why understanding hedge fund beta is important
DESCRIPTION
Case Study Presentation at the Portable Alpha Asia 2006 conference on 25th – 27th April 2006 Conrad Hotel, Hong Kong, ChinaTRANSCRIPT
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Why understanding hedge fund beta is important?
Portable Alpha Asia 2006 Conference25 – 27 April 2006, Conrad Hotel, Hong Kong
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This presentation and the analysis herein contains proprietary information and is not to be copied, reproduced, used, or divulged to any person in whole or in part without proper written authorization from an officer or director of Siemens AG. This information is the property of Siemens AG and is subject to completion and amendment.
The content of the presentation should not be interpreted as legal, tax, or investment advice. This document has been prepared by Siemens for discussion purposes only, based upon unaudited financial data. Siemens does not make any representation that the strategy will or is likely to achieve performance comparable to that shown. This document is not an offer to sell or a solicitation for the sale of a security nor shall there be any sale of security in any jurisdiction where such offer, solicitation, or sale would be unlawful. An investment in any of the products may involve a high degree of risk, including the risk of complete loss of an investment, and may only be made pursuant to final offering documents. Past performance of Siemens and / or any of its respective affiliates, employees, members, or principals is not indicative of future results and is no guarantee targeted performance will be achieved.
Siemens is under no obligation to release to the public any revised financial data that reflect anticipated or unanticipated events or circumstances. This presentation does not claim to be all-inclusive or to contain all of the information that any particular party may desire. No representation or guarantee is made regarding the accuracy or completeness of any of the information contained herein. Any person in possession of this presentation agrees that all of the information contained herein is of a confidential nature. Furthermore, the same person will treat the information in a confidential manner and will not directly or indirectly, disclose, or permit agents or affiliates to disclose, any of such information without the prior written consent of Siemens.
BY ACCEPTING THIS DOCUMENT YOU ACKNOWLEDGE THAT ALL OF THE INFORMATION HEREIN SHALL BE KEPT STRICTLY CONFIDENTIAL BY YOU.
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Agenda
Why understanding hedge fund beta is important?
Differentiating between hedge fund beta and traditional investment beta
Are hedge funds just repacking beta and selling it as a pure alpha strategy?
Replicating the hedge fund returns synthetically
Why should investors pay for embedded beta?
Conclusion
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Differentiating between hedge fund beta and traditional investment beta
Return Alpha = SkillMarket Return
=Market Risk
ResidualsSingle Index Model
ttXY εβα ++= *t
tttt XAY εβδα +++= **
Pure Alpha „Agility“
Hedge Fund
Return
Market Return=
Market RiskResiduals
APT
VolatilityRisk
CommodityRisk
CurrencyRisk
CreditRisk
... FactorsTBD*
ttkkttttt XXXXXY εβββββα +++++++= ***** 44332211 L
*See Appendix A.
Hedge Fund Beta:
Traditional beta:
Beta Agility
Alpha Agility
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An example
Source: CSFB, Siemens/fin4cast
Traditional beta Hedge fund beta
Alpha
Beta
Ag
ility
Risk Facto
rs
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We need to understand what drives the hedge fund return
Pure alpha requires a lot of maintenance. It comes from manager’s skills:
technology and know how
„Agility” is the ability to invest in ways not open to traditional investors:
Beta Agility: derivatives, short-selling, no restrictions on consentration limits
and credit ratings of investments
Alpha Agility: complex trading rules (Model Risk, Momentum Risk), leverage
Market beta is not a good reason to invest in hedge funds
Pure alpha is desirable, not reliable and not replicable
Agility is desirable, reliable and difficult to replicate
Market beta is reliable, easily replicable, but not desirable
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Agenda
Why understanding hedge fund beta is important?
Differentiating between hedge fund beta and traditional investment beta
Are hedge funds just repacking beta and selling it as a pure alpha strategy?
Replicating the hedge fund returns synthetically
Why should investors pay for embedded beta?
Conclusion
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We need to translate the hedge funds returns into risk factors
Principal Component Analysis (PCA)
Dedicated Short
Managed Futures
GlobalMacro
Conv_ArbFI_Arb
Long ShortEquity
Emerging Markets
CSFB/T HFI
Event_D
Multi_Strat
Equity_MN
*See Appendix B for 6 principal components.Source: CSFB, Reuters, Thomson Financial, Siemens/fin4cast
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Let us have a close look at the returns of CSFB/Tremont Hedge Fund Index
Model with Beta Agility Beta T-Statistic Significance
Commodity Risk 0.3303 3.2349 0.0020Credit Risk -0.0355 -0.3157 0.7533Currency Risk -0.2056 -1.9300 0.0583Emerging Market Risk 0.4078 3.2941 0.0016Interest Rate Risk (short) -0.1566 -1.6451 0.1051Interest Rate Risk (long) 0.0284 0.2352 0.8148Liquidity Risk 0.2178 2.1539 0.0352Yield Curve Risk -0.1633 -1.6347 0.1073Market Risk -0.2591 -1.8066 0.0758Style Risk 0.1531 1.5825 0.1187Volatility Risk -0.2400 -1.6176 0.1109
R-squared 0.5041Adjusted R-squared 0.4146S.E. of regression 0.0077Correlation 0.7100Durbin-Watson stat 1.6143F-statistic 5.6365Prob(F-statistic) 0.0000
Model with Beta and Alpha Agility
Beta T-Statistic Significance
Commodity Risk 0.2211 2.3716 0.0210Credit Risk -0.0485 -0.4927 0.6241Currency Risk -0.2031 -2.1428 0.0363Emerging Market Risk 0.2320 2.0081 0.0492Interest Rate Risk (short) -0.1346 -1.6093 0.1129Interest Rate Risk (long) 0.0962 0.8881 0.3781Liquidity Risk 0.1451 1.5458 0.1275Yield Curve Risk -0.0926 -1.0405 0.3024Market Risk -0.1859 -1.4425 0.1545Style Risk 0.1102 1.2597 0.2128Volatility Risk -0.2800 -2.1415 0.0364Model Risk -0.0022 -0.0175 0.9861Momentum Risk 0.4136 3.4698 0.0010
R-squared 0.6316Adjusted R-squared 0.5505S.E. of regression 0.0067Correlation 0.7947Durbin-Watson stat 1.8638F-statistic 7.7817Prob(F-statistic) 0.0000
Model with Beta Agility, Alpha Agility and Pure Alpha
Beta T-Statistic Significance
Pure Alpha 0.0738 0.4701 0.6420Commodity Risk 0.2410 1.6106 0.1185Credit Risk 0.1043 0.6836 0.4998Currency Risk -0.2993 -1.8470 0.0753Emerging Market Risk 0.4532 2.0792 0.0469Interest Rate Risk (short) -0.0161 -0.1019 0.9196Interest Rate Risk (long) 0.0406 0.1930 0.8484Liquidity Risk 0.2482 1.6690 0.1063Yield Curve Risk -0.1555 -0.9538 0.3483Market Risk -0.0960 -0.4366 0.6658Style Risk 0.1668 1.0569 0.2996Volatility Risk -0.1787 -0.8711 0.3911Model Risk -0.1621 -0.9293 0.3607Momentum Risk -0.0733 -0.4289 0.6713R-squared 0.54277Adjusted R-squared 0.33048S.E. of regression 0.00611Correlation 0.73673Durbin-Watson stat 1.70051F-statistic 2.55677Prob(F-statistic) 0.01822
Beta agility and alpha agility seem to be significant sources of hedge fund return,
but is there any pure alpha?
Pure alpha tends to be less a matter of pure skill, but more a matter of flexibility
It is difficult to separate the alpha agility (flexibility) from the pure alpha (skill)
Source: CSFB, Reuters, Thomson Financial, Siemens/fin4cast
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Agenda
Why understanding hedge fund beta is important?
Differentiating between hedge fund beta and traditional investment beta
Are hedge funds just repacking beta and selling it as a pure alpha strategy?
Replicating the hedge fund returns synthetically
Why should investors pay for embedded beta
Conclusion
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Replicating the hedge fund returns synthetically
It was necessary to understand where the hedge fund returns
came from in the past
It is more important to understand what will drive the hedge fund
returns in the future
Building Autoregressive Models with exogenous Variables (ARX)
to predict the future returns of:
CSFB/Tremont Hedge Fund Index
CSFB/Tremont Equity Long/Short Index (see Appendix D)
CSFB/Tremont Emerging Market Index (see Appendix E)
CSFB/Tremont Global Macro Index (see Appendix F)
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Interest Rate Risk (US long)
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Forecasting the returns of the CSFB/Tremont Hedge Fund Index
Pure alpha
Momentum Risk
Market Risk
Interest Risk (EU long)
Commodity Risk
Emerging Market Risk
Credit Risk
Source: Siemens/fin4cast
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What sources of return are more likely to drive the performance of CSFB/Tremont Hedge Fund Index in the future?
Source: Siemens/fin4cast
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How the model did and what it expects for April 2006?
Forecast for April 2006 +0.73%Source: Siemens/fin4cast
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Agenda
Why understanding hedge fund beta is important?
Differentiating between hedge fund beta and traditional investment beta
Are hedge funds just repacking beta and selling it as a pure alpha strategy?
Replicating the hedge fund returns synthetically
Why should investors pay for embedded beta?
Conclusion
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Why should investors pay for embedded beta?
Embedded beta could be divided into market beta and beta agility.
Investors should be prepared to pay for pure alpha, alpha agility and beta
agility, but not for market beta.
Market beta is easy to replicate. Investors should buy it cheaper form the
traditional managers.
Although beta agility is replicable, few hedge fund managers are in a position to
replicate it.
Investors should be prepared to pay for alpha agility, which is difficult to
replicate.
Investors should pay for pure alpha, which comes from innovation, technology
and know how and is not replicable.
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Agenda
Why understanding hedge fund beta is important?
Differentiating between hedge fund beta and traditional investment beta
Are hedge funds just repacking beta and selling it as a pure alpha strategy?
Replicating the hedge fund returns synthetically
Why should investors pay for embedded beta?
Conclusion
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Summary: Why understanding hedge fund beta is important?
Traditional beta (single factor model) vs. hedge fund beta (multi factor model):
agility explains most of hedge fund returns
Pure alpha is desirable, not reliable and not replicable
Hedge Funds sell beta agility and alpha agility:
Both seem to be significant sources of hedge fund returns
Pure alpha tends to be less a matter of pure skill, but more a matter of flexibility
Deep understanding of past hedge funds performance is not enough. We need
a better grip on what will drive future hedge fund returns.
alpha agility: momentum and trading model
beta agility: emerging market factor, commodity factor, stock market factor
Investors should pay for pure alpha and be prepared to pay for agility
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Appendix A: Considered (Hidden) Risk Factors of Hedge Funds
Market Risk: S&P 500
Commodity Risk: Dow Jones AIG Commodity Index
Credit Risk: Spread (Yield M.L. US Corp BBB Bonds – Yield M.L. US Corp. AAA Bonds)
Emerging Market Risk: JPMorgan EMBI+ Composite
FX Risk: NYBOT US Dollar Index
Style Risk: Spread (S&P 500 – Russell 2000)
Interest Rate Risk: US Treasury Bill 90 day (short), Euro Bund (long EU), US T-Note 10y (long US)
Volatility Risk: VIX Index (implicit volatility of S&P 100 options)
Yield Curv Risk: Spred (US 30 year Treasury Bond – US Tresury Bill 90 day)
Liquidity Risk: NYSE Traded Volume
Model Risk: fin4cast Global Macro Diversified Futures Index I
Momentum Risk: Autoregressive Term
Source: Reuters, Thomson Financial, Siemens/fin4cast
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Appendix B: Principal Component Analyses
Source: CSFB, Reuters, Thomson Financial, Siemens/fin4cast
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Appendix C: Descriptive Statistics and Correlation Matrix
Descriptive StatisticsConvertible Arbitrage
CSFB Hedge Fund Index
Dedicated Short
Emerging Market
Equity Market Neutral
Event Driven
Fixed Income
Arbitrage
Global Macro
Long Short Equity
Managed Futures
Multi Strategy
Mean 0.78% 0.61% -0.16% 1.52% 0.48% 0.75% 0.45% 0.72% 0.31% 0.78% 0.70% Median 0.85% 0.61% -0.48% 1.98% 0.43% 0.82% 0.56% 0.50% 0.39% 0.45% 0.71% Maximum 3.40% 2.72% 10.89% 7.34% 2.39% 2.71% 2.97% 4.36% 10.31% 9.20% 4.86% Minimum -2.36% -1.09% -13.56% -5.06% -0.49% -1.76% -2.21% -1.77% -7.99% -8.71% -1.54% Std. Dev. 0.011 0.008 0.045 0.026 0.005 0.009 0.010 0.012 0.029 0.038 0.010 Skewness -0.14 0.24 -0.10 -0.31 1.05 -0.25 -0.41 0.47 0.40 -0.20 0.79 Kurtosis 3.07 2.91 3.44 2.75 4.63 3.45 3.42 3.10 5.81 2.77 5.72Autocorrelation 1st Order 0.55 0.10 0.13 0.07 0.18 0.26 0.19 0.20 0.00 0.10 0.24 Jarque-Bera 0.2354 0.7093 0.6950 1.3361 21.2498 1.3784 25.7536 2.6895 25.5599 0.6553 29.7101 Probability 0.8889 0.7014 0.7065 0.5127 0.0000 0.5020 0.2759 0.2606 0.0000 0.7206 0.0000 Observations 72 72 72 72 72 72 72 72 72 72 72
Correlation MatrixConvertible Arbitrage
CSFB Hedge Fund Index
Dedicated Short
Emerging Market
Equity Market Neutral
Event Driven
Fixed Income
Arbitrage
Global Macro
Long Short Equity
Managed Futures
Multi Strategy
Convertible Arbitrage 1.00CSFB Hedge Fund Index 0.41 1.00Dedicated Short -0.22 -0.45 1.00Emerging Market 0.20 0.51 -0.60 1.00Equity Market Neutral 0.31 -0.05 -0.10 0.00 1.00Event Driven 0.48 0.59 -0.59 0.61 0.02 1.00Fixed Income Arbitrage 0.39 0.25 -0.03 0.12 0.13 0.35 1.00Global Macro 0.27 0.39 0.17 -0.06 -0.04 0.09 0.17 1.00Long Short Equity -0.02 0.68 -0.64 0.52 -0.18 0.37 0.00 -0.07 1.00Managed Futures 0.05 0.47 0.14 -0.14 -0.08 -0.05 -0.21 0.38 -0.03 1.00Multi Strategy 0.60 0.61 -0.52 0.54 0.15 0.64 0.42 0.06 0.40 -0.08 1.00Period: 31 January 2000 - 31 January 2006
Source: CSFB/Tremont, Siemens/fin4cast
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Appendix D: Forecasting Model for CSFB/Tremont Long Short Equity Index
Yield Curve Risk
Pure alpha
Momentum Risk
Market Risk
Interest Risk (EU long)
Commodity Risk
Emerging Market Risk
Credit Risk
Source: Siemens/fin4cast
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Appendix D: What sources of return are more likely to drive the performance of CSFB/Tremont Equity Long/Short Index in the future?
Source: Siemens/fin4cast
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Appendix D: How the model did and what it expects for April 2006?
Forecast for April 2006 +0.61%Source: Siemens/fin4cast
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Appendix E: Forecasting Model for CSFB/Tremont Emerging Market Index
Yield Curve Risk
Pure alpha
Momentum Risk
Market Risk
Interest Risk (EU long)
Commodity Risk
Emerging Market Risk
Credit Risk
Source: Siemens/fin4cast
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Appendix D: What sources of return are more likely to drive the performance of CSFB/Tremont Emerging Market Index in the future?
Source: Siemens/fin4cast
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Appendix D: How the model did and what it expects for April 2006?
Forecast for April 2006 -0.63%
Source: Siemens/fin4cast
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Appendix D: Forecasting Model for CSFB/Tremont Global Macro Index
Yield Curve Risk
Pure alpha
Momentum Risk
Market Risk
Interest Risk (EU long)
Commodity Risk
Emerging Market Risk
Credit Risk
Source: Siemens/fin4cast
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Appendix D: ARX Model for CSFB/Tremont Global Macro Index
Source: Siemens/fin4cast
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Appendix D: How the model did and what it expects for April 2006?
Source: Siemens/fin4cast
Forecast for April 2006 -0.02%
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Biographies
Dr Miroslav Mitev is a managing director and head of quantitative research and strategy development at Siemens/fin4cast. Dr Mitev is responsible for the development of innovative, systematic long-short investment strategies for institutional investors world wide based on Siemens/fin4cast technology. After joining Siemens in 2001 Dr Mitev successfully formed a qualified team of 25 professionals which is continuously developing the Siemens/fin4cast Technology and building mathematical forecasting models for a variety of financial instruments like currency futures, commodity futures, stock index futures, bond futures, single stocks and hedge fund indices. Dr Mitev is in charge of the Siemens/fin4cast’s research cooperation with various universities and is actively involved in the scientific management of numerous master thesis and dissertations. Dr Mitev is a regular speaker at international conventions on liability driven investing, asset management, hedge funds, portable alpha, advanced quantitative studies, algo-trading and system research. Dr Mitev’s research is published on a regular basis in international journals and presented on international scientific conferences. Prior to joining Siemens Dr Mitev was at CA IB, the Investment Bank of Bank Austria Group, where he was in charge of the quantitative research of the securities research division. Dr Mitev received a Master of Economics and Business Administration with main focus on Investment Banking and Capital Markets. Dr Mitev also received a PhD in Economics with main focus on Finance and Econometrics.
Dr. Miroslav MitevSiemens AG ÖsterreichSiemens IT Solutions and Services PSE/fin4castPhone: +43 (0) 51707 46253Fax: +43 (0) 51707 56465Mobile: +43 (0) 676 9050903Email: [email protected]