Carol Osler, Discussant
Stern Microstructure: May 8, 2015
» Forex doesn’t “close”
˃ Still need benchmark prices
˃ 1993: WM Co + Reuter “Fixes“
˃ Most influential: 4pm London time
» Customers submit orders to dealers for trades @ Fix
˃ 3:45pm deadline for 4pm Fix
˃ Dealers commit to trade with customer @ Fix price
» Fix traders = Fund managers to avoid tracking risk
˃ Large orders
˃ Many executed simultaneously
 High volatility pre-Fix
» 2011: Melvin & Prins highlight high volatility @ Fix
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“London 4pm Fix: The most important FX institution you never heard of”
Focus on end-month
Surprisingly: Does not document volatility
Just analyzes it
+ Provides explanation based on portfolio hedging
–  Equity returns   Hedging flows @ Fix   Volatility @ Fix
– Substantiates with regression analysis
+ Highlights perverse incentives of Fix trading: Maximize price move
» Hedging perspective suggests Fix volatility self-reinforcing
˃ Hedge trades @ Fix   Volatility @ Fix   Tracking risk
˃ Could explain secular rise in Fix volatility
» At Fix, incentive is to Maximize price move
Normal Trade
S
Incentive:
Minimize price move
Standard techniques:
Split trades ….
B
S
Dealer buys ,
Closes position
Dealer quotes prices
Sells to customer
Fix Trade
Incentive:
Maximize price move
New techniques ….
S Fix
S
B
S
Dealer learns
Amount to sell
B
B
Dealer
Buys
Dealer sells
Closes position
» June 2013, Bloomberg: Traders colluding to manipulate Fix
prices in chat rooms
˃ Fall 2013:
Many traders put on leave, fired, etc.
Civil suits filed
Revised version of Melvin & Prins (Nov 2013)
Says nothing about dealer incentives
to maximize price moves *
˃ 2014:
Traders on leave are fired or “resign” (total 20+)
Civil suits combined to class action suit
˃ Late 2014: Banks pay big fines to CFTC, FCA, others
Tantalizing chat details courtesy of regulators
“Cartel” “Let’s double-team them” …..
˃ Future …. Criminal suits against individual traders?
» Thoroughly documents unusual dynamics around 4pm Fix
˃ 315 Charts, 14 Tables, average entries/table = 290, 4-6 significant digits
Section 3. Volatility @ Fix
+ How big is Fix volatility relative to long-run volatility?
+ How big is Fix volatility end-month vs. mid-month?
+ How big is daily volatility relative to returns @ macro horizons?
Small
Much bigger
Very Big
Section 4. Placebo: Volatility away from Fix
+ How big is volatility at normal times vs. the Fix?
+ Has distribution of normal returns shifted over time?
Section 5. What exactly happens pre-Fix?
+ Big moves beginning 3:45, especially end-month
Section 6. What exactly happens post-Fix?
+ Less volatility than pre-Fix
+ Some retracement of biggest pre-Fix moves
Small
Not much
» Evidence for high volatility around 4pm Fix
Frequency distribution, pre-Fix returns
EUR/USD 5 Min
-20
Non-Fix
-10
0
10
Mid-month Fix
20
End-Month Fix
» Retracement tendency is “remarkable”
Basis Points
»
20
15
20
5
0
-5
-10
-15
-20
3pm
>75th percentile end-month
End-month avg
Mid-month avg
Mid-month avg
End-month avg
>75th percentile end-month
3:30
4:00
4:30
5:00
» Retracement tendency provides profitable trading strategy
˃ Sell @ 4pm if price rises 3:45 – 4:00, vice versa
˃ For end-month, profitable & high Sharpe even after transaction costs
˃ Reliable? Assumes can always trade at prices in dataset
+ At month-end Fix, EUR Fix trading sometimes > $500 (FCA)
+ EUR 4pm limit-order-book cumulative depth averages $170-$210 million
+ Hmmmmm
» Suggestion: Examine retracement at longer horizons
˃ For end-month Fixes, major share of pre-fix returns typically reversed
by noon the next day
» Documentation seems to be paper’s primary goal
» “Behavior of spot rates in the minutes immediately before and after 4:00 pm are
[sic] quite unlike that observed at other times”
» Paper wisely careful about “collusion”
˃ … if indeed [collusion] took place, could [it] have materially affected the
determination of the Fix to the detriment of participants in the forex and other
financial markets. This paper presents statistical evidence pertinent to this issue.
» Also: Insights from standard models
» Section 2: “Standard model” = Portfolio Shifts (PS)
˃Designed to capture forex market “over the trading day”
˃Batch trades in sequences of 3 with specific characters
+ Customer (Random Investors)  Dealer
+ Dealer Dealer
+ Dealer  Customer (Risk-averse Investors)
» Volatility is to be expected with portfolio rebalancing
˃ If many funds want to sell foreign currency, other agents must buy
+ Dealers do not hold positions overnight
+ Contrast with Duane’s perspective – rebalancing not fundamental
information
˃ In PS model, “other agents” are other investors
+ Price falls to create bigger risk premium on foreign currency
˃ In reality, “other agents” also include firms who import/export
+ Price falls to make imports of foreign goods cheaper
˃ Evidence to date supports active role of import/export firms in
absorbing financial rebalancing flows
» Is PS model consistent with observed behavior around Fix?
» PS model does not predict surge, 3:45-4:00, and retracement
+ Model implies instantaneous jump at 3:45 to end-of-day price
+ No retracement
» Is PS the only relevant model?
˃ Advantage: Incorporates market’s 2 tiers
˃ But Fix price dynamics occur
entirely within interdealer market
˃ Any dealing model potentially helpful
Dealer
Importers,
Exporters
Dealer
Asset
Mgrs
» Could be fruitful to examine other models
˃ PS assumes perfect competition among dealers
+ True in 2002
+ But interdealer market now highly concentrated
+ Insights from models of imperfect competition?
– E.g. Holden & Subrahmanyam (1992) rat race
– Predicts smooth price approach to new equilibrium after information
˃ PS assumes dealers know ALL customer order flow when trading with
other dealers
+ But in reality … they don’t (and wish they did …..)
+ Most existing models assume incomplete dealer information
+ Insights from classic Figlewski (1981)?
» Could be fruitful to examine other models
˃ PS dealers are combination of Seppi’s informed & rebalancing traders
– Know customer rebalancing flows
– Are (essentially) doing the trading for customer
– Have good information about true value
» Because it depends on magnitude of rebalancing
+ Insights from Seppi’s model?
˃ PS assumes batch trades in sequences of 3 with specific character
+ But allegations of manipulation describe attempts to exploit
continuous trading process
+ Insights from Kyle models with many trades or from sequentialtrade models?
» Could be fruitful to design other models
˃ PS assumes dealer incentives = Bank incentives
+ But in reality, alleged collusive activities violated bank policies
– Collusion allegations include sharing client order information
– “Compliance” offices routinely stress importance of protecting client
information
– Dealers may have maximized own bonuses at bank expense
– Model with agency costs (& chat rooms = low costs of collusion?)
˃ PS assumes dealers first trade with customer, then cover their position
+ But sequence reversed for Fix trades
+ Reverse sequence creates perverse incentives (Melvin & Prins 2011)
+ Optimizing model of Fix trading?
» Thorough documentation of price dynamics @ London 4pm Fix
˃ Exchange rate behavior provides great puzzles
» Interesting discussion of whether dynamics fit PS model
˃ Recommend examine whether dynamics fit other standard models
˃ Maybe develop new models consistent with institutional constraints and
incentives unique to Fix
» Other suggestions
˃ Trimming tables, charts
˃ Measures more closely tailored to examining potential influence of Fix
˃ -- offline