September 16, 2013
Fixed Income Asset Management
Presentation at Yale University
Ramu Thiagarajan
Global Head - Fixed Income Quantitative Research
Agenda
 Issues in Fixed Income Asset Management
 Size of the Market and Performance of the market
 Risks in Fixed Income Assets
 How do you make money in Fixed Income
 When does quant fail?
 Security Selection Issues in FI
 Use of FI in Other Strategies
 Asset Allocation Issues (depending on time)
 Q&A
AllianceBernstein.com
Fixed Income Asset Management
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Fixed Income Asset Management – Size of the Market
Fixed Income Market vs. the Equity *
 Market Cap: Equity Market
53 Trillion
 Market Cap: Bond market ?
92 Trillion
What are the different type of FI securities FIX
BarCap US Agg
32%
22%
Treasury
36%
Government-Related (Agency, Local Authority…)
Corporate
(Financials, Utility, Industrial...)
Securitized
(MBS, ABS, CMBS...)
10%
* Source: Bond—BIS consolidated “Debt security statistics”, Equity—WorldBank “Market Capitalization”, as of 2012
AllianceBernstein.com
Fixed Income Asset Management
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AllianceBernstein.com
Fixed Income Asset Management
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Sep-13
May-13
Jan-13
Sep-12
May-12
Jan-12
Sep-11
May-11
Jan-11
Sep-10
May-10
Jan-10
Sep-09
May-09
Jan-09
Sep-08
May-08
Jan-08
Sep-07
May-07
Jan-07
Sep-06
May-06
Jan-06
Sep-05
IR=11.5%
May-05
BarCap Glb Agg
Jan-05
IR=4.1%
Sep-04
S&P 500
May-04
230
Jan-04
Sep-03
May-03
Jan-03
Sep-02
May-02
Jan-02
Sep-01
May-01
Jan-01
190
Sep-00
210
May-00
Jan-00
Sep-99
May-99
Jan-99
Performance of Equity vs. Bonds
Bond and Equity Index (Jan-99 =100)
170
150
130
110
90
70
50
Fixed Income Asset Management
What drives FI returns
 Role of Systematic Risk vs. Idiosyncratic Risk
How do you make money in FI portfolios
 Tools for Positioning on Systematic Risk
 Tools for Positioning for Alpha
Alpha in Rates
Alpha in Credit
AllianceBernstein.com
Fixed Income Asset Management
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Fixed Income Asset Management
Has its own Lingo
CDS
Swaptions
Spread Duration,
Bull Duration,
Bear Duration
Confusing array of indices
 BarCap US Aggregate / US Aggregate Intermediate
 BarCap US High Yield / US High Yield 2% Issuer Cap
 J.P. Morgan EMBI+ / EMBIG / EMBIG Diversified / GBI-EM / GBI-EM Global…
Complex set of derivatives
 Interest rate swap, futures, FRA; Swaptions, Caps & Floors
 CDS, CDX, TRS; CLN, CDO
AllianceBernstein.com
Fixed Income Asset Management
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Many moving parts in a FI security
Yield curve - Duration, Slope and Convexity
Spread Risks
 Mortgages
 Corporates
 Municipals
 TIPS
Currency rate movements
Volatility and Prepayment Risk – specific to MBS
Liquidity Risk
AllianceBernstein.com
Fixed Income Asset Management
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What drives risk in fixed income securities
Systematic Risk
 Level, Slope & Curvature
 Volatility
Idiosyncratic Risk
 Security Specific Risk
Argentinian Bonds
AllianceBernstein.com
Fixed Income Asset Management
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Beta Explains 90-95% of the Volatility of a Diversified Portfolio
US Investment Grade Corporate Index
40
35
Brokerage
Reits
Excess Return (%)
April-Sept 2009
30
Finance Companies
25
Insurance
20
Banking
Basic Industry
15
Consumer Cyclical
Capital Goods
10
Consumer Non-Cyclical
5
0
0.0
0.5
1.0
1.5
Beta
March 31, 2009
2.0
2.5
Historical data to be used for illustrative purposes only.
As of: September 30, 2009
Source: Barclays Capital and AllianceBernstein
AllianceBernstein.com
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Systematic Risk vs Idiosycratic Risk
 Systematic Risk in Yield Curve (Litterman and Scheinkman [1991])
 3-factor model(Level, steepness and curvature) explains more than 95% or Variance of excess returns
AllianceBernstein.com
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How do you make money when systematic Risk contributes to such a
high proportion of variance explained
 You need to get a feel for the risk regime you are in.
 Can you forecast risk regimes?
 Many different methods
 Regime switching models
 Hidden Markov models
 A simpler way of looking at Risk Regime Indicators
 Supply of Liquidity
 Demand for Liquidity
AllianceBernstein.com
Fixed Income Asset Management
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The Slope of the Yield Curve as a Leading Indicator of Future Real
Economic Activity
4
3
Slope
2
1
0
(1)
(2)
(3)
(4)
76
78
81
83
85
87
90
92
94
97
99
01
04
06
08
10
Year
NBER Recession
Slope
As of September 2011
Source: St. Louis Fed Economic Data
AllianceBernstein.com
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Unemployment Rate Goes Up When The Curve Is Inverted…
Historical data to be used for illustrative purposes only.
As of January 2010
Source: Adrian, Estella and Shin (2010), New York Fed
AllianceBernstein.com
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…However Slope Is Not a Good Predictor of Future Market Movement
2-10 Slope
SPX
Avg
>Avg
<Avg
8.68%
1.70%
14.48%
Avg
>Avg
<Avg
14.85%
16.16%
13.62%
Information Ratio
Avg
>Avg
<Avg
0.58
0.11
1.06
Batting Average
Avg
>Avg
<Avg
63.39%
59.82%
65.73%
Max Drawdown
>Avg
<Avg
-16.79%
-14.46%
Return
0.40
0.35
0.30
Vol
Density
0.25
0.20
0.15
0.10
0.05
0.00
(14)
(11)
(8)
(5)
(2)
1
4
7
10
13
SP500 Return
Flat/Inverted Yield Curve Environment
Steep Yield Curve Environment
Historical data to be used for illustrative purposes only.
As of February 2010
Source: AllianceBernstein
AllianceBernstein.com
Fixed Income Asset Management
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Capital Markets Lag the Slope of the Yield Curve
1800
3.0
1600
2.5
1400
Get out of the market too early
2.0
1.5
1000
1.0
Slope
Index
1200
800
0.5
Enter the market too early
600
0.0
400
200
(0.5)
0
(1.0)
91
93
95
97
99
01
03
05
07
09
11
Year
S&P 500 Close
Slope
As of August 2011, Global Credit Opportunities Portfolio (Michael Weisman:NY)
Source: AllianceBernstein
AllianceBernstein.com
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VIX Alone Cannot Be Used As Market Regime Indicator
 The percentage of positive 6-month-forward returns does not decline monotonically as VIX
increases across the quintiles
Percent Positive Return
 Q1 – lowest VIX quintile, Q5 – highest VIX quintile
VIX Quintiles
As of August 31, 2011
Source: Bloomberg and AllianceBernstein
AllianceBernstein.com
Fixed Income Asset Management
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Market Cycle Indicator: Identify Current Regime and Regime Trend
Our Market Cycle Indicator (MCI) is a proprietary tool we use to identify the macro phase
Phase I
Phase II
Phase III
Phase IV
5.5%
5.2%
12/31/2003
2/28/2010
2.3%
2.2%
1.5%
0.9%
(0.1)%
Q2 2011
(3.8)%
(4.7)%
(6.6)%
(6.7)%
(8.9)%
3/31/2007
US IG Corp
19.5%
US HY Corp
13.9%
S&P
EMD
5.1%
Currency
1.4%
CMBS
4.0%
3.9% 3.3%
0.1%0.5%
2.5% 2.6%
1.1%
10/31/2000
10/31/2008
Historical data to be used for illustrative purposes only.
As of February 1, 2010
Green dots are representations of points in time. Returns are annualized monthly excess returns to Treasuries. Currency returns are top five minus bottom five G10 currencies as measured by
interest-rate differentials. US HY Corp.: August 31, 1998–February 28, 2010; US IG Corp: August 31, 1998–February 28, 2010; EMD: January 31, 1993– February 28, 2010; Currency: June 30,
1979–February 28, 2010. S&P: June 30, 1979-February 28, 2010. CMBS: June 30, 1999-February 28, 2010.
Source: Barclays Capital, Bloomberg and AllianceBernstein
AllianceBernstein.com
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Macro Insights: Market Cycle Indicator Identifies Signals Amongst Noise
Phase I
 MCI is above average
and increasing
Phase II
 MCI is above average
and decreasing
Phase III
 MCI is below average
and decreasing
Phase IV
 MCI is below average
and increasing
Typical Conditions of Market Factors
 Macro Economy
 Macro Economy
 Macro Economy
 Macro Economy
 Monetary Policy
 Monetary Policy
 Monetary Policy
 Monetary Policy
 Liquidity
 Liquidity
 Liquidity
 Liquidity
 Volatility
 Volatility
 Volatility
 Volatility
Typical Market Performance
Risk Seeking
Income Bias
Risk Averse
Measured Risk
 Asset Return
 Asset Return
 Asset Return
 Asset Return
 Correlation
 Correlation
 Correlation
 Correlation
As of February 2010
Source: AllianceBernstein
AllianceBernstein.com
Fixed Income Asset Management
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Market Cycle Indicator
Market Cycle Indicator Score
6
4
2
0
(2)
(4)
(6)
50
56
62
68
74
80
86
92
98
04
10
Year
MCI Phase
MCI
As of August 31, 2011
Source: AllianceBernstein
AllianceBernstein.com
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Market Cycle Indicator Continues to Diverge by Region
AllianceBernstein MCI
As of Jul 24, 2013
Source: AllianceBernstein
AllianceBernstein.com
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Components of the new Euro Crisis Indicator
Market-based Variables:
1.
CDS on USD senior 5 year (solvency)
2.
Fundamental Variables :
1.
Unemployment Rate
EURIBOR-OIS (liquidity)
2.
PMI
3.
2 year yield in local currency and
3.
YoY Private sector credit growth
4.
Corporate Credit OAS (risk premium)
4.
YoY Government debt growth.
Choice of Variables: We picked reasonable proxies ex-ante for the following underlying
constructs – Solvency, Liquidity and risk premia.
 We use the spread between PIIGS country and Germany for the variables.
 We use the sum of the equally weighted z-scores (4 market and 4 fundamental) as
our Euro crisis indicator.
AllianceBernstein.com
Fixed Income Asset Management
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Euro-Crisis Indicator
Market Indicator
Fundamental Indicator
20
8
15
6
4
10
2
5
0
0
-2
-5
-4
-10
-6
AllianceBernstein.com
Fixed Income Asset Management
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A side note on the importance of risk regime indicators for Equity
selection strategies
 Equity stock selection model
 Valuation
 Quality
 Momentum
 Analyst behavior
 Size
 How do those behave in different risk regimes?
AllianceBernstein.com
Fixed Income Asset Management
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Equity Factor Returns Under Different Regimes
 Analyst behavior
 Earning Momentum
 Expected Growth
 Price Reversal
 Relative Value
Percent
 Valuation
 Quality
 Profit Trend
 Momentum
 Price Momentum
 Size
As of August 31, 2011
Source: Credit Suisse and AllianceBernstein
AllianceBernstein.com
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 ALPHA MODELS IN FIXED INCOME
AllianceBernstein.com
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Building Factor Models for Fixed Income
 Deterministic Components of the yield curve
4
 Carry
 Roll down
Term Structure (Forward Rate)
3.5
3
C
2.5
Yield
B
2
In steep curve environment
Point A: invest in short-end and earn 0.2% yield
1.5
Or, buy a bond at Point C and hold for half year,
suppose Term Structure stays same, bond will
move to point B and earn some yield between
Point B & Point C, which is higher than Point A.
1
0.5
A
0
0
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0
Maturity
 Stochastic Components of the yield Curve
 Changes in the yield curve
AllianceBernstein.com
Fixed Income Asset Management
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Global Country/Yield Curve Model: Multiple Integrated Elements
Term Structure
Movement
+
Carry and Roll Down
=
Bond Return
Mean Reversion
Yields tend to mean revert—very low rates stimulate growth and inflation
and lead to tighter monetary policy and higher rates in the future; very high
rates stifle growth leading to accommodative monetary policy and lower
rates in the future
Macro Factors
Higher levels of economic activity, captured by data such as industrial
production and labor markets, are generally associated with higher inflation
and higher rates. Low levels of economic activity are associated with lower
rates and lower inflation
Momentum
The movement in yields tend to be persistent over a period of time as
changes in economic cycles take time, thus momentum is effective in
determining the path of future rates in the short—medium term
AllianceBernstein.com
Fixed Income Asset Management
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Global Country/Yield Curve Model: Outputs
January 2011 Expected Returns:
Point to an Underweight in Canada
January 2012 Expected Returns:
Point to an Overweight in Canada
5
Composition of Forecast Change in
Global “Level” by Factor*
0.15
4.5
EUR
0.10
4
3.5
0.5
USD
3
EUR
1
Percent
Percent
CAD
2
2.5
CAD
JPY
1.5
JPY
(0.05)
(0.10)
(0.15)
Globe
0.5
0
Percent
0
Globe
(0.20)
USD
(1)
(0.25)
Jan Jun Nov Apr Sep Feb Jul
10 10 10 11 11 12 12
R
(0.5)
0.25
2
4
6
8
10
Maturity (Years)
0.25
2
4
6
Maturity (Years)
8
10
F Momentum
Mean Reversion
Economic
Total
Expected returns are over 6 months, annualized, hedged to USD.
*Percentage change over six months
Source: AllianceBernstein
AllianceBernstein.com
Fixed Income Asset Management
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Global Country/Yield Curve Model: Outputs
Maturity
Developed Markets
North America
Yield
Roll
Convexity
Term
Local
Expected
Returns
Hedging
Cost
Hedged
Expected
Return
2
5
10
0.82%
1.79%
2.73%
0.32%
1.11%
0.95%
0.00%
0.11%
0.37%
-0.84%
-1.48%
-1.66%
0.30%
1.53%
2.40%
-0.46%
-0.46%
-0.46%
-0.15%
1.07%
1.94%
Europe
2
5
10
0.67%
1.36%
2.19%
0.16%
0.78%
0.74%
0.09%
0.11%
0.28%
-0.65%
-1.44%
-2.18%
0.27%
0.80%
1.02%
-0.27%
-0.27%
-0.27%
0.01%
0.54%
0.75%
Asia
2
5
10
1.95%
2.53%
3.13%
0.22%
0.58%
0.46%
0.01%
0.08%
0.34%
-0.11%
-0.42%
-1.39%
2.06%
2.76%
2.54%
-1.55%
-1.55%
-1.55%
0.51%
1.21%
0.99%
2
5
10
6.02%
6.89%
7.36%
0.51%
0.75%
0.13%
0.04%
0.13%
0.29%
-0.47%
-0.91%
-1.86%
6.09%
6.86%
5.92%
-5.93%
-5.93%
-5.93%
0.17%
0.93%
-0.01%
Eastern Europe
2
5
10
4.21%
4.93%
5.71%
0.28%
0.77%
0.51%
0.01%
0.04%
0.10%
-0.54%
-1.16%
-1.89%
3.96%
4.57%
4.42%
-3.24%
-3.24%
-3.24%
0.72%
1.34%
1.18%
Emerging Asia
2
5
10
3.50%
4.01%
4.55%
0.32%
0.55%
0.54%
0.02%
0.06%
0.23%
-0.29%
-0.67%
-1.47%
3.55%
3.95%
3.86%
-6.44%
-6.44%
-6.44%
-2.89%
-2.50%
-2.59%
Emerging Markets
Latin America
AllianceBernstein.com
Fixed Income Asset Management
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Global Credit Model: Multiple Integrated Elements
Spread Movement
+
Carry
=
Excess Return
Mean Reversion (Spreads)
Issuers with comparatively wide spreads are attractive
Asset Return Momentum
Strong equity markets are a positive indicator for corporate bonds
Peer Group Momentum
Correlation and persistence exist in return patterns among a peer group*
Corporate Metrics
Leverage, liquidity, profitability
*Peer group refers to issuers within the same duration and spread range
AllianceBernstein.com
Fixed Income Asset Management
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Global Credit Model: Outputs
Expected Excess Returns Suggest Positioning Changes
2.0
1.5
1.0
0.5
0.0
(0.5)
9/30/2011
Utilities
Telecom
Materials
Technology
Industrials
Healthcare
Financials
Energy
Consumer Staples
Consumer Discretionary
(1.0)
3/31/2012
Three-month forward expected excess returns for intermediate maturities, derived from AllianceBernstein quantitative forecasts
Source: AllianceBernstein
AllianceBernstein.com
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Risks in Corporate Bonds
Corporate Bonds carry a lot of Idiosyncratic Risk
If Spreads widen on a bond, it will take 20 winners to over that loss
Example: Enron Bonds
AllianceBernstein.com
Fixed Income Asset Management
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Fundamental Credit Model Framework
Drivers of excess return
Valuation
Risk-Return
Trade-off
AllianceBernstein.com
Fundamentals
Balance
Sheet
Income
Statement
Market Metrics
Cashflow
Statement
Equity
Return
Consensus
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Model Framework
• OAS per leverage
Valuation
• Cash flow accruals
• Non-current asset accruals
• Management discretion
Fundamental
Market
• Equity momentum
• Analyst revision
AllianceBernstein.com
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Correlation of Factors
AllianceBernstein.com
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Factor Performance
Source: AB Research, Barclays, Compustat, FactSet
AllianceBernstein.com
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Our Fundamental Alpha Score is a strong predictor of forward returns that
has worked well over time.
Source: AB Research, Barclays, Compustat, FactSet
AllianceBernstein.com
Fixed Income Asset Management
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Batting Average
70.0%
60.0%
59.7%
57.6%
56.7%
50.0%
45.8%
45.0%
44.9%
40.0%
30.0%
20.0%
10.0%
0.0%
3 Tricile
5 Quintile
Top
AllianceBernstein.com
10 Decile
Bottom
Fixed Income Asset Management
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Staying in Low Composite Group is More Likely to Suffer Blow-up
 Looking at the data from 2008 to 2010, names in Quintile One is more likely to suffer the blowup in spread
 We define blow-up as spread widens more than two standard deviation from the mean spread
movement at the same period of time
AllianceBernstein.com
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Fundamental Model Live Performance (2012- 2013)
AllianceBernstein.com
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What have we done so far?
Built Built Risk Regime Models – To get beta positioning right
Built Alpha Models - To get security level positioning right
Build Risk Models – To monitor risk in portfolios
AllianceBernstein.com
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Fundamental vs Quant
Failure of Quant Models:
Why Do quant models fail?
 Failure of Risk Regime Models
A steep slope is an environment for risk taking, but our FED has engineered a steep
slope and has not be necessarily been a great environment for risk taking
 Failure of Alpha Models
Mean reversion factor in the country-to-slope model – failed during crisis
 Failure of Fundamental Credit Models
Event Risk
 BOTTOM LINE – WATCHING MONEY FLOWS IS IMPORTANT IN FI MANDATES
 COMBINING QUANT AND FUNDAMENTAL SIGNALS IS VERY IMPORTANT
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Yield Curve Model failure during crisis -- Total Return
Barclays 7-10Y Total Return Ranked using Total Return Forecast:2000-2013
160
Top Bracket
Bottom Bracket
Spread
Ranking 12 DM countries:
140
We rank the countries
based on Total Return
Forecast and put highest
1/3 in the top bracket and
the lowest 1/3 in the top
bracket.
120
100
80
For each of the brackets,
we add the average of the
6 month forward total
return. (divided by 6 in
this monthly plot) for the
countries in the bracket.
60
40
20
0
-20
Jan00
May01
AllianceBernstein.com
Oct02
Feb04
Jul05
Nov06
Apr08
Aug09
Dec10
May12
Sep13
Fixed Income Asset Management
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FCM Model Risk
Potential high turnover and related transaction cost can eat up the excess return
 High turnover from equity return and earning revision
OAS is not a clean credit risk measure, hence valuation can be misleading due to:
 Risk aversion, driven by macro-environment and investor sentiment
 Liquidity premium, which is driven by supply and demand and institutional preference
The model works at the portfolio level and it should be used with great caution when
picking one or two names rather than a basket of names simply based on the
composite score
AllianceBernstein.com
Fixed Income Asset Management
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FCM Model Failure Examples
 AMGN: It was a low score name because of negative contribution from cash accruals factor. However, it
didn’t consider the nature of its one time litigation cash payment. Stripping away that component, it will make
the name more favorable
 DELL: Event risk (LBO). Dell was a high score name driven by attractive spread and positive equity
performance. However, the bonds failed to perform because the spread is priced in the forward-looking
increase of leverage and the company was transferring the wealth from debt holder to equity holder
AllianceBernstein.com
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AllianceBernstein.com
Fixed Income Asset Management
Sep-13
Aug-13
Jul-13
5Y
Jun-13
May-13
Apr-13
Mar-13
Feb-13
Jan-13
Dec-12
Nov-12
Oct-12
Sep-12
Aug-12
Jul-12
Jun-12
May-12
Apr-12
Mar-12
Feb-12
Jan-12
Dec-11
Nov-11
Oct-11
145
Sep-11
Aug-11
Jul-11
Jun-11
May-11
Apr-11
Mar-11
Feb-11
Jan-11
Dec-10
Nov-10
Oct-10
Sep-10
Managing Risks in FI Portfolios – Rate Volatility is important
USD Swaption Implied Vol (3M 5Y&10Y)
10Y
125
105
85
65
45
25
Source: Bloomberg. Unit: bps, annual implied Vol
45
Managing Risk in FI Portfolios -- Scenario Analysis
 When volatility spikes, historical correlations fail and even small exposures can be hurtful
Factor Name
Loading Unit
AUD
GBP
SGD
Total
MW
MW
MW
SGD YC Level
Net Loading Scenario Factors
8/27/2013
US Rates sell-off
Scenario
Aug Actual
Returns
Factors
8/27/2013
8
-1
-6
24
-1
-7
0
Actual
Returns
-11
-10
4
-8
6
-1
1
-24
-3
-26
OAD (Yr)
-0.16
77
1.3
210
3
GBP YC Level
OAD (Yr)
-1.19
106
12.6
237
28
GBP YC Slope
JPY YC Level
Long-Short KRDs (Yr)
OAD (Yr)
0.52
-7.98
80
19
-4.1
15.4
158
-57
-8
-45
JPY YC Slope
Long-Short KRDs (Yr)
-0.68
34
2.3
-54
-4
USD YC Level
OAD (Yr)
1.73
133
-23.1
133
-23
USD YC Slope
Long-Short KRDs (Yr)
0.34
131
-4.4
86
-3
EUR YC Level
OAD (Yr)
0.66
105
-6.9
177
-12
EUR YC Slope
Long-Short KRDs (Yr)
-1.48
61
9
91
13
AUD YC Level
Total YC
Total Total
OAD (Yr)
1.2
137
-16.1
-14
-14
207
-24
-74
-100
Note:Sample portfolio, for illustration only, may not reflect real portfolio performance and risk.
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What Happens when Both Fundamental and Quant Fails
Pray…
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Asset Allocation and Risk Parity
 What is Risk Parity?
 Ways to Accomplish Risk Parity
 Lessons from Studies in Risk Parity
 Applications of Risk Parity
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Introduction
 Magic of Portfolio diversification
Return
Year One
Return
Year Two
Asset A
100%
-50%
Asset B
-50%
100%
 Portfolio composed of two risky assets with 50% allocation to each
(rebalanced) delivers high return zero volatility (in this case)
50/50
Portfolio
Return
Year One
Return
Year Two
25%
25%
 Diversification is only free lunch?
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Different Approaches to Diversification
 Assets provide returns and risks
 Returns are hard to measure
 Perhaps the focus should be on risk
 Hence Risk Parity provides diversification of risk by spreading it over different risky assets
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Introduction
What is Risk Parity?
risk contribution from all assets is equal
Consider two assets: stocks and bonds
Expected Excess Return
Expected volatility
Stocks
Bonds
5.25%
1.75%
15%
5%
Compose two portfolios:
Risk Contribution
From Stocks
Risk Contribution
From Bonds
60/40 portfolio
92%
8%
25/75 portfolio
50%
50%
25/75 is a risk parity portfolio
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Risk Parity and Mean Variance Frontier
Risk Parity Line and Traditional Frontier
Risk Parity Line and Traditional Frontier
8%
Risk Parity Line
7%
6%
Return
Risk Parity 9.6%
5%
60/40
4%
25/75 Risk Parity
3%
2%
1%
0%
2%
4%
6%
8%
10%
12%
14%
16%
Risk
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Discussion
 Advantages of Risk Parity
 Need not rely on Expected Returns
 Need not rely on MVO
 Disadvantages of Risk Parity
 Need leverage
 May not beat 60/40 all the time
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Active Risk Parity
In Active Risk Parity, Bhansali [2012] uses data from January 1990 to March 2012
(Source Bloomberg)
Uses Monthly Close price for index, and target 10% total Portfolio volatility
First iteration uses dynamic correlation with look-back period of 12 months
Volatility proxy indices used:
Equity
Bond
Proxy used for Volatility
VIX
MOVE
Vol. contribution
Beta * VIX
Rate Vol * Duration(BAGG)
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Active Risk Parity
 Exhibit 1: Relative performance of a 60/40 vs.
Hypothetical Optimal Two-Factor Risk Parity Portfolio
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 Exhibit 2: Comparison of a 60/40 vs.
Dynamic Factor Risk Parity Portfolio
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Inferences from Research on Risk Parity
Risk Parity is a useful concept
Could be a fruitful portfolio implementation tool
May not outdo 60/40 all the time
Certainly should be used a trojan horse
Outperformance of RP is a function of
 Time Period
 Instruments Chosen
 Correlations between stocks and Bonds can be unstable
 Spikes in correlations can hurt levered RP funds
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Issues with Risk Parity
No theoretical basis for RP
Cannot completely ignore expected return estimates
Sharpe Ratios are not necessarily equal amongst asset classes
Levering Bonds may not be optimal in all scenarios
When leverage is used, expected return cannot be totally ignored!
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Application of Risk Parity
Risk parity can increase bond return preserving information ratio
Cumulative returns
2.5
2
1.5
RP
60/40
1
S&P
B Glob T
0.5
Aug-12
Aug-10
Aug-08
Aug-06
Aug-04
Aug-02
Aug-00
Aug-98
Aug-96
Aug-94
Aug-92
Aug-90
-0.5
Aug-88
0
Risk Parity
60/40
S&P
Barclay Global Treasury
1.90
0.96
0.68
2.08
STD
3.9%
9.1%
14.9%
3.1%
mean
7.4%
8.7%
10.2%
6.5%
IR
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Can we construct a RP portfolio using mutual funds?
Risk parity can achieve equity like return with FI only
Cumulative returns
3
2.5
2
RP
1.5
USG Idur
US HI
AB EM Debt
1
60/40
0.5
US Gov Int duration
1.34
1.68
STD
6.3%
mean
8.4%
US HI
Jul-12
Jul-11
Jul-10
Jul-09
Jul-08
Jul-07
Jul-06
Jul-05
Jul-04
Jul-03
Jul-02
Jul-01
Jul-00
Jul-99
Jul-98
RP
IR
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Jul-97
-0.5
Jul-96
0
AB EM Debt
60/40
0.84
0.93
0.77
3.8%
16.0%
16.1%
10.0%
6.4%
13.5%
14.9%
7.7%
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Issues with Risk Parity
When can RP fail
 When equities rally
 Other reasons
RP and Tail Risk
 RP does not explicitly account for tail risk concerns
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Thank you
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Appendix 1-- Risk Parity in terms of risk factors
In Risk Parity for the Masses by Steiner [2012],
 Portfolio’s variance can be expressed as
 Introduce the covariance
 Divide by
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,
,
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Appendix 1-- Risk Parity in terms of risk factors (contd)
 Take partial derivative,
 Therefore,
can be decomposed to:
 Define rci as:
 s.t.
 The risk parity portfolio is defined as a portfolio in which all assets have equal contribution to
volatility:
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