This document discusses risk management practices for hedge funds. It notes that risk management approaches differ across hedge funds based on their strategies. Common risk management tools discussed include Value at Risk (VaR), stress testing, concentration limits, drawdown management, and liquidity risk monitoring. The document cautions that no single risk management approach is best and that risk measures have limitations but can still provide useful insights if used appropriately.

1. Hedge Fund Risk
Management
Barry Schachter
Prepared for
Yale School of Management
Hedge Fund Program for Chinese Executives
September 17, 2015

2. The Varieties of Hedge Fund
Risk Management
Hedge funds are not all alike, and risk management is not the
same for all funds.
Hedge Funds Not all Alike
Risk Management Needs
Differ from HF to HF
No Single Approach to Risk
Management is Best
No external force (i.e.,
regulation) creates a
‘monoculture’ of Risk
Management

3. The Place of the CRO in the
Hedge Fund Organization
Most Hedge Funds Have
No CRO
Need for Independent Risk
Function Is a Flawed Idea –
Does Not Solve Agency or
Incentive Problems
Risk Management
Knowledge creation
Information exchange
Risk-Informed
Decisions
Many HF Risk Managers
only perform one or two

4. Knowledge Creation - Risk Prism
Good Risk Management Identifies Risks Like A Good
Prism Separates White Light

5. Risk Filter
Good Risk Management Mitigates Unwanted Risks
Like A Filter Eliminates Specific Colors from Light

6. Understanding Risk
Management
• From Harvard Business Review (March 2009)
o “Six Ways Companies Mismanage Risk” by René Stulz
• Mismeasurement of Known Risks
• Failure to take risks into account
• Failure in communicating the risks to top
management
• Failure in monitoring risks
• Failure in managing risks
• Failure to use appropriate risk metrics

7. Understanding Risk
Management
• These Six Are Better Thought of As the Recurring
Challenges of Risk Management
• A Successful Risk Management Function will Address
All Six
• But Cannot Eliminate Any of these Challenges

8. Assumptions Matter
• Assumptions Place Limitations on the Exploration of
Potential Risks
• It isn’t Enough to Question All Assumptions
• It is Necessary as Well To Identify Hidden
Assumptions
• It is Necessary to Consider Possibilities that are
Outside of Experience
• This Process Can Be Formalized as An Extension of
the Commonly Used “What Can Go Wrong?”
Analysis of Investment Ideas

9. Assumptions Matter

10. Assumptions Matter
Global Financial Crisis Partly Blamed on MBS
Risk Models where HPA Rate Could Not Be
Negative for a Large, Geographically Diverse
Portfolio
…ratings agencies assigned their highest rating to
many mortgage-backed securities based partly on
the assumption that home prices would not fall.
Source: Investopedia (http://bit.ly/1icYojX)
…assumption that a diversified portfolio of US real
estate had little risk of falling in value.
Source: E. Rosengren. (http://bit.ly/1icYS9E)
…models used to price mortgage portfolios under-
weighted scenarios with large price declines.
Source: J. Kourlas. (http://bit.ly/1id1J2g)

11. Sources of Risk for HFs
Hedge
Fund
Economy
&
Markets
Investors
Data &
Systems
Trading
Counter-
parties

12. Risk from Economy and
Markets

13. Trading Risk
Management
• Focused on Total Return, HFs Do Not Have a
Benchmark (other than cash)
• Active Risk Equals Total Risk
• Tracking Error is not a Meaningful Measure of
Portfolio Risk
• Risk Management of HF Trading Risk Does Not
Reduce to Solving A Standard Portfolio Optimization
Problem
• Aspects of the Optimization Problem Remain in
o Control of Common Factor Exposures
o Influence on Risk Allocation (Risk Budgets)

14. Trading Risk
Management
• Trading Risk Management Employs Multiple Risk
Measures to Support a Decision Process that is
Data-driven but Ultimately Qualitative
• In this Process Return Correlation is Not Sufficient
Information for Making Risk-Based Decisions
• Using Multiple Measures Adds to Confidence in
Decision-Making and Improving Communication
About Risk
• Using Multiple Measures Helps Capture Elements of
(Knightian) Uncertainty that Are Not Well
Represented by Correlation

15. Measures Used in Trading
Risk Management
• Value at Risk (VaR) (or Expected Shortfall)
• Stress Testing
• Sensitivities (e.g., Duration, PV01, OAS, Credit
Spread options)
• Notionals (e.g., Gross and Net Market Value)
• Beta-adjusted Exposures (single or multi-factor)
• P/L volatility
• Drawdown
• Measures of Liquidity
• Measures of Concentration

16. Value at Risk (VaR)
 It Is the Magnitude of
Portfolio Loss that May
Be Exceeded
with a Specified
Probability (e.g., 5%)
or Confidence Level
 This is a Statistical Risk
Measure (Derived from
Statistics of a Set of
Observations)

17. Calculating VaR
 Many Ways to Calculate and Each
May Give Different Results, but All
Are Alternative Estimates of a
Distribution Function (Unknown, Assumed, or Fitted
by Backtesting)
 General Approaches:
 Parametric – Covariance Matrix
 Historical – Empirical (Nonparametric)
Distribution
 Monte Carlo – Covariance Matrix and
Simulation
 Most Vendors Include Most of These Alternatives

18. Knowledge from VaR
 Risk Aggregation – Allows
Combining Risks from Different
Sources
 Risk Comparison – Creates A Common Measuring
Stick
 Risk Allocation – Can Deduce Rules to Distribute
Risk (Based on Marginal Contribution to Portfolio
Risk)
 Risk Attribution – Can Identify Exposures to
Components of Portfolio Risk

19. VaR Limitations
 Assumes Historical Data Used Is
Appropriate for Forecast
Distribution Estimate
 Many Subjective Elements in Modelling
 Filling Missing Data, Implied Volatility
Modeling, Security Pricing Models, Proxy
Rules
 False Precision & No Confidence Bands
 VaR Calculation Methods Each Have
Unique drawbacks
 Parametric: Normal, Nonlinear Positions
 Historical: No ‘beyond the sample’
 Monte Carlo: Parametric

20. VaR Problems – Imagined
 Assumes Normal Distribution
 Have Explained it is Not Necessary
 Works Until You Most Need it
 Need Risk Measures in All Environments,
Not Just Crises
 Didn’t Predict the Crisis
 Not Intended to Predict Returns
 Excessive Reliance on VaR Contributed to the
Crisis
 Anecdotally, I never saw much reliance
placed on VaR

21. Using Value at Risk
Limits
 The Good
 One of few truly risk-based measurements
 Can be used on very complex portfolios
 The Bad
 Can be Gamed by the Risk Taker, and
 May force selling in market decline and
Encourage buying in quiet market
 Not Well Adapted to Some Strategies, like
Merger Arb, Distressed Credit
 The Ugly
 Has Been Accused of Contributing to
Systemic Risk (“Liquidity Black Hole”)

22. Black Swan Risk
• ‘Black Swan’ Made Familiar by Nassim Taleb
• Event that Cannot Be Anticipated, Has a Big
Impact, and is Explained only after the Fact
• Often Mischaracterized As Any Low Probability
Event or Emerging Risk
• Because A Black Swan Is Unanticipated, it Cannot
be Hedged (Beforehand)
• It is Claimed that Building General Resilience into
Risk Taking is the only Way to Manage This

23. Tail Risk Hedging
• ‘Black Swan’ Insurance
• Hedge against the Unknown
Unknowns
• Some Have Argued that All
Market Disruptions Exhibit
Increased Volatility and
Equity Sell-offs
• Tail Risk Therefore Is Usually A
Long Option Strategy
• Unlikely to be cost effective
if
o Options Fairly Priced (A
Cognitive Bias Issue)
o Measured over a Long Time
Interval (A Business Cycle)

24. Stressed VaR
Global Financial Crisis Partly Blamed on MBS Risk
Models where HPA Rate Could Not Be Negative for a
Large, Geographically Diverse Portfolio
…ratings agencies assigned their highest rating
to many mortgage-backed securities based partly
on the assumption that home prices would not
fall.
Source: Investopedia (http://bit.ly/1icYojX)
…assumption that a diversified portfolio of US
real estate had little risk of falling in value.
Source: E. Rosengren. (http://bit.ly/1icYS9E)
…models used to price mortgage portfolios
under-weighted scenarios with large price
declines.
Source: J. Kourlas. (http://bit.ly/1id1J2g)

25.  Estimate Risk Using Volatile
Environment
 Risk Changes Over Time Caused
Only by Portfolio Changes
(Look-back window never moves)
Stressed VaR

26. Reverse Stress Test
Scenario-based Stress Test:
What you do: Define price shocks to risk
factors, then revalue the portfolio
What you get: An estimate portfolio loss that
results from applying those shocks
Reverse Stress Test:
What you do: Define a portfolio loss level,
then investigate through what shocks that
may happen
What you get: Many alternative sets of price
shocks that all result in the defined loss level

27. Finding Good Scenarios from
Reverse Stress Testing

28.  Drawdown: Fall from a
Maximum
 Drawdown in neither
inherently good nor
Bad
 Volatility entails
drawdown
 Expected
Drawdown
Increases as
Volatility Increases
 Drawdown has a down
side, however
Drawdown

29.  Return is about end points,
Drawdown is about the path
taken between end points
 The path matters if it can affect
the outcome
 Drawdown may reflect Bad
Portfolio Decisions
 Drawdown may Trigger Investor
Redemptions
Why Manage Drawdown

30.  Limit: At a certain magnitude of drawdown risk is
reduced or portfolio liquidated
 Drawdown Limits Reduce Expected Return (In
General)
 Intermediate Drawdown Thresholds may create
magnet effect (“pull to limit”)
 Considerations
 Portfolio Liquidity
 Magnitude of Correlations (Multi-manager)
 Trading Styles
 Investor Liquidity Terms
Drawdown Limits

31. Concentration &
Diversification
• One or a Few Positions
Contribute
disproportionately to
Portfolio Risk
• Causes Too Much
Portfolio Sensitivity to
Idiosyncratic Events, or
Exposure to a Market
Factor

32.  Constraints on Maximum Position Size
 In Long/Short Equity, Commonly 10% of
AUM for Longs, 5% for Shorts
 Constraints on Factor Exposures – In
Long/Short Equity Sometimes set at Zero
Exposure
Concentration &
Diversification

33. Trading Liquidity Risk
• The Risk to Realization of Gains on a Position
or Augmentation of Losses When Attempting
to Exit
• Liquidity is Defined In Terms of The Cost to
Exit
• Factors Affecting That Cost Are
o Position Size in Relation to Market Trading Activity
o The Transaction Costs of Exiting (Including Crossing
the Spread)
o The Impact of Speed of Exit on Price

34. Trading Liquidity Risk
• Optimizing Exit Strategy Is More Common
In Algorithmic Trading
• Monitoring Liquidity and Setting Liquidity
Limits Is The More General Approach
Used

35. Trading Liquidity Risk
• Monitoring Liquidity
o Exchange-traded – Position Size as Percent of
Average Daily Volume
o OTC – Two Dimensions
• The Product of the “Clip Size” and Number of Clips That
Can be Traded in a Day with Minimal Market Impact
• The stability of the ability to transact
• Liquidity Limits, If Used, are Strategy
Dependent
o May Take the Form, “No More than X% of Long (Short)
Exposure Requires More than 5 Days to Exit at 20% ADV”

36. Crowded Trade Risk
• A Special Case of Liquidity Risk
• Crowded Trade Origins
o Hedge Funds Follow Similar Strategies (E.g., Merger Arb)
o Hedge Fund Group Think (E.g., Global Macro often)
• Creates Latent Illiquidity, Manifests Only
when Hedge Funds as a Group Attempt to
Exit at the Same Time
• Difficult to Detect Beforehand, Except
Anecdotally

37. Crowded Trade Risk
Management
• Data Monitoring and Reporting
o Large Hedge Fund Ownership in Portfolio Names/Strategies
o Large Equity Short Interest
o Changes in correlations among assets
• In Some Cases, Maintaining Lower Leverage
(or More Unencumbered Cash)

38. Risk Parity - Crowded
Trade?
• Risk Parity Made
Popular By Bridgewater
and AQR
• Equities and Fixed
Income Balanced
weighted to Have
Equal Risk Contribution
(Requires Adding
Leverage to the FI
Share)

39. Risk Parity – Crowded Trade?
 Problem:
bonds and
equities sell-off
at same time
 1 August,
JPMorgan’s
index of risk
parity funds
lost 8.2 per
cent since the
beginning of
May;
 Russell 3000
lost 9.2% over
same period

40. P&L Attribution
• Evaluate the Extent Gain/Loss Arises from
Unwanted or Unexpected Sources
• Use multiple approaches
o By strategy, or long vs. short Positions
o By single factor or multiple factor decomposition

41. Trading Risks from
Cognitive Biases
• Risk Taking Decisions are Subject to Bias
o https://en.wikipedia.org/wiki/List_of_cognitive_biases
• Important Biases for HF Risk Management
o Confirmation Bias
o Hindsight Bias
o Recency Bias
o Disposition Effect
o Round Number Bias
• Managed Through
o Data Analysis, e.g., holding period analysis
o Qualitative Discussion with Risk Takers, Adopting Heuristics

42. Risk from Counterparties

43. Quantification of
Counterparty Risk
• In General, Hedge Funds’ Approach is
Unsophisticated, Limited to Monitoring Current
Exposure
• Counterparty Exposure Measures
o Current Exposure
• Margin Posted (Required and Excess)
• OTC Swap and Option Exposure
o Potential Exposure – VaR-like Estimate
• Measures of Counterparty Default Risk
o Credit Spread and Credit Rating
o Stock Return and Implied Stock Volatility

44. Management of
Counterparty Risk
• Minimize Excess Margin
• Limit Rehypothecation (or Keep Margin/Collateral
at 3rd Party)
• Use Multiple Prime Brokers

45. Funding Liquidity –
Counterparty Margin
• Margin Calls Can Become An Existential Risk for a HF
o Insufficient Unencumbered Cash Requires forced Position
Liquidation
• HFs Target Significant Levels of Unencumbered
Cash
• Margin Should be Optimized to the Extent Feasible
o Prime Brokers Use Different Margining Approaches
o Allocate Trading Positions to Primes to Minimize total Margin
• Conduct margin portfolio stress tests
o Must Model Margin Agreement Terms
o Examine Impact of Changes in Market Environment and
Possible Changes in Required Margin

46. Risk from Data and
Systems

47. Operational, Legal,
& Reputational Risk

48. Operational Risks - Mitigation
• Trading
o Fat Fingers – Limit Trade Sizes
o Incorrect Orders – Internal Verification and Review
• Risk Measurement
o Bad Historical Prices – Integrity Checks
o Incorrect Position Set-up – Internal Verification for new
Positions
o Diagnostic Reports (after the fact)
• Proprietary Code
o Test Cases – User-performed Tests
o Cross-Validation

49. Risk from Investors

50. Investor Risk Disclosure
• Investors are better partners when they
understand the strategy and are Able to
Correctly interpret the risks and returns
• Disclosures are an Opportunity to Dispel
Misconceptions/Biases, E.g.,
o Interpretation of Maximum Drawdown
o Usefulness of Benchmark Tracking Errors

51. Investor Risk Disclosure
• Should Disclose Information that Allows Investors to
Determine Fund is Following Risk Guidelines in PPM
• Should Disclose Risk Decomposition Consistent with
Permitting Investors to Evaluate Strategy Drift
• I Would Not Participate In Risk Aggregation
Programs, such as Opera or Hedge Platform, as It is
Impossible to Stand Behind the Data After It has
Been Processed by a Third Party
• I Would Not Disclose Non-Public Regulatory
Information, such as form PF, as It May Be
Misleading as to the Fund’s Actual Risk

52. Investor Risks
 Liquidity –
Redemptions
 MFN” clauses
and side letters,
 the death spiral)
 Adverse affects on
strategy
implementation
 Managing to
monthly P&L
 Managing
drawdown

53. Emerging Risks
• Identification through Environment Scanning
o “…those who look only to the past or the present are
certain to miss the future.” John F. Kennedy (http://bit.ly/1IQWxXb)
• Known Unknowns that
o Cannot be Quantified (Knightian ‘uncertainty’),
o Are Not Priced,
o Nor Able to Be Hedged through Diversification

54. Emerging Risk
Measurement
• Big Data Analytics
o RecordedFuture.com – Threat Analysis
o DataMinr.com – Twitter as Information Discovery
o Cytora.com – Knowledge from Unstructured Public Data
• Brainstorming
Potential Scenarios
• Current Example
o El Nino
• Impact on Agriculture
• Transport
• Sensitivities

55. Emerging Risk - Example
• El Niño – Identified as a Likely to be “Very
Strong” (3rd time Since 1950)
• Impact Cited or Speculated
o Agriculture, E.g., California
o Transport, E.g. Transport
Cost from Asia to US
o Winter Resorts
• Estimate Price
Sensitivities (Severity)
• Estimate Likelihood

56. In Sum
• Have only Touched on Many Aspects of and Issues
in Hedge Fund Risk Management
• Hedge Fund Risk Management Defies A Simple
Description
• This is a good thing
• In Every Manifestation
Aimed at Improving
Risk-based Decisions
• Improving Performance,
not Avoiding Risk

57. Additional Readings
• “Parity Strategies and Maximum Diversification”
http://bit.ly/1JHmQQe
• “Extreme Risk Management” http://bit.ly/1JHmUzy
• “Financial Risk Management: A Practitioner’s Guide to
Managing Market and Credit Risk”, John Wiley & Sons, 2012
•