2008
2009
2010
2011
2012
2013
2014
Source: J.P. Morgan Equity Derivatives Strategy.
Simple rolling short VIX futures strategies (particularly focused on the short-end of the futures curve) can monetize the term
structure premium, but suffer significant draw-downs during volatility spikes (Figure 17). For example, the strategy
underlying the XIV and XXV inverse-VIX futures ETNs has returned 18% per year since the end of 2005, but experienced
64% annualized volatility, yielding an information ratio of just 0.29. In addition to short exposure to the term structure
slope, these strategies have short gamma exposure similar to other inverse and levered ETFs. Additionally, the strategy
suffered a maximum draw-down of 92% from Feb 2007-Nov 2008, and another loss of 74% in H2 2011. By comparison,
the inverse mid-term futures strategy returned 4.5% per annum on 32% volatility for an information ratio of 0.14 since
2005.
To better understand properties of term structure premium, we have calculated average levels of premium (PnL of a short
term structure position) for different values of the VIX. Figure 18 shows that the term premium tends to increase with the
VIX until the level of ~25, but then sharply drops at higher levels of the VIX. This is a result of larger volatility of volatility
at higher levels of the VIX that can cause steep losses for short-term structure positions. As expected, PnL volatility also
increases with the VIX, causing information ratio to be relatively constant until VIX levels of ~25 and then sharply drop for
higher levels of the VIX.
Figure 17: Inverse short-term VIX strategies like XIV, XXV monetize
the term structure premium but can suffer steep draw-downs during
vol spikes
Inverse short-term VIX futures strategy* performance
450
400
350
300
250
Short-Term Futures
Mid-Term Futures
200
150
100
50
0
2006 2007 2008 2009 2010 2011 2012 2013 2014
Figure 18: Average PnL of the Term structure premium, and PnL
volatility for different levels of VIX at the time of entering the trade
Average of Term Structure
Premium
Volatility of Term Structure
Premium (Right axis)
5
4
6
5
4
3
3
y = -0.0068x + 1.6786
R² = 0.0032
2
2
1
1
0
0
10
15
20
25
30
35
40
Source: J.P. Morgan Equity Derivatives Strategy.
Source: J.P. Morgan Equity Derivatives Strategy. CBOE. * Calculated from CBOE’s S&P 500
VIX Short-Term and Mid-term Futures Indices (SPVXSP and SPVXMTR <Index>)
To improve the risk-adjusted performance of the term structure premium, and address the poor performance at high levels of
the VIX one can cap exposure to spikes (e.g., by buying OTM calls). Alternatively one can look for reliable signals to
14
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Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
minimize or eliminate exposure when risk of a VIX spike is high. We examine signals in greater detail in the Signals for
Trading Volatility section, but note one signal that has generally worked historically is term structure inversion. The frontend of the VIX curve typically inverts for several days ahead of a significant spike (see, e.g., p24 in 2012 VIX review), so
one can use this feature to hopefully avoid significant losses in a term structure premium monetizing strategy by hedging
exposure to the premium when inversion occurs.
The J.P. Morgan V-Volemont index (JPVOVVOL <Index> on Bloomberg) is an example of a strategy that employs
these risk reduction methods, and attempts to monetize both the VIX implied volatility and term structure premia. The
strategy sells VIX call spreads daily (struck at the first strike below the future level and 2X the lower strike), which captures
the natural roll-down of the VIX futures curve. By selling call spreads instead of futures or calls, the strategy has limited
upside exposure during volatility spikes, and since the call spreads are net short delta and gamma, the strategy is selling the
VIX implied volatility premium at the same time.
Figure 19: Performance of the J.P.Morgan V-Volemont Strategy
260
240
220
200
180
160
140
120
100
80
Jun-07 Jun-08 Jun-09 Jun-10 Jun-11 Jun-12 Jun-13
Source: J.P. Morgan. Note: All price performance excludes commissions. Past performance is
not indicative of future returns.
The strategy also employs a signal to attempt to further reduce the incidence of significant losses. When the VIX spot index
is below the 1M weighted average of the 1st and 2nd VIX futures levels, the term structure is inverted and the signal is
considered 'risk off'. In ‘risk off’ mode, the strategy delta-hedges the sold call spreads so that one is exposed only to the
VIX volatility premium (which remained positive most of the time during volatility spikes as noted in Figure 12), and not to
the term structure premium, which tends to experience sharp draw-downs during spikes. The back-tested performance of
the strategy is shown in Figure 19. Since inception in June 2007, the strategy has returned 13.8% on 12.6% annualized
volatility, yielding an Information Ratio of 1.10.
15
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
VIX Skew Premium
As we discussed in our primer on Options on Implied Volatility, VIX options typically exhibit inverse skew due to the
strong positive correlation between volatility and volatility of volatility. In other words, VIX options should exhibit upward
skew as realized volatility of VIX is higher at higher levels of volatility.
Figure 20 shows the trailing 1-month realized volatility of 2-week VIX future changes (modeled based on VIX spot data
since 1989, blue dots) as well as the corresponding implied volatility of VIX options (Black volatility since 2007, red dots)
as a function of the average 1 month VIX level. VIX option implied volatility skew seems to closely follow the historical
relationship of VIX volatility at different levels of the VIX.
Figure 21 shows the 1-month realized volatility of 2-week VIX future returns (modeled based on VIX spot data since1989,
blue dots) as well as implied volatility of VIX options (Black volatility since 2007, red dots) as a function of the average 1month VIX level expressed in terms of % moneyness.
Both charts provide empirical justification for upward VIX skew and indicate that VIX options on average fairly price the
realized skew of VIX distribution (as they display similar slopes of implied and realized VIX volatility). This would
indicate that the VIX skew premium is much smaller than the implied-realized volatility and term structure premia. Based
on this analysis, and the results from our testing of various systematic strategies (presented in the next section), we believe
VIX skew was historically priced on average pretty close to its fair value.
Figure 20: Realized and implied volatility of VIX at different VIX spot
levels
50
VIX Realized Vol vs VIX Level
45
VIX Implied Vol vs VIX Level
y = 0.8937x
R² = 0.8259
Figure 21: Realized and implied volatility of VIX at different levels of
moneyness
200
VIX Realized Vol (%) vs VIX 1M % Chg
180
VIX Implied Skew vs VIX 1M % Chg
40
160
35
140
30
y = 0.8759x
R² = 0.5736
25
120
100
20
80
15
60
10
40
5
20
0
10
15
20
Source: J.P. Morgan Equity Derivatives Strategy.
25
30
35
40
y = 74.905x - 14.102
R² = 0.2255
y = 39.571x + 34.525
R² = 0.1264
0
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Source: J.P. Morgan Equity Derivatives Strategy.
Figure 22 shows the levels of 1M and 2M 120-100% VIX skew. VIX skew tends to move inversely to the level of the VIX,
i.e., it flattens out on significant volatility spikes (e.g., 4Q08, 3Q11). The reason for this is the expected mean reversion of
the VIX, i.e., as VIX volatilities move higher, skew prices a lower probability of the VIX moving even higher. Skew also
tends to steepen as the VIX moves lower, as OTM call premiums are somewhat sticky and the likelihood of an increase
from the low VIX level is higher. This mechanically forces skew higher when the VIX itself moves lower. This is also one
of the reasons why we see 120-100% VIX skew generally exhibiting an increasing trend since 2012. Table 6 lists historical
statistics on the VIX 120%-100% skew levels. One can use these statistics to assess the richness/steepness of the skew,
while having in mind the relationship between the level of the VIX and skew discussed above.
16
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Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 22: Historical VIX skew
30
25
Table 6: Historical VIX skew stats
1M 120-100% Skew
90
2M 120-100% Skew
80
VIX Level (RHS)
70
20
60
50
15
40
10
30
20
5
0
2007 2008
5th %ile
1M 120-100% skew
Last
Last
3Q081Y
4Y
2Q09
15.8
11.7
0.7
2M 120-100% skew
Last
Last
3Q081Y
4Y
2Q09
10.3
6.7
-0.5
25th %ile
20.1
15.1
5.1
12.2
8.7
2.7
Median
22.6
18.0
8.6
13.6
10.8
4.6
75th %ile
24.5
21.3
10.8
14.9
12.6
6.2
95th %ile
27.2
25.2
13.8
17.3
15.6
8.6
Source: J.P. Morgan Equity Derivatives Strategy.
10
0
2009 2010 2011 2012 2013 2014
Source: J.P. Morgan Equity Derivatives Strategy.
17
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Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Evaluating Common VIX Trading Strategies
Investors can select an appropriate VIX trading strategy based on an assessment of the levels of the implied-realized
volatility, term structure and skew premia. Table 3, 5 and 6 give historical percentiles of these measures that can be used to
gauge whether each metric looks rich or cheap within a historical context. An investor who is trying to gauge the relative
attractiveness of the 3 risk premia should look at the recent levels of the premia and compare it to the risk each one carries
(comparing them at the same unit of risk). For instance, being short the 2nd month future to collect the term structure
premium exposes an investor to 100% short exposure to the VIX future. On the other hand, selling an ATM call option
exposes an investor to ~50% short exposure to the VIX future and the premium should be commensurately lower. Selling
an ATM straddle creates a risk exposure of the same magnitude as the short future exposure (same expected PnL volatility,
different directional exposure). Given the same risk exposure of a straddle and term structure trade, one would expect
around the same amount of premia. The skew premium is smaller in magnitude than the implied volatility and term
structure premia.
While our analysis suggests that the VIX skew is priced relatively fairly, even assuming a large premium in terms of implied
volatility points for OTM options, the absolute dollar level of premium is typically lower than for the term structure and
volatility premia (e.g., 1M ATM+5 call options priced at 20 points higher would translate to ~0.3points of skew premium,
compared to the average ~1.4 point term structure/volatility premia). The risk taken by the short skew position is also very
different (exposure to tails) than the risk in futures or ATM options and it is less straightforward to compare term structure
and implied volatility premia to skew premia at the same level of risk.
Table 7 below lists our estimates for the average size of each premium over the last 4 years and last year, measured in VIX
index points per month and scaled to a 100% delta exposure to the VIX.6
Table 7: Comparison of average risk premia
Term Structure
Implied Volatility
Skew
Last 4Y
1.4
1.4
~0
Last 1Y
0.8
0.6
~0
Source: J.P. Morgan Equity Derivatives Strategy.
We note that both the term structure and implied volatility premia declined over the past year. This is a result of the general
decline in realized and perceived market risk (e.g., decline of the VIX, realized volatility, etc.) as well as shift in the
supply/demand balance for VIX-based protection.
Once the view is formed on each of the 3 risk premia, an investor can decide on the most appropriate VIX strategy. Figure
23 presents a guide to VIX strategies based on one’s assessment of the richness of the VIX Volatility and Term Structure
premia. For example, an investor who believes VIX volatility is cheap and the term structure is flat (i.e., that both the VIX
Volatility and Term Structure premia are prone to increase) would choose strategies in the lower left quadrant of the chart. If
the investor believes skew is steep he may buy call spreads or strangles (instead of calls and straddles).
6
Calculated by the following methodology: Term Structure: amount of the average 1M term structure roll-down for 2M VIX futures.
Implied Volatility: the average premium of 1M ATM VIX straddles to the subsequent absolute 1M VIX move. Although the straddle has
zero delta at inception, ~50 delta exposure in both directions leads to the same expected PnL volatility.
18
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 23: Guide to VIX strategies based on VIX Volatility and Term Structure premia
High VIX volatility
Flat term structure
VIX Volatility Premium
High VIX volatility
Steep term structure
Short
Straddle
Short
Put
Short
Call
Short
Strangle
Long Calendar
Spread*
(e.g. Long 2nd month call,
Short 1st month call)
Long
Futures
Short Put
Spread
Long Risk
Reversal
Short Calendar
Spread*
(e.g. Short 2nd month call,
Long 1st month call)
Low VIX volatility
Long
Flat term structure
Call
Long Call
Spread
Short Call
Spread
Short Risk
Reversal
VIX Term
Structure
Premium
Long Put
Spread
Short
Futures
Long
Strangle
Long
Straddle
Long
Put
Low VIX volatility
Steep term structure
Source: J.P. Morgan Equity Derivatives Strategy. * Calendar spreads are based on relative steepness of different parts of the term structure - e.g., the long 2nd/short 1st month call spread is based
on the view that the roll down from 1st future to spot is steeper than the roll-down from 2nd to 1st future
In the light of our analysis of various VIX premia, we also evaluated the historical performance of various systematic
strategies using VIX futures and options over the last 7+ years. The most common application for VIX options is hedging,
given the typically inverse relationship between volatility and risk assets. On the other hand, VIX risk premia can be
monetized by investors seeking to generate portfolio yield. We thus examine the performance of 2 sets of strategies below:
hedging strategies that are typically long volatility and risk premium strategies that are typically short volatility.
VIX Hedging Strategies
The hedging strategies we tested all experienced negative performance over the full backtest period, a result that should be
expected given their insurance nature and the positive average levels of VIX premia. To compare the effectiveness of these
hedging strategies against their cost, we calculate a ratio of the strategies’ beta to a simple rolling long VIX futures strategy
divided by their annualized cost. Strategies with higher ratios offer cheaper protection (i.e., better ‘bang for your buck’).
Figure 24 shows the backtest performance of various VIX hedging strategies since 2007, and Table 8 presents summary
performance and risk statistics.
19
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 24: Performance of systematic VIX hedging strategies7
160
140
120
100
80
60
40
20
0
2007
2008
2009
2010
2011
1M Long Futures
1M 5pt OTM Calls
1M ATMF/+5 Call Spread
1M BuyWrite ATMF+5
1M Short ATMF Puts
Short 1M-Long 2x2M ATM call ratio calendar
2012
2013
2014
1M -1 x +2 Call Ratio (ATMF+2.5/+7.5)
1M ATMF Call
1M ATMF+2.5/+7.5 Call Spread vs ATMF Put
1M Risk Reversals (ATMF+5 Call/ATMF Put)
Long 2M ATMF call, short 1M put calendar
Source: J.P. Morgan Equity Derivatives Strategy.
Table 8: Performance statistics for systematic VIX hedging strategies (options strategies sorted by upside beta/cost)
26.0%
Beta to 1M
Long
Futures
1.00
Upside
Beta* to
Futures
1.00
5.7
Upside
Beta /
Cost
5.7
9.1%
0.17
0.33
10.5
20.3
-4.1%
12.2%
0.37
0.63
9.0
15.2
1M ATMF Call
-10.7%
16.8%
0.59
0.90
5.5
8.4
1M Risk Reversals (long 5pt OTM Call/short ATMF Put)
-11.4%
19.0%
0.71
0.72
6.2
6.3
1M ATMF/+5 Call Spread
-6.5%
6.3%
0.19
0.24
2.9
3.7
1M Buy-Write (long future, short 5pt OTM call)
-11.1%
17.5%
0.57
0.36
5.1
3.2
Long 2M ATMF call, short 1M put
-21.7%
25.6%
0.86
0.61
4.0
2.8
1M ATMF+2.5/+7.5 Call Spread vs ATMF Put
-11.7%
15.8%
0.51
0.33
4.4
2.8
1M Short ATMF Puts
-5.2%
11.0%
0.30
0.09
5.7
1.7
Short 1M-Long 2x2M ATM call ratio
-11.0%
9.6%
0.18
0.09
1.6
0.8
Strategy
Ann.
Return
Volatility
1M Long Futures
-17.4%
1M Short 1x 2.5 pt OTM call, Long 2x 7.5 pt OTM calls
-1.6%
1M 5pt OTM Calls
Beta /
Cost
Source: J.P. Morgan Equity Derivatives Strategy. * Beta only for periods where the VIX increased
7
Backtest methodology: All strategies assume the relevant VIX derivative is entered on the close of the monthly VIX expiry day (30
calendar days before the next S&P 500 option expiry) at the best bid (for shorts) or offer price (for longs) and then held until the
following month’s final settlement. Unless indicated with a multiplier in the strategy (e.g. 1x2 call spreads), each future and option
position has vega notional exposure of $1 each month (based on a starting level of 100 at the beginning of the backtest).
20
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Based on these results, we found the most cost effective hedging strategies over the cycle were buying out-of-the-money
calls, and selling 1x2 call ratios (sell 1x a 2.5 point OTM call and buy 2x 7.5 point OTM calls) which delivered upside
beta/cost ratios of ~3x higher than a simple rolling futures strategy. The OTM calls and 1x2 call ratio strategies reduce
exposure to the VIX implied volatility premium (e.g., compared to an ATM call), instead gaining exposure to fairly priced
VIX skew premium. The conclusion that these strategies delivered the best risk-adjusted returns suggests that the VIX
implied volatility premium is much richer than the skew premium (in-line with our analysis that suggested that the skew
was fairly priced). Strategies that were long futures, ATM options, or some combination of both performed with a similar
exposure to cost ratio, confirming our observation that term structure and implied volatility premia traded at the similar
levels.
VIX Risk Premium Strategies
The risk premium strategies sell at least one of the 3 risk premia we discuss in the VIX Risk Premia section: VIX implied
volatility, skew or term structure premium. We examine the strategies’ risk/reward by comparing Information Ratios, but
also highlight the tail risk one is exposed to through fields for the maximum drawdown and excess kurtosis. Figure 25
shows the backtest performance of various VIX risk premium strategies since 2007, and Table 9 presents summary
statistics.
Figure 25: Performance of systematic VIX risk premium strategies
180
160
140
120
100
80
60
40
20
2007
2008
2009
2010
1M Short Futures
1M Long ATMF/-2pts Put Spreads
Short 1M-Long 2M ATMF call calendar
1M Short Straddle ATMF+2pts
1M Short Strangles (Put ATMF-2/Call ATMF+5)
Straddle calendar: short 1M ATMF put, 2M ATMF call
2011
2012
2013
2014
1M Short ATMF Call
1M Short ATMF/+5pts Call Spreads
1M Short ATMF Straddles
1M Short Straddle ATMF-2pts
1M 1x2 Call Ratio (ATMF+2.5/+7.5)
Source: J.P. Morgan Equity Derivatives Strategy.
21
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Table 9: Performance statistics for systematic VIX risk premium strategies (grouped by Risk Premium sold)
Ann.
Return
Volatility
IR
Beta to
1M Short
Futures
Upside
Beta to
Futures
Downside
Beta
Max
Drawdown
Excess
Kurtosis*
1M Short Futures
8.0%
28.4%
0.28
1.00
1.00
1.00
-61%
17.1
1M Short ATMF Call
5.2%
18.9%
0.28
0.59
0.44
0.65
-46%
-0.5
1M Short ATMF/+5pts Call Spreads
2.3%
5.2%
0.44
0.15
0.17
0.03
-13%
3.2
-1.1%
10.9%
-0.10
0.09
-0.10
0.17
-35%
6.9
Strategy
Risk Premium
Sold
-0.5%
3.2%
-0.16
0.05
0.04
0.06
-10%
-1.1
Term Structure
Term Structure,
Implied Vol
Term Structure,
Implied Vol
Implied Vol, Term
Structure
Term Structure
Short 1M-Long 2M ATMF call calendar
-0.9%
8.3%
-0.11
0.19
0.09
0.23
-21%
28.7
Term Structure
1M Short ATMF Straddles
1.6%
16.5%
0.10
0.27
0.01
0.36
-35%
20.7
Implied Vol
1M Short Strangles (Put ATMF-2/Call ATMF+5)
-0.7%
14.6%
-0.05
0.17
-0.15
0.28
-34%
24.6
Implied Vol
1M Short Straddle ATMF-2pts
3.6%
18.6%
0.19
0.39
0.13
0.48
-37%
26.4
Implied Vol
1M Short Straddle ATMF+2pts
-0.4%
15.0%
-0.03
0.09
0.41
-0.21
-39%
14.4
Implied Vol, Skew
1M 1x2 Call Ratio (ATMF+2.5/+7.5)
-1.9%
9.9%
-0.19
0.18
-0.07
0.26
-24%
66.9
Skew
Straddle calendar: short 1M ATMF put, 2M
ATMF call
1M Long ATMF/-2pts Put Spreads
Source: J.P. Morgan Equity Derivatives Strategy. * Kurtosis is a measure of the fatness of the tails of a probability distribution (the 4th moment)
Over the full cycle, the best performing strategy on a risk-adjusted basis (IR 0.44) was selling call spreads, as the strategy
monetizes both the term structure (due to roll-down) and implied volatility premia, but limits the tail risk by buying the
fairly priced skew premium. However, the absolute return of this strategy was much lower than the short futures and short
ATM call strategies (IR=0.28). These strategies provide relatively pure exposure to the rich term structure and implied
volatility premia, but also had largest drawdown due to short VIX exposure. The almost identical IR of the short future and
short call strategies again confirms parity between the term structure and implied volatility premia during the backtested
time period.
We additionally note the wide dispersion in performance for short straddle strategies based on where they are struck.
Straddles struck at or below the future price significantly outperformed those struck above the forward; the latter
underperformed due to its long VIX delta position, causing it to be long the expensive term structure premium, which
offsets its gains from the short implied volatility premium.
In Table 10 we examine the performance of these strategies over the last 2 years, and note VIX strategies that sold the
implied volatility premium were the best performers, as the term structure premium has become less attractive recently.
Consistent with our observation in the Hedging Strategies section, strategies that sold the skew risk premium
underperformed (both across the cycle, and over the last 2 years), suggesting this premium is relatively small (in fact, likely
comparable to the transaction costs needed to capture it).
Table 10: Performance statistics for systematic VIX risk premium strategies (last 2Y, ranked by IR)
Strategy
Ann.
Return
Volatility
IR
1M Short ATMF Call
1M Short Straddle ATMF-2pts
Straddle calendar: short 1M ATMF put, 2M ATMF call
1M Short ATMF/+5pts Call Spreads
1M Short ATMF Straddles
1M Short Futures
1M Short Strangles (Put ATMF-2/Call ATMF+5)
1M Short Straddle ATMF+2pts
Short 1M-Long 2M ATMF call calendar
1M Long ATMF/-2pts Put Spreads
1M 1x2 Call Ratio (ATMF+2.5/+7.5)
10.3%
8.8%
10.9%
4.3%
10.7%
9.8%
5.2%
7.8%
-0.4%
-1.0%
-1.5%
2.4%
3.7%
4.9%
1.9%
5.0%
5.1%
3.7%
7.8%
2.8%
3.1%
0.5%
4.36
2.42
2.22
2.21
2.14
1.91
1.39
1.00
-0.13
-0.32
-3.18
Beta to
1M Short
Futures
0.37
0.28
-0.79
0.28
-0.66
1.00
-0.54
-1.39
-0.03
0.50
0.05
Excess
Kurtosis
1.5
-0.4
-1.2
3.2
0.5
0.1
1.0
-1.1
3.9
-1.8
5.0
Risk Premium Sold
Term Structure, Implied Vol
Implied Vol
Implied Vol, Term Structure
Term Structure, Implied Vol
Implied Vol
Term Structure
Implied Vol
Implied Vol, Skew
Term Structure
Term Structure
Skew
Source: J.P. Morgan Equity Derivatives Strategy.
22
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Signals for Trading Volatility
Introduction
Many investors have asked us how one can use the VIX to predict market direction or to improve portfolio risk allocations.
VIX traders often look at data such as credit spreads or interest rates volatility in an attempt to predict levels of the VIX,
under the assumption that one of the markets can lead or lag. We often hear statements such as “higher VIX (also called
“fear index”) is leading the equity market lower”, or “widening credit spreads are leading the VIX higher”, etc. In reality,
many of these moves happen at the same time, or the lead-lag relationship happened on one occasion but failed in another.
In this section we tested various signals that can potentially predict the VIX (as well as investigated use of the VIX as a
potential signal for predicting other markets). We tested a number of potential VIX signals grouped in 4 categories listed
below:
1) VIX Reversion/Trending: Using past levels of the VIX to predict its future behavior, i.e., examining autocorrelation
properties of the VIX.
2) Cross-Asset Volatility Data: Volatilities of different asset classes have been highly correlated over the past decade. This
creates the possibility to use interest rate, credit, FX, and commodity volatility to predict future levels of the VIX (or vice
versa).
3) Macro-Economic data: Macro data are highly correlated to the volatility of various assets. We investigated the
usefulness of macro data such as employment, economic activity, consumer and housing data in predicting asset volatility.
4) Equity Data: We explore challenges and opportunities of using various equity market data (such as S&P 500 technical
indicators, option position data, and cross-regional equity volatility) to forecast future VIX levels.
Finally we illustrate the potential use of the VIX to allocate between various Equity Risk Premia such as ROE, Momentum,
Volatility, Dividends, and others.
Rather than designing specific VIX trading strategies and elaborating on detailed implementation, we test the usefulness of
various signals in forecasting VIX levels. For this reason in many of our analyses we simplify or omit trading cost
assumptions, and in some cases we simply aim to establish a causal relationship between a signal and the VIX without
attempts to assess the profitability of a realistic strategy (that would need to include spreads and other costs such as term
structure roll-down). We plan to elaborate in future publications. Many of the relationships that we present here are already
used in our volatility forecasts (e.g., our annual outlook or monthly volatility commentary).
Before analyzing the signals outlined above, we wanted to highlight some of the challenges and potential pitfalls of signal
based VIX strategies.
One challenge with strategies that go long or short the VIX based on a signal is that they may suffer due to the prominent
tails of the VIX returns distribution. Similar to strategies that are systematically short volatility premia (implied volatility,
term structure), a signal based strategy may be caught outright short the VIX at the “wrong time” such as in 2008. Figure
26 illustrates a hypothetical VIX trading strategy based on various equity signals. The strategy that otherwise had a decent
performance, was clearly short volatility into the 2008 spike, leading to a catastrophic loss. Despite the fact that the loss
was recovered relatively quickly, a client invested in this strategy would have likely cut their exposure during the drawdown. This highlights the importance of exposure and leverage control in signal based VIX strategies.
Another and related issue with VIX signals is that a strategy may have a strong backtest performance (and even show
significant information ratios), but only because of one or two episodes of large gains, e.g., during the 2008 and H2 2011
volatility spikes. The problem with these strategies is that it is often hard to tell if the signal worked by chance, or if it was
created only to work during these historical episodes, and whether it will work in the future. Figure 27 shows a strategy of
trading the VIX based on EURUSD volatility. Over the full sample, the Information Coefficient of this signal is significant
23
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
and the performance is positive – however, this was clearly caused by “correct” positioning in the 2008/9 episode in which
FX signals “correctly predicted” the largest VIX spike (and reversion). Unless there is a strong economic rationale and
conviction, we would not recommend pursuing signals that backtest well only on account of a handful of extreme episodes.
Figure 26: Signal based RV strategy caught short VIX at the “wrong
time”
Figure 27: Signal that works only because it worked well during the
tail event
2500
1000
800
2000
600
1500
400
1000
200
0
1989
1993
1997
2001
2005
2009
2013
500
-200
-400
-600
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
0
2000
2004
2008
2012
-500
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
As with all systematic strategies, another risk is the issue of capacity of the strategy (signal) and eventual broad market
awareness of a signal. For instance, the daily rebalancing of VIX ETNs (daily roll of short term index, gamma rebalance of
levered and inverse VIX ETNs) has created predictable flows in VIX futures near the market close. Signal strategies based
on these flows were working very well in 2011. In 2012, the signal weakened as market participants became broadly aware
of these predictable passive flows.
Last but not least are the risks of in-sample or historical biases in signal research and design. When looking for signals
researchers will often start by looking at what has worked before. To mitigate risks of this approach, one can use signal
robustness tests, simplicity tests, and always have a sound economic justification (rather than rationalization) for a signal.
Out of sample performance may suffer because of in-sample biases, but it could also be caused by changes in market regime
or microstructure that causes the signal to stop working.
Despite these potential pitfalls, VIX signals can be traded as stand alone strategies, and can be used to improve VIX risk
premia strategies (e.g., “smarter” ways to be short risk premia) or to subsidize the cost of long volatility exposure in hedging
strategies.
24
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
VIX Reversion
Before looking at the signals coming from other asset classes or macroeconomic variables, we will first see if one can use
past levels of the VIX to forecast future VIX returns. To answer that one needs to study reversion and momentum properties
of the VIX time series.
It is well known that volatility has properties of mean reversion and persistence. Mean reversion of volatility is intuitively
clear as volatility cannot rise above or fall below certain levels. The highest level of volatility is likely limited by the levels
of market circuit breakers and the formula for calculating volatility (e.g., omission of business disruption days , the fact that
price return cannot be lower than -100%). The lowest level of volatility in theory is zero, but in practice it is higher as there
is a minimal level of market activity determined by programmatic index rebalances, derivative position hedging and the fact
that lower volumes lead to relatively higher market impact of individual transactions. Given upper and lower bounds,
volatility is naturally a mean reverting measure. Volatility also tends to persist in regimes such as low, medium and high
volatility. These regimes are driven by business cycles, interest rates cycles, and financial crises.
In our study we have used VIX data going back to 1986 (VXO prior to 1990). Figure 28 shows the correlation between VIX
returns over trailing time periods (such as 1 day, 1 week, 1 month, etc.) and subsequent VIX returns (again over different
time periods). One can see that, as expected, across different time horizons VIX shows strong mean reversion properties.
This can be well noted in the performance of a hypothetical (non-tradable) strategy that buys the VIX after a weekly drop,
and sells the VIX after a weekly increase. Reversion is equally consistent for down moves (long) and up moves (short), as
well as for other volatility indices such as V2X.
Figure 28: Correlation of past and future VIX returns (e.g., 1st row and
5th column shows correlation of 1 day VIX return and subsequent 1
week VIX return, etc.)
Correl.
1d 2d 3d 4d 1wk 2wk 3wk 1M 2M 3M -
1d +
-8%
-23%
-19%
-19%
-14%
-17%
-16%
-15%
-12%
-13%
2d +
-23%
-28%
-25%
-21%
-18%
-23%
-21%
-19%
-16%
-17%
3d +
-19%
-25%
-20%
-17%
-17%
-22%
-21%
-19%
-17%
-18%
4d +
-19%
-21%
-17%
-17%
-18%
-23%
-22%
-21%
-18%
-19%
1wk +
-14%
-18%
-17%
-18%
-19%
-24%
-22%
-21%
-19%
-20%
2wk +
-17%
-23%
-22%
-23%
-24%
-26%
-25%
-26%
-23%
-24%
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
3wk +
-16%
-21%
-21%
-22%
-22%
-25%
-26%
-26%
-25%
-25%
1M +
-15%
-19%
-19%
-21%
-21%
-26%
-26%
-26%
-26%
-26%
2M +
-12%
-16%
-17%
-18%
-19%
-23%
-25%
-26%
-30%
-32%
3M +
-13%
-17%
-18%
-19%
-20%
-24%
-25%
-26%
-32%
-34%
Figure 29: Performance of a hypothetical non-tradable strategy of
VIX and V2X (spot) mean reversion
500
400
VIX Reversion Long
VIX Reversion Short
VIX Reversion L/S
V2X Reversion Long
300
V2X Reversion Short
V2X Reversion L/S
200
100
0
1989
1993
1997
2001
2005
2009
2013
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Clearly one cannot trade the VIX and V2X spot levels, and this strategy therefore cannot be implemented. Volatility
markets to a certain extent anticipate reversion of volatility, mainly via the relationship between the volatility term structure
and level of volatility. VIX futures are slower to rise, and slower to revert than spot VIX. Additionally, there is a negative
carry for a long position at low VIX levels and often negative carry of short positions at high VIX levels (during periods of
inverted term structure). Figure 30 shows the performance of a reversion strategy when taking into account the term
structure flattening/steepening, and Figure 31 shows the strategy implemented with a tradable VIX ETF and V2X Mini
Futures (i.e., taking into account trading costs such as bid-offer and negative carry of long VIX positions).
25
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 30: VIX and V2X generic 1st futures reversion strategy
600
500
Figure 31: Reversion strategy including trading and roll-down costs
400
UX1 Reversion
350
FVS1 Reversion
300
400
80
VXX Reversion
60
FVS Reversion (Cost Included)
40
250
300
200
150
200
0
100
100
0
2004
20
-20
50
2006
2008
2010
2012
2014
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
0
2005
-40
2007
2009
2011
2013
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Despite the challenges of implementing VIX reversion, it is a useful signal in trading volatility and assessing market moves.
For instance, some of the problems of trading VIX futures on a reversion signal can be circumvented by trading the S&P
500 (or Euro STOXX 50) as a proxy for VIX. Such a strategy performed well over time and results for VIX and V2X
strategies are shown in Figure 32. Reversion of the VIX (V2X) is also closely related to reversion of the S&P 500 (SX5E)
returns. However, reversion of volatility indices is more significant than reversion for respective cash indices as shown in
the Figure 33 for the VIX. S&P 500 and SX5E reversion is likely related to microstructure of the derivatives market in
which end users are buying index options and dealers are hedging daily, leading also to daily volatility being higher than
weekly volatility. 8 This level of reversion is not present with Asian indices, where end users are typically selling options as
part of a retail structured product.
Figure 32: S&P 500 and Euro STOXX 50 reversion based on VIX and
V2X signals
500
VIX Reversion vis S&P 500
400
Figure 33: 1-week lagged correlation of S&P 500 and VIX returns
30%
20%
S&P 500 Reversion
10%
VIX Reversion
0%
V2X Reversion vis SX5E
-10%
300
-20%
-30%
200
-40%
-50%
100
0
1995
1998
2001
2004
2007
2010
2013
-60%
1990
1993
1996
1999
2002
2005
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
2008
2011
2014
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
We have been asked about the existence of momentum in volatility and how it could be used to time spikes in volatility. As
Figure 28 shows, we have not found positive serial correlation of VIX returns at any time horizon. Figure 34 shows lagged
correlations analysis (answering the question of whether there are any delayed effects of VIX changes that can lead to
development of momentum, e.g., can a 1 month VIX return influence the 1 month VIX return 3, 6, or 10 months later). We
have not found evidence of VIX trending in either immediate (Figure 28) or lagged returns (Figure 34). Therefore we are
8
See our reports on Market Impact of Derivatives Hedging, Weekly momentum/reversion effect
26
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
concluding that momentum effects in the VIX are not significant, and are likely related to a particular tail event in the past
but cannot be profitably traded in a systematic fashion. An illustration of this is a test of a simple VIX momentum signal we
have seen proposed. The momentum signal is triggered when the VIX is higher than its short-term moving average at the
same time as the short-term moving average is above the long-term moving average.
Figure 34: VIX Reversion at all Lags (e.g., blue line shows correlation
of 2M VIX return, to 2M VIX return at any future day within a Year)
10%
Figure 35: VIX Momentum signal based on crossing moving
averages – it worked in 2008, but failed before and after
-2
1993
5%
-3
0%
-4
-5%
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
-5
1 Day
-10%
-6
1 Week
2 Week
-15%
-7
3 Week
1 Month
-20%
-8
2 Month
-25%
-9
3 Month
-10
-30%
1
51
101
151
201
251
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
This signal worked in the period from 2006 to 2012 (red box) but failed to work outside of it due to the strong mean
reversion of the VIX as well as the cost of rolling long forward volatility exposure.
27
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
VIX and Cross-Asset Volatility
Option implied volatility of various asset classes has been highly correlated over the past decade. Figure 36 shows levels of
short-term implied volatility for FX (EM, DM), Interest rates, Equities (S&P 500, NKY, MSCI EM), Commodities (Gold,
Oil), and Credit spreads. High correlation of volatility levels is a result of the increasing role of macro developments as the
driver of asset prices, as well as increased use of cross-asset strategies such as cross-asset hedging or relative value trading.
High but imperfect correlation of asset volatilities leaves the possibility that volatility in one asset class may lead the others,
and hence its potential for use as a signal.
Figure 36: Volatilities across asset classes are correlated
300
140
120
100
VIX
JPMVXYEM
USDJPYV1M
VNKY
XAUUSDV1M
IRVOL1US
JPMVXYG7
EURUSDV1M
V2X
VHSI
CL1 1M 50D Call
CDX IG 5Y
250
200
80
150
60
100
40
50
20
0
2000
0
2001
2003
2004
2006
2008
2009
2011
2013
Figure 37: Cross-asset signals tested
Volatility Index Asset Class Asset
History
JPMVXYG7
FX
G7 FX
2000
JPMVXYEM
FX
EM FX
2000
EURUSDV1M
FX
EUR
2000
USD JPYV1M
FX
JPY
2000
IRVOL1US
Rates
1Y10Y
2000
V2X
Equity
SX5E
2000
VNKY
Equity
NKY
2000
VXEEM
Equity
MSCI EM
2000
XAUUSDV1M
Commodity
Gold
2005
CL1 1M 50D
Commodity
Oil
2005
CDX HY 5Y
Credit
HY
2005
CDX IG 5Y
Credit
IG
2005
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
To investigate this possibility, we have tested potential causality relationships of 1 month changes in asset volatility. In
other words, we want to see if equity volatility is causing rates volatility, if credit is leading FX volatility, etc.
In our approach we used Granger causality tests, which involve multiple regressions on lagged variables (controlling for
persistency of the independent variable). The challenge of using simple regression analysis as below is that many of the
relationships between volatilities do not satisfy the standard assumptions of linear regressions (Gauss-Markov assumptions).
Therefore, applying these tests may give results pointing to a highly statistical relationship (high t-tests) caused by large
outlier events, while removing the outliers significantly weakens the signal.
When we performed a standard Granger causality test, we obtained a number of significant relationships. Many of these
relationships are significant because an asset led or lagged around the large volatility increases in 2008. A table showing
the in-sample statistical significance of causal relationships between volatility pairs is given in Figure 38. For instance, the
first row shows that during the full sample of the past 15 years, the VIX has been driven by EURUSD and gold volatility,
and last 3 rows indicate that Credit (HY and IG) as well as Oil volatility have been mostly following volatilities in other
asset classes.
28
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 38: Granger causality matrix for cross-asset volatilities at 1M lag
Volatility
VIX
G7 FX
EM FX
EUR
JPY
1Y10Y
SX5E
NKY
MSCI EM
Gold
Oil
HY
IG
VIX
-8.2
9.8
10.7
1.4
9.8
1.7
8.8
3.8
1.1
10.6
13.5
9.4
G7 FX
6.4
-9.1
1.9
0.9
10.3
5.7
7.4
3.2
-2.4
11.0
13.8
14.4
EM FX
2.4
7.7
-10.5
-0.7
13.0
0.9
8.5
2.0
-0.7
13.4
9.6
9.6
EUR
11.2
7.3
11.9
-6.2
8.9
8.9
11.5
9.5
-1.0
12.0
15.3
17.0
JPY
8.6
-2.5
7.0
1.5
-11.2
7.2
10.3
2.7
-1.1
6.2
9.9
12.0
1Y10Y
-0.8
9.5
1.6
9.5
5.3
--0.7
1.6
2.4
0.9
14.6
7.9
7.6
SX5E
3.9
5.6
8.3
7.7
0.8
11.8
-8.4
5.5
2.4
10.1
12.3
9.1
NKY MSCI EM
1.7
8.1
2.7
8.2
4.6
10.3
2.6
6.0
-0.7
5.0
14.5
16.8
-0.9
4.8
-13.6
-1.1
-0.0
5.6
7.9
8.6
16.2
18.0
10.5
12.8
Gold
13.4
13.1
18.2
14.5
11.3
6.0
11.5
17.0
12.9
-9.8
11.9
13.9
Oil
0.9
3.3
2.5
1.9
1.3
-2.9
-0.1
2.7
-2.0
-2.0
-5.2
6.8
HY
-5.6
6.2
1.9
12.0
-0.4
-6.1
-5.2
-4.4
-0.1
1.4
12.9
--6.6
IG
-6.3
2.5
-1.7
5.9
-3.1
-4.3
-6.2
-5.7
-2.1
-2.4
11.7
12.3
--
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
One can also notice that some of the relationships are of mean reversion type (e.g., suggest selling volatility in one asset
class after volatility increased in another asset class) and some will be of trending type (e.g., buying volatility in one asset
after increase of volatility in another). Investors should also keep in mind that some trending relationships (one asset’s
volatility leading the other) may be masked by the strong mean reversion of volatility itself. That is why reversion signals
(that are more often short volatility) generally show better performance than trending signals9. In designing a trading model
investors should balance reversion and momentum signals in order to avoid creating a strategy that will be systematically
short volatility by utilizing only reversion signals.
As we show below, a number of significant lead/lag volatility relationships are driven by the 2008 event, and the
relationship is not consistent in various sub-intervals of the backtest. In other words, one asset’s volatility has been able to
predict 2008 or 2011 crashes for another asset’s volatility, but has not been able to consistently predict month over month
changes over the full sample period.
Figure 39-Figure 44 below illustrate the performance of strategies that trade significant signals from Figure 38. In the
description of the figures we have indicated several lead/lag relationships over the full sample, as well the hypothetical test
of a trading strategy that follows the cross-volatility signal. For instance, Figure 40 shows PnL (in vega points) of trading
V2X based on signals from: EUR, JPY, rates and Gold volatility, and credit spreads.
9
In some cases simple lagged correlation may indicate a reversion signal, while a proper causality test may indicate a trending
relationship.
29
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 39: VIX: Lagging EUR, JPY, G7 FX, VHSI, Gold Volatility;
Leading HY and IG Credit Spreads.
2500
Figure 40: V2X: Lagging EUR, JPY, Rates Volatility, Gold Volatility;
Leading HY and IG Credit spreads.
2000
EURUSDV1M
EURUSDV1M
2000
JPMVXYG7
1500
XAUUSDV1M
1500
XAUUSDV1M
IG 5Y
1000
HY 5Y
1000
IRVOL1US
All Signals
IG 5Y
HY 5Y
500
All Signals
500
0
2000
0
2000
2002
2004
2006
2008
2010
2012
2014
-500
Figure 41: Rates: Lagging VIX, VNKY, EM FX, JPY; Leading Oil
Volatility, HY and IG Spreads.
0
2000
6000
VHSI
IG 5Y
2000
All Signals
2002
2004
2006
2008
2010
2012
2014
VNKY
XAUUSDV1M
All Signal
0
2005
2007
2009
2011
2013
-4000
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
JPMVXYG7
V2X
Figure 44:EURUSD Volatility: Lagging VIX, Rates Volatility, V2X, Gold
Volatility and IG Credit Spreads
1000
800
VHSI
600
CL1 1M 50D Call
HY 5Y
400
All Signal
VIX
IRVOL1US
V2X
XAUUSDV1M
IG 5Y
All Signals
200
100
-1002005
EURUSDV1M
-2000
Figure 43: Gold Volatility: Lagging V2X, VHSI; Leading G7 FX
Volatility and HY Credit
300
VIX
VHSI
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
500
2014
V2X
4000
-2000
700
2012
JPMVXYG7
JPMVXYEM
-1000
900
2010
Figure 42: IG Credit Spreads: Lagging VIX, V2X, Rates Volatility,
G7FX, Gold Volatility
HY 5Y
1000
2008
-500
8000
VIX
CL1 1M 50D Call
2000
2006
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
3000
2004
-1000
-1000
4000
2002
2007
2009
2011
-300
-500
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
2013
0
2000
2002
2004
2006
2008
2010
2012
2014
-200
-400
-600
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
30
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
The preceding figures show that there is hardly any asset volatility that can consistently (in and out of sample, and in various
sub-samples) lead volatility of other asset classes. This can also be interpreted that there is no asset class in which traders are
consistently “smarter” or have better foresight of future crises than traders in other asset classes. However, we can also see
that for each asset class there was always another asset class (albeit different at different times) that could predict a volatility
increase at a certain point in time. This is also intuitive as some of the major market crises started in a particular asset class
such as the 2008 US subprime crisis, 2011 European crisis, EM crisis (Asia/Russia ’97/98), or ’70s crises caused by
Energy/geopolitical concerns.
This observation can be used to construct a signal to control risk in highly leveraged strategies such as selling volatility. We
have constructed such a signal that simply counts the percentage of asset classes for which volatility is increasing (i.e., when
the signal is 0 volatility in all asset classes is declining, and when the signal is 100% volatility in all asset classes is
increasing). Figure 45 shows such a signal (using volatilities from Figure 37) alongside historical levels of the VIX index.
We can see that the signal was increasing before virtually every significant spike in the VIX over the past 15 years.
Specifically, before the large VIX spike in 2008, volatility of every single asset class in our sample was rising (i.e., the
signal was 100%).
Using this signal to control selling volatility is illustrated in Figure 46. We designed a simple strategy that sells 1-month
S&P 500 variance swaps when the signal is below a particular threshold. We can see that refraining from selling variance
when 70%, 80%, or even 90% of asset classes show increases in volatility can significantly improve the risk-adjusted
performance of short volatility strategies.
Figure 45: Risk signal (% of assets for which implied volatility
increased - left axis), and the VIX (right axis)
Figure 46: Performance of short S&P 500 volatility strategy
controlled by cross-asset risk signal
1
80
450
0.9
70
400
0.8
60
350
0.7
50
0.6
40
0.5
30
0.4
20
0.3
10
0.2
0
0.1
-10
0
2000
-20
2002
2004
2006
2008
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
2010
2012
2014
Signal
Max
Min
Stdev
Average
IR
0.3 0.4 0.5 0.6 0.7 0.8 0.9
12.1 12.1 14.5 15.2 15.9 15.9 15.9
-6.1 -8.3 -13.3 -15.7 -16.4 -17.2 -20.7
2.0 2.4 2.8 3.1 3.4 3.7 4.0
1.0 1.5 1.7 2.0 2.3 2.3 2.4
0.52 0.61 0.61 0.65 0.68 0.64 0.62
0.95
1
15.9 15.9
-27.3 -43.4
4.3 5.5
2.5 2.2
0.57 0.40
300
250
200
150
0.5
0.6
0.7
0.8
0.9
1
100
50
0
2000
2002
2004
2006
2008
2010
2012
2014
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Analyzing in more detail how the signal works in practice, we find that the signal eliminates selling of volatility into spikes,
which will many times result in a missed opportunity to sell volatility at peak levels, but should also avoid selling volatility
ahead of nearly all major market crises. This does not materially change the average return of the strategy, but significantly
reduces the draw-downs and overall volatility of returns. Figure 47 shows the average changes of the VIX following a
certain reading of the cross-asset risk control signal—on average, the level of the signal does not correlate with subsequent
performance of the short volatility strategy. However (as shown in Figure 48), following higher readings of the signal, the
short vol strategy has significantly higher volatility.
31
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 47: Average returns are not strongly affected by the signal
threshold (sometimes hit, sometimes recovery)…
Figure 48: …but risk for short volatility strategies significantly
increases following high signal readings
1.5
10
1
0.5
8
0%
23%
46%
67%
85%
Std. of Subsequent 30D VIX Change
6
5
Avg. Subsequent 7D VIX Change
4
-1.5
Avg. Subsequent 14D VIX Change
3
-2
Avg. Subsequent 30D VIX Change
-1
Std. of Subsequent 14D VIX Change
7
0
-0.5
Std. of Subsequent 7D VIX Change
9
-2.5
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
2
1
0
0%
23%
46%
67%
85%
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
32
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
VIX and Macro Signals
Volatility regimes and the transition between volatility regimes are often caused by macroeconomic events and cycles. For
this reason, analyzing macro data and their changes may provide useful signals to manage volatility strategies. Macro data
are typically released at low frequency and at different dates during a month, often with a substantial lag, which makes it
difficult to use in short-term systematic volatility trading models. However, macro data can be very useful in making longer
dated VIX forecasts.
In our analysis we studied how US macro data affect the VIX and Interest rate volatility. Macro data that we considered can
be divided into four different categories: employment data, business activity, consumer, and housing data. We selected 18
macro time series with at least 30 years of time history and broad investor following. The list of macro data that are
correlated to the VIX and that we considered as potential signals for volatility trading are given in Figure 49.
Performing a Granger causality test for this set of data shows several causality relationships between the various macro time
series, VIX and interest rate volatility. To avoid potential issues with backfilling the macro time series and their potential
revisions, we looked at the impact of macro data on the VIX and interest rates volatility with a 2-month lag. While this has
arguably weakened many of our signals, it ensured that we are not using data that were not available at the time. Figure 49
shows t-statistics for both Granger causality tests and simple lagged correlations. We can see that volatility indices are
impacted by changes in non-farm payrolls, leading indicators, industrial production and factory orders even with a lag of
two months. Generally improvements of these macro data put downward pressure on volatility.
Figure 50 shows the hypothetical (non-tradable) performance of a simple model that trades spot interest rate volatility
(IRUS1VOL) based on monthly changes in the VIX, payrolls, leading indicators, IP and factory order data.
Figure 49: Potential macro signals for the VIX and interest rate
volatility
VIX
Macro Data Time series
VIX
Interest Rate Volatility
Change in Nonfarm Payrolls
Initial Jobless Claims
Change in Manufact. Payrolls
Continuing Claims
ISM Manufacturing
Philadelphia Fed.
Chicago Purchasing Manager
Leading Indicators
Industrial Production
Factory Orders
NFIB Small Business Optimism
U. of Michigan Confidence
Consumer Confidence
Personal Spending
Total Vehicle Sales
Building Permits
Housing Starts
New Home Sales
Grang. t
-0.0
-2.3
0.3
-2.0
-0.3
-0.6
-0.4
0.1
-2.1
-3.7
-2.3
-0.3
0.8
1.8
-0.2
0.2
-0.6
-0.6
-0.9
2M Lag t.
--1.8
-1.9
-0.6
-0.4
-1.0
-0.2
1.2
1.8
0.9
-1.9
-1.2
0.2
0.9
1.7
1.0
1.4
0.0
0.8
1.0
IRVOL1US
Grang. t
2M Lag t.
2.8
-0.2
---2.6
-1.5
1.2
1.9
-2.5
1.8
-0.1
1.1
-1.7
0.1
-1.3
0.6
-0.8
0.8
-3.7
-2.7
-3.3
-0.9
-1.7
-1.7
-0.4
-2.1
-0.3
-0.4
0.3
-0.7
-0.4
-0.9
-0.1
-0.1
-0.3
-2.4
-0.1
-0.6
-0.2
0.9
Figure 50: Trading interest rates volatility based on macro data
300
VIX Median
250
200
150
Change in Nonfarm Payrolls
Leading Indicators
Factory Orders
All Signals
100
50
0
1988
1991
1994
1997
2000
2003
2006
2009
2012
-50
-100
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Perhaps more promising than looking at the lagged impact of monthly changes in macro variables, is to look at forecasting
volatility levels based on the historical relationship between the VIX and macro data. For instance, one can use a trailing
regression to calculate a “macro implied” level of the VIX and compare it to the actual level of the VIX. If the “forecasted”
value is higher an investor can go long volatility, and if the forecasted value is lower the investor goes short volatility.
Figure 51 shows the hypothetical performance of a strategy that buys the VIX when it appears cheap relative to an out-ofsample estimate, and sells it when it appears expensive. We have performed this exercise for 18 different macro
fundamental time series and find fairly consistent positive performance, indicating that macro fundamentals do indeed drive
the VIX (i.e., the VIX is often converging to macro fundamentals). These models can be used to decide on a tactical
allocation to volatility, or separately as a signal to enhance strategic long or short volatility positions. Using the same set of
33
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
US macro signals also works (although the relationship is weaker) in forecasting Euro STOXX 50 volatility (V2X) as
shown in Figure 52. However, we found limited predictive power of US macro signals when applied to FX and commodity
volatility.
Figure 51: Trading VIX based on US macro signals
140
120
100
80
Figure 52: Trading V2X based on US macro signals
80
Change in Nonfarm Payrolls
Initial Jobless Claims
Change in Manufact. Payrolls
Continuing Claims
Philadelphia Fed.
Chicago Purchasing Manager
Leading Indicators
Industrial Production
Factory Orders
NFIB Small Business Optimism
U. of Michigan Confidence
Consumer Confidence
Personal Spending
Total Vehicle Sales
Building Permits
Housing Starts
New Home Sales
AVERAGE
AVERAGE
60
40
60
Initial Jobless Claims
Change in Manufact. Payrolls
Continuing Claims
Philadelphia Fed.
Chicago Purchasing Manager
Leading Indicators
Factory Orders
NFIB Small Business Optimism
U. of Michigan Confidence
Consumer Confidence
Personal Spending
Total Vehicle Sales
Building Permits
Housing Starts
New Home Sales
20
40
0
2002
20
0
1988
Change in Nonfarm Payrolls
2005
2008
2011
2014
-20
1991
1994
1997
2000
2003
-20
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
2006
2009
2012
-40
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
34
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
VIX and Equity Market Signals
In addition to cross-asset, macro and VIX based signals, we analyzed various equity market data and their ability to predict
VIX levels. We tested several groups of equity signals that can be roughly divided into the following categories: Implied
volatility signals, Realized Volatility/Realized Risk signals, S&P 500 Options positioning signals, Market Technical signals
derived from S&P 500 levels, and signals from equity markets in other regions. We tested monthly signals, i.e., looking at
how a 1-month change in a variable impacts the VIX over the subsequent month.
The various signals we tested are shown in Figure 53. For implied volatility measures we used 1M implied-realized
volatility spread, term structure slope (3-1Month, and 12-3 months), and 3M 95-105% implied volatility skew. Realized risk
measures used were: realized S&P 500 volatility, realized stock correlations, S&P 500 autocorrelation, and Skewness and
Kurtosis of S&P 500 returns over a trailing 1 month period. Option data included P/C open interest ratio, P/C volume ratio
and P-C aggregated option gamma. For market technical indicators we examined S&P 500 RSI, Market Volume and
Advance-Decline time series. Finally we tested for cross-market spillover effects and tested R2000, Nasdaq 100, Euro
STOXX 50, Nikkei and Hang Seng implied volatility time series as potential drivers of future VIX levels.
The matrix in Figure 54 shows a Granger causality test at a one month lag calculated over full sample period (in-sample).
For instance, the first row lists potential drivers of the VIX and contains the respective full sample t-statistics. The first
column shows the other direction of a causal relationship, i.e., t-stats for the VIX as a driver of other variables.
One has to keep in mind that many of the signals will appear significant due to a contemporaneous relationship with the
VIX and strong mean reversion of VIX itself. For instance, some of the trending signals that imply that an increase in one
variable leads to an increase in the VIX, such V2X or VHSI, may show poor results in a backtest given the strong mean
reversion of the VIX itself (i.e., lagged correlation is negative, but Granger correlations positive). While the performance of
a simple strategy that buys VIX after a V2X increase appears to be a losing strategy over full sample, its importance is in
offsetting the risk of signals that will largely exploit reversion of Volatility.
Figure 53: VIX Equity market signals tested (and historical signal
availability)
SPX Implied - Realized
3-1 Implied SPX TRMS
12-3 Implied SPX TRMS
Implied Volatility Skew
Realized SPX Volatility
Realized Correlation
SPX Autocorrelation
Skewness
Kurtosis
P/C Open Interest Ratio
P/C Volume Ratio
P-C Gamma Imbalance
Implied SPX
Implied SPX
Implied SPX
Implied SPX
Realized risk
Realized risk
Realized risk
Realized risk
Realized risk
Option
Option
Option
1986
1989
1989
1996
1986
1986
1986
1986
1986
1995
1995
1996
SPX Return
Market RSI 3d
Market RSI 14d
Market RSI 30d
MVOLNE Index
ADLN
SPXAD Index
RVX
VXN
V2X
VNKY
VHSI
technical
technical
technical
technical
technical
technical
technical
Other Markets
Other Markets
Other Markets
Other Markets
Other Markets
1986
1986
1986
1986
1988
1990
1997
1996
1996
2000
2000
2000
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
35
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
4.8
-23.3
-0.7
-6.4
9.2
27.7
28.4
-2.8
2.5
0.1
2.1
7.4
9.9
21.7
4.5
-2.6
-12.4
-8.8
8.2
4.7
2.3
9.5
4.8
11.1
8.2
VHSI
-1.8
-24.9
2.6
-2.1
3.8
27.7
29.5
-0.7
3.4
-1.2
3.3
8.0
10.4
21.0
5.1
-4.0
-7.7
-6.6
3.5
VNKY
RVX
P/C Volume 5d avg
P/C OI 5d avg
P-C Gamma
-0.8 -4.1 5.3
1.6 -2.5
-4.3 -11.8 -15.0 -11.7 -20.7
1.6
0.3 -6.9 -0.8 6.3
-1.2 0.0 -2.2 -1.6 -1.3
1.0
2.2
8.1
9.5
5.7
3.0 11.5 20.0 15.3 23.7
-1.0 0.6 -2.4 -3.0 27.9
5.4 -4.2 1.2
1.6 -5.0
0.7
0.9
0.4 -1.3 4.0
-2.7 1.3 10.2 3.8 -2.6
-0.2 0.0 -1.5 -1.2 4.2
-3.8 4.5 -0.6 -1.0 6.3
-3.3 1.9 -7.3 -2.8 9.3
6.9 20.4 13.7 14.9 22.8
3.2 -1.7 -2.2 4.9
4.6
5.0
2.8 -5.1
4.8 -2.4
-1.2 -7.9
4.1 -0.4 -4.3
-7.7
1.2 -3.7 4.5
1.7
-1.0 -3.4 4.4
0.1
4.6
1.5 -2.3 8.5
2.8
1.2
3.7 -3.3 9.5
5.1
8.6
3.4 -2.0 9.2
2.0
5.0
V2X
0.1
2.0 -0.9 1.8
1.2 -2.3 -0.1
-1.1 7.6 -14.9 9.9 10.2 14.9 -9.2
-3.3 -0.7 -3.8 -0.2 -1.8 1.2
4.2
-1.3 -1.4 -1.1 -0.3 -1.6 -0.6 0.7
6.6 -2.5 2.8 -2.6 -5.4 -4.3 -4.1
-1.3 -11.2 14.0 -14.8 -14.1 -18.9 -3.6
-2.3 -0.8 9.4 -1.6 -1.8 -5.1 8.7
1.2 -1.0 1.1
0.5
2.1 -3.8
-0.9
1.7 -0.8 0.6 -1.5 3.4
-1.5 -1.6
-2.8 -3.3 -2.1 -3.1
0.1
0.7
2.0
1.0 -0.6 4.4
2.2
0.3
3.5
0.5
-8.5 7.0
1.3
2.5
2.6
3.1
6.9
5.4
-3.4 -10.1 9.5 -12.5 -17.7 -22.9
-7.6 -1.4 3.2
0.1 -1.4 -4.9 -10.7
2.4 -1.3 -1.5 -1.5 -2.5 -0.5 -8.9
1.7
0.2 -4.5 1.6
1.4
5.2 -4.5
4.6
0.5 -2.6 1.2
2.0
6.1 -5.7
3.9
1.9
1.8
1.7
3.8
3.8
1.2
3.9
2.3 -0.4 2.4
3.5
3.9
1.7
2.7 -0.2 -0.8 -0.6 -1.4 -2.1 -0.9
4.9 -4.8 1.8 -4.6 -3.6 -2.5 -0.4
0.5 -3.7 0.1 -5.2 -2.2 -1.7 0.4
30D AutoCorr
Corr 30
SPX RSI14
SPX RSI3
ADLN
MVOLNE
SPXAD
30D Kurt
30D Skew
30D Vol
3M 95-105
TRMS 12-3
TRMS 3-1
-5.2 -3.7 -0.3 1.4
5.2 -3.2
-14.8
34.2 23.4 -3.6 -14.8 -1.9
5.2 -3.4
-3.5 -0.6 5.9
6.3
-6.7 2.1 12.5
-2.6 -4.7 10.4
8.0
3.0 -3.4 -6.4
2.7 -7.4
22.3 22.3 -38.2 -29.6 6.3
-1.2
22.8 2.1 -25.4 -31.4 4.3 13.5
-4.9 3.3
4.5
5.7 -5.5 -6.3 3.1
4.5 -2.8 -4.0 -4.4 0.9
5.3
2.2
0.3
4.9
1.3 -0.9 1.4 -3.9 -4.5
5.0 -4.1 -3.8 -5.4 -0.1 6.8
4.3
5.5 -2.7 -4.9 -8.4 3.1
5.8
1.7
3.6 -2.4 -3.2 -6.8 2.3
4.1
7.5
22.3 12.0 -25.3 -22.1 10.7 9.2 -8.9
1.0
3.7
0.2 -0.4 3.8 -2.2 -0.5
-4.5 2.7
2.6
3.8 -3.4 -5.2 -3.0
-8.0 4.9
3.8
4.4 -2.0 -9.6 -0.2
-9.0 2.6
3.8
7.0 -5.9 -8.4 1.5
8.6 -7.2 -7.3 -3.6 3.5 10.5 -3.6
4.9 -5.8 -7.3 -5.7 2.9
7.5 -7.3
4.7
1.2 -6.2 -3.0 2.9
0.7 -11.8
12.6 -0.6 -14.2 -13.0 4.4
7.9 -14.1
7.7
0.3 -10.4 -9.3 1.1
4.3 -11.9
VXN
VIX (prior VXO)
VIX-Realized
TRMS 3-1
TRMS 12-3
3M 95-105
30D Vol
30D Skew
30D Kurt
SPXAD
MVOLNE
ADLN
SPX RSI3
SPX RSI14
Corr 30
30D AutoCorr
P-C Gamma
P/C OI 5d avg
P/C Volume 5d avg
RVX
VXN
V2X
VNKY
VHSI
VIX-Relized
VIX (prior VXO)
Figure 54: Causality matrix at 1M lag for various Equity signals
2.2
-17.3
1.3
-4.5
7.4
17.2
17.5
-3.4
4.8
-1.1
4.2
6.3
6.6
17.8
1.0
-5.3
-9.6
-9.8
2.2
1.7
-0.9
8.5
-17.0
-1.1
-7.9
12.4
20.8
20.3
-2.1
3.7
0.0
3.6
5.4
6.3
16.4
2.7
-4.2
-5.9
-5.4
11.4
10.0
4.7
14.3
-0.4
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Several signals that historically showed a significant in-sample relationship with the VIX and the performance of these
signals in predicting the VIX are shown in Figure 55. These signals include: Advance/Decline index (an increase led to
lower VIX), Market Volume (increase led to lower VIX), Correlation (increase led to lower VIX), P-C Gamma (increase led
to lower VIX), 30d RSI (increase led to lower VIX), RVX (increase led to lower VIX), P/C OI (increase led to higher VIX),
V2X and VHSI (increase led to higher VIX). Hit rates of these signals were not high (ranged from 52% to 56%) and
individual signals implemented in a hypothetical strategy for trading the spot VIX resulted in information ratios from 0 to
0.4. Most of these signals also had positive out of sample performance (i.e., over different sub-samples).
One challenge with a number of the Equity signals listed above is that they have a reversion bias, such as selling the VIX
post increase in levels of market correlations, market volumes, or strong RSI. These signals may be closely related to the
reversion properties of the VIX and S&P 500 returns discussed in the first section, and will share similar implementation
challenges. Also these signals are likely exposed to the same risk of selling volatility into a large tail event. Exceptions to
this are the V2X and VHSI signals that performed poorly over the full sample, but were able to reduce the risk of various
reversion signals. We have highlighted the use of cross-regional volatility as a signal to minimize the risk of short volatility
strategies in our paper: Risk Premia in Volatility Markets). Figure 56 shows in-sample average performance of long VIX
signals and short VIX signals. One can clearly see the importance of maintaining a balance between reversion and trending
signals that is needed to eliminate the tail risk of reversion signals (that keep on selling volatility in rising environment).
36
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 55: Performance of several equity signals that showed full
sample significance
150
Figure 56: Average performance of all long volatility and short
volatility signals
40
30
100
Long Signals
Short Signals
20
50
10
0
-50
1989
1992
1995
1998
2001
2004
2007
ADLN
SPX RSI30
P-C Gamma
RVX
V2X
AVG SMAL MODEL
-100
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
2010
2013
0
1989
1992
1995
1998
2001
2004
2007
2010
2013
-10
-20
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
An important note is that the above Equity signals were tested over the past 30 years focusing on the ability to forecast VIX
levels. This does not take into account important considerations of trading VIX futures, such as the responsiveness of the
term structure, and cost of carry for long or short VIX positions. Given the additional cost that is accompanied with trading
VIX futures, we expect that a number of equity signals discussed will not lead to viable trading strategies.
37
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Equity Factor Allocation based on VIX
Lastly, we investigated whether the VIX can be used to dynamically allocate risk between various equity factors. In the
past, most equity quant investors used the approach of maintaining a balanced allocation to proven equity risk factors.
Maintaining a portfolio of factors that deliver positive premia and have relatively low and stable correlations can often yield
information ratios superior to that of holding a broad equity exposure. However, over the past few years quant investors
have shown more interest in models that change factor exposure based on various timing signals.
In the analysis below we have investigated the potential use of the VIX in these factor allocations. First, we calculated
causality relationship between the VIX and performance of 10 common equity long-short factors calculated by our team.
These are: Return on Equity (ROE), Small Size factor, 12M Forward Earnings Yield, Extended price momentum, 12M
Price Momentum, Dividend Yield, 3M (low) Volatility, FCF to IC, Accruals Factors, FCF to IC Growth, and Free Cash
flow yield. For definitions of these factors, see our US Factor Reference handbook. We focused on the ability of the VIX
to forecast factor performance over the time horizon from 1 week to 1 month. Figure 57 shows the t-test for the full sample
(1995-2014) causal relationship. While there are many significant relationships over the full sample, only a few of them are
persistent over various sub-intervals (out of sample). Figure 58 shows these factors.
It is also reassuring to see that out-of-sample significant relationships have intuitive explanations. Quality stocks (ROE) and
Low Volatility stocks usually benefit from an increase in the VIX (and are hurt by a declining VIX). This is caused by
investors’ flight to quality after an initial increase of market volatility. On the other hand, Small Capitalization stocks and
“deeper” Value stocks (12M Forward Earnings Yield) tend to suffer even after an initial volatility increase (and benefit after
volatility declines). The Dividend factor overall was positively impacted by rising volatility; however, this relationship has
not been stable. Over the past 20 years, high dividend stocks have changed their character from value (e.g., dogs) to more
quality/defensive names (e.g., utilities and staples). This has resulted in the shift of how these stocks respond to volatility
shocks. Momentum stocks have on average been positively impacted by higher volatility (especially over the short time
horizon of ~1 week) as investors tend to stay with the stocks that “worked” (over longer time horizons the relationship
between momentum and volatility is weaker).
Figure 57: VIX and Equity factor full sample causality matrix (1M lag)
30D Lag
VIX
IRVOL
ROE
Si ze
12MFwdEY
12M Pmom
Di v Yld
3M Low Vol
FCF to IC
Accrual s
FCF to IC Gr.
FCF Yld
VIX
IRVOL
ROE
Size
1.9
9.1
13.3
-15.0
-6.2
3.7
8.5
10.5
12.5
0.9
1.8
9.9
-7.8
-6.4
11.2
-6.1
-1.7
4.4
3.7
10.3
9.6
5.6
-5.9
8.9
5.5
-0.3
1.0
4.0
-3.5
-10.8
6.4
-9.5
1.8
4.6
9.5
0.7
4.6
9.2
-1.5
9.3
0.3
-0.2
-5.1
-4.5
12MFwdEY12M Pmom Div Yld 3M Low VolFCF to IC AccrualsFCF to IC Gr. FCF Yld
0.2
6.3
3.9
8.2
4.8
2.0
9.5
4.5
-9.0
-8.9
-3.8
5.7
-2.1
1.9
-3.2
-7.6
6.7
-2.0
3.9
5.3
12.9
3.5
-1.3
9.2
5.2
-1.4
5.6
-1.0
6.2
3.7
-2.1
-7.4
5.3
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
-3.1
-0.6
5.8
3.1
-3.0
-2.0
10.1
7.9
4.4
7.5
5.3
-5.4
-9.7
-1.6
5.3
-3.7
-4.5
-2.3
-7.0
4.6
9.5
0.9
7.8
7.9
6.2
1.5
7.1
7.0
-0.5
9.2
12.0
1.6
13.6
2.3
5.1
4.1
0.6
7.0
1.9
2.7
5.5
1.3
4.0
1.1
-1.6
-7.8
7.2
2.5
-2.5
4.0
-7.0
4.2
9.1
-2.5
13.4
Figure 58: Significant relationships between the VIX and factors at
1W and 1M lags
Factor
7D Lag
30D Lag
'94-'14 '94-'04 '04-'14 '94-'14 '94-'04 '04-'14
ROE
4.8
3.4
3.0
13.3
8.8
9.6
Small Size
-5.1
-1.9
-8.8 -15.0 -3.8 -16.7
12mFwdEY
-4.6
-0.8
-2.7
-6.2
-4.7
-8.9
12M PMOM
7.8
2.1
2.0
3.7
-3.4
6.7
Dividend Yld -6.2
-3.6
4.0
8.5
-4.0 12.1
3M Low Vol.
2.5
1.3
3.5
10.5
1.6
11.7
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
Figure 59 and Figure 60 compare the performance of VIX-based factor timing models and the performance of a long only
basket of factors. For the 1-week trading model we have used 6 factors with significant out of sample relationships with the
VIX, and for the 1-month trading model we have used 4 factors with significant out of sample relationships (from Figure
57). One can see that these simple versions of VIX-based factor timing strategies have outperformed their respective long
only factor baskets.
38
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Figure 59: Performance of weekly factor rotation strategy based on
the VIX signal, and performance of an equal weighted factor basket
(no signal)
350
300
Figure 60: Performance of monthly factor rotation strategy based on
the VIX signal, and performance of an equal weighted factor basket
(no signal)
200
VIX Factor Timing (1 Week)
VIX Factor Timing (1 Month)
Factor Basket
Factor Basket
250
150
200
150
100
100
50
1995
1997
1999
2002
2004
2007
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
2009
2011
2014
50
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
Source: J.P. Morgan Equity Derivatives Strategy, Bloomberg.
While in our study we have not considered realistic quant trading models that would incorporate factor exposure, market
impact and turnover constraints, we believe that using volatility as a signal to allocate between factors can be a useful
approach for real quant portfolios.
39
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Risks of Common Option Strategies
Risks to Strategies: Not all option strategies are suitable for investors; certain strategies may expose investors to significant
potential losses. We have summarized the risks of selected derivative strategies. For additional risk information, please call
your sales representative for a copy of “Characteristics and Risks of Standardized Options.” We advise investors to consult
their tax advisors and legal counsel about the tax implications of these strategies. Please also refer to option risk disclosure
documents.
Put Sale. Investors who sell put options will own the underlying asset if the asset’s price falls below the strike price of the
put option. Investors, therefore, will be exposed to any decline in the underlying asset’s price below the strike potentially to
zero, and they will not participate in any price appreciation in the underlying asset if the option expires unexercised.
Call Sale. Investors who sell uncovered call options have exposure on the upside that is theoretically unlimited.
Call Overwrite or Buywrite. Investors who sell call options against a long position in the underlying asset give up any
appreciation in the underlying asset’s price above the strike price of the call option, and they remain exposed to the
downside of the underlying asset in the return for the receipt of the option premium.
Booster. In a sell-off, the maximum realized downside potential of a double-up booster is the net premium paid. In a rally,
option losses are potentially unlimited as the investor is net short a call. When overlaid onto a long position in the
underlying asset, upside losses are capped (as for a covered call), but downside losses are not.
Collar. Locks in the amount that can be realized at maturity to a range defined by the put and call strike. If the collar is not
costless, investors risk losing 100% of the premium paid. Since investors are selling a call option, they give up any price
appreciation in the underlying asset above the strike price of the call option.
Call Purchase. Options are a decaying asset, and investors risk losing 100% of the premium paid if the underlying asset’s
price is below the strike price of the call option.
Put Purchase. Options are a decaying asset, and investors risk losing 100% of the premium paid if the underlying asset’s
price is above the strike price of the put option.
Straddle or Strangle. The seller of a straddle or strangle is exposed to increases in the underlying asset’s price above the
call strike and declines in the underlying asset’s price below the put strike. Since exposure on the upside is theoretically
unlimited, investors who also own the underlying asset would have limited losses should the underlying asset rally. Covered
writers are exposed to declines in the underlying asset position as well as any additional exposure should the underlying
asset decline below the strike price of the put option. Having sold a covered call option, the investor gives up all
appreciation in the underlying asset above the strike price of the call option.
Put Spread. The buyer of a put spread risks losing 100% of the premium paid. The buyer of higher-ratio put spread has
unlimited downside below the lower strike (down to zero), dependent on the number of lower-struck puts sold. The
maximum gain is limited to the spread between the two put strikes, when the underlying is at the lower strike. Investors who
own the underlying asset will have downside protection between the higher-strike put and the lower-strike put. However,
should the underlying asset’s price fall below the strike price of the lower-strike put, investors regain exposure to the
underlying asset, and this exposure is multiplied by the number of puts sold.
Call Spread. The buyer risks losing 100% of the premium paid. The gain is limited to the spread between the two strike
prices. The seller of a call spread risks losing an amount equal to the spread between the two call strikes less the net
premium received. By selling a covered call spread, the investor remains exposed to the downside of the underlying asset
and gives up the spread between the two call strikes should the underlying asset rally.
Butterfly Spread. A butterfly spread consists of two spreads established simultaneously – one a bull spread and the other a
bear spread. The resulting position is neutral, that is, the investor will profit if the underlying is stable. Butterfly spreads are
established at a net debit. The maximum profit will occur at the middle strike price; the maximum loss is the net debit.
Pricing Is Illustrative Only: Prices quoted in the above trade ideas are our estimate of current market levels, and are not
indicative trading levels
40
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Disclosures
This report is a product of the research department's Global Equity Derivatives and Quantitative Strategy group. Views expressed may
differ from the views of the research analysts covering stocks or sectors mentioned in this report. Structured securities, options, futures
and other derivatives are complex instruments, may involve a high degree of risk, and may be appropriate investments only for
sophisticated investors who are capable of understanding and assuming the risks involved. Because of the importance of tax
considerations to many option transactions, the investor considering options should consult with his/her tax advisor as to how taxes affect
the outcome of contemplated option transactions.
Analyst Certification: The research analyst(s) denoted by an “AC” on the cover of this report certifies (or, where multiple research
analysts are primarily responsible for this report, the research analyst denoted by an “AC” on the cover or within the document
individually certifies, with respect to each security or issuer that the research analyst covers in this research) that: (1) all of the views
expressed in this report accurately reflect his or her personal views about any and all of the subject securities or issuers; and (2) no part of
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expressed by the research analyst(s) in this report. For all Korea-based research analysts listed on the front cover, they also certify, as per
KOFIA requirements, that their analysis was made in good faith and that the views reflect their own opinion, without undue influence or
intervention.
Important Disclosures

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months, we expect this stock will perform in line with the average total return of the stocks in the analyst’s (or the analyst’s team’s)
coverage universe.] Underweight [Over the next six to twelve months, we expect this stock will underperform the average total return of
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J.P. Morgan Equity Research Ratings Distribution, as of March 31, 2014
J.P. Morgan Global Equity Research Coverage
IB clients*
JPMS Equity Research Coverage
IB clients*
Overweight
(buy)
44%
58%
45%
78%
Neutral
(hold)
44%
49%
48%
67%
Underweight
(sell)
11%
40%
7%
60%
*Percentage of investment banking clients in each rating category.
For purposes only of FINRA/NYSE ratings distribution rules, our Overweight rating falls into a buy rating category; our Neutral rating falls into a hold
rating category; and our Underweight rating falls into a sell rating category. Please note that stocks with an NR designation are not included in the table
above.
41
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
Equity Valuation and Risks: For valuation methodology and risks associated with covered companies or price targets for covered
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or your J.P. Morgan representative, or email research.disclosure.inquiries@jpmorgan.com.
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Conflict of Interest
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42
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.
Marko Kolanovic
(1-212) 272-1438
marko.kolanovic@jpmorgan.com
Global Equity Derivatives & Quantitative Strategy
16 May 2014
Bram Kaplan, CFA
(1-212) 272-1215
bram.kaplan@jpmorgan.com
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"Other Disclosures" last revised April 5, 2014.
Copyright 2014 JPMorgan Chase & Co. All rights reserved. This report or any portion hereof may not be reprinted, sold or
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43
This document is being provided for the exclusive use of Aaron Medley at JPM PRIVATE BANKING - UNITED STATES.