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Empirical Analysis of Fund of Hedge Funds (Tass database)
Presented to:
Research Project andWorking Paper
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Presenter: Florian BoehlandtUniversity: University of Stellenbosch – Business SchoolSupervisor: Prof Eon Smit
Prof Niel KrigeResearch Title: A Risk-Return Assessment of Fund of Hedge
Funds in Comparison to Single Hedge Funds – An Empirical Analysis
Contact: [email protected]
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‘In the business world, the rearview mirror is always clearer than the windshield’
- Warren Buffett -
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Research Purpose
1. Comparative time series analysis of Fund of Hedge Funds vs. Single Manager Funds
2. Estimating the impact of leverage on downside volatility and risk
3. Constructing style indices from risk parameters and AUM weightings
4. Automating data import and data analysis for future quantitative analysis (‘dashboard’)
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Code Execution (1/2)
Data Import •Extract relevant data from Access (SQL)•Import data as Pivot table report
Data Treatment •Test for serial correlation / normality•Calculate adjusted excess returns
Data Analysis •Select funds with consistent data series •Determine fund specific risk parameters
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Code Execution (2/2)
Weighting •Estimate weighted average parameters•Construct style indices
Comparative Analysis •Calculate within-group variation•Calculate between-group variation
Data Output •Tabular display of aggregate results•Construction of line - bar charts
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Hedge Fund Categories (TASS)
Categories
Directional
Dedicated Short Bias
Global Macro
Emerging Markets
Global Macro
Long / Short Equity
Managed Futures
Fund of Hedge Funds Market Neutral
Equity Market Neutra
l
Event Drive
n
Event Driven
Convertible Arbitrage
Fixed Income Arbitra
ge
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Data Import
•Code•Fund (Name)•Main Strategy
Information
•MM_DD_YYYY (Date)•Yield•Ptype (ROI or AUM)
Performance
•Leverage (Yes/No)System
Information
Access Database Excel Pivot table report
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Risk-Return Parameters (1/2)
• Return on Investment• Downside Risk
– Standard Deviation– Downside Deviation– Value at Risk– Modified Value at Risk
• Maximum Continuous Drawdown
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Risk-Return Parameters (2/2)
• 3-Factor Regression– Regression Alpha– Average Error term– Information Ratio
Adaptation Current Research
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t – test (betweenstrategies)
UnbalancedANOVA (withinand betweentreatments)
t – test (leveragevs. no leverage)
t – test forequal means
t – test forequal means
t – test forequal means
Statistical Tests
Strategy 1Leverage
Strategy 1No
Leverage
t – test forequal means
Strategy 2Leverage
Strategy 2No
Leverage
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Step 1: Copy folder to desktop or hard drive
User Guide (1/4)
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Step 2: Manual amendments to source code
User Guide (2/4)
'***********************************************'-->RAWDATA()'Rotate through PivotItems Strategy'1 = Convertible Arbitrage'2 = Dedicated Short Bias'3 = Emerging Markets'4 = Equity Market Neutral'5 = Event Driven'6 = Fixed Income Arbitrage'7 = Fund of Funds'8 = Global Macro'9 = Long/Short Equity'10 = Managed Futures'***********************************************'set index and endloopi to stategy in focus (e.g. 7 for Fund of Funds)'set endloopj and startloopj to strategies compared'e.g. comparing Fund of Hedge Funds to Fixed Income Arbitrage :
index = 7endloopi = 7endloopj = 7startloopj = 6
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Step 3: Open spreadsheet shell and start execution
User Guide (3/4)
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Step 4: Fill in Userform
User Guide (4/4)
Select hard drive
Select file path
Select parameter
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Example Output (1/2)
1/1/1994 7/1/1995 1/1/1997 7/1/1998 1/1/2000 7/1/2001 1/1/2003 7/1/2004 1/1/2006 7/1/200750
100
150
200
250
300
350
400
450
500
550
600
Fund of Funds
Convertible Arbitrage
Dedicated Short Bias
Emerging Markets
Equity Market Neutral
Event Driven
Fixed Income Arbitrage
Global Macro
Long/Short Equity
Managed Futures
S&P 500 DRI Index
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Example Output (2/2)
04/01/199807/01/199910/01/2000 01/01/200204/01/2003 07/01/200410/01/200501/01/200704/01/2008
-15-10-505
1015
00.0050.010.0150.020.0250.030.0350.04
Fixed Income Arbitrage
Test
sta
tistic
Fund of Funds
FoH
F 36
m V
olat
ility
04/01/1998 07/01/1999 10/01/2000 01/01/2002 04/01/2003 07/01/2004 10/01/2005 01/01/2007 04/01/2008
-15-10-505
1015
-0.2-0.100.10.20.30.40.50.6
Global Macro
Test
sta
tistic
Fund of Funds
FoH
F 36
m IR
Joint starting point
Significance
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Empirical Findings (1/2)
• Measures of volatility and downside risk were significantly improved for FoHFs, compared to their single-strategy peers
• No evidence was found that FoHF strategies overcharge for risk diversification benefits
• With reference to continuous drawdown, attrition rates and VaR, FoHFs are a valuable supplement to the institutional portfolio
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Empirical Findings (2/2)
• It could not be established whether gearing affected hedge fund performance – either favourably or adversely
• Some statistical evidence could be found of a higher exposure of leveraged funds to the recent subprime crisis
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Extended Research
• Hedge Fund Linear Pricing Models– Sharpe Factor Model (Sharpe, 1992)– Constrained Regression (Otten, 2000)– Fama-French Factor Model (Fama, 1992)
• Factor Component Analysis (Fung, 1997)• Simulation of Trading component (lookback
straddle)
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Prediction Models
Prediction Models
AR
ARMA
ARIMA
GLS
Univariate
Multivariate
Conditional
PCA Polynomial Fitting
Taylor Series
Higher Co-Moments
Constrained
Lagrange
KKT
Simulation
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Sources
Fama, E.F. & French, K.R. 1992. The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2), June, 427-465. [Online] Available: http://links.jstor.org/sici?sici=0022-1082%28199206%2947%3A2%3C427%3ATCOESR%3E2.0.CO%3B2-N
Fung, W. & Hsieh, D.A. 1997. Empirical characteristics of dynamic trading strategies: the case of hedge funds. Review of Financial Studies, 10(2), Summer, 275-302. [Online] Available: http://faculty.fuqua.duke.edu/~dah7/rfs1997.pdf
Otten, R. & Bams, D. 2000. Statistical Tests for Return-Based Style Analysis. Paper delivered at EFMA 2001 Lugano Meetings, July. [Online] Available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=277688
Sharpe, W.F. 1992. Asset allocation: management style and performance measurement. Journal of Portfolio Management, Winter, 7-19. [Online] Available: www.uic.edu/classes/fin/fin512/Articles/sharpe.pdf