From Replication to Forecasting – Creating a new and active hedge fund benchmark 1) The document discusses replicating hedge fund returns through dynamic long/short trading of liquid futures using mechanical rule-based and multi-factor modeling approaches. 2) It presents fin4cast's two-stage integrated multi-factor approach to replicating the HFR Hedge Fund Index returns. 3) The authors propose going beyond replication to creating a new active hedge fund benchmark through combining return replication models with return forecasting models.

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From Replication to Forecasting –
Creating a new and active hedge fund benchmark
Hedge Fund Replication & Alternative Beta
28th – 29th November 2007
Ritz-Carlton Hotel, Hong Kong
December 07
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Disclaimer
From Hedge
Fund Replication This presentation and the analysis herein contains proprietary information and is not to be copied, reproduced, used, or divulged to any
to Hedge Fund 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
Forecasting 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
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BY ACCEPTING THIS DOCUMENT YOU ACKNOWLEDGE THAT ALL OF THE INFORMATION HEREIN SHALL BE KEPT STRICTLY
CONFIDENTIAL BY YOU.
The views and opinions expressed in this presentation are those of the authors only, and do not necessarily represent the views and
opinions of Siemens AG, or any of its employees. The authors make no representations or warranty, either expressed or implied, as to
the accuracy or completeness of the information contained in this presentation, nor are they recommending that this presentation
serves as the basis for any investment decision. This presentation is prepared for the Hedge Fund Replication & Alternative Beta 2007,
27th November – 29th November 2007, Ritz-Carlton, Hong Kong only. Research support from Fin4Cast is gratefully acknowledged.
Prof. Georg Dorfleitner*, Maria Crepaz**, Klaus Gams**, Dr. Martin Kuehrer** and Dr. Miroslav Mitev**
* Professor of Finance, Department of Finance, University of Regensburg, Germany.
** Siemens AG Österreich, Siemens IT Solutions and Services, Program and System Engineering, Fin4Cast, Gudrunstrasse 11, 1100
Vienna, Austria, Phone: +43 (0) 517 07 46360, Fax: +43 (0) 517 07 56256, email: info@fin4cast.com.
The corresponding paper “From Replication to Forecasting – Creating a new and active hedge fund benchmark” is available upon request.
December 07
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Dr. Miroslav Mitev

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From Replication to Forecasting – Creating a new and active
From Hedge
hedge fund benchmark
Fund Replication
to Hedge Fund
Forecasting
Replication of hedge fund returns – does it really work?
The magic behind – how to replicate?
Limits of hedge fund replication – good to know.
Synthetic replication – presenting the results
From indexation to replication – what‘s next? Creating a new
and active hedge fund benchmark
Conclusion & research outlook
December 07
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Dr. Miroslav Mitev

4. s
Replication of hedge fund returns – does it really work?
From Hedge
Fund Replication
to Hedge Fund
Forecasting
The replication of hedge fund returns aims:
to deliver similar month-to-month returns to a particular hedge
fund style
to replicate the statistical properties of a particular hedge fund
index
to separate the hedge fund alpha of a particular hedge fund style
from the traditional and the alternative beta
to lower cost and provide greater transparency and liquidity
to provide benchmarks for investments in hedge funds
to provide liquid underlings for structured products
to eliminate single-manager risk and style drift
to provide access for a larger number of investors
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Dr. Miroslav Mitev

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Replication of hedge fund returns – does it really work?
From Hedge
Fund Replication
Strong evidence from the recent academic research that a large portion of hedge
to Hedge Fund
fund returns can be synthetically replicated through a dynamic long/short portfolio of
Forecasting
tradable liquid futures:
Mechanical rule-based trading
(Fung and Hsieh, 1997) use look-back straddles to replicate a trend following strategy mechanically
Products: Merrill Lynch Equity Volatility Arbitrage Index, Merrill Lynch FX Clone, Deutsche Bank Currency Return
Index, and Bear Stearns “Mast” (Fixed Income) Index
Multi-factor modeling
(Schneeweis et al, 2003) introduce futures and options as observable factors and replicate the return process of
various hedge fund strategies
(Jaeger and Wagner, 2005) estimate factor models to model the underlying hedge fund risk premiums using a
broad set of risk factors and (non-linear) rule-based strategies
(Hasandhodzic and Lo, 2006) estimate linear factor models to replicate individual hedge funds using six
common factors corresponding to liquid exchange traded instruments
(Fung et al, 2006) estimate a seven-factor model for fund of funds using one traditional and six alternative
factors
(Gams, Kuehrer and Mitev, 2007) introduce an integrated and dynamic two stage multi-factor approach to
replicate the month-to-month returns of HFR Hedge Fund Index.
Products: Goldman’s Absolute Return Tracker index (GS-ART), Merrill Lynch Factor Index, JPMorgan Alternative
Beta Index (ABI), Deutsche Bank Absolute Return Beta Index. Partners Group’s Alternative Beta
Copula-based algorithm
(Kat and Palaro, 2006) use a copula-based approach to design trading strategies that generate returns with
predefined statistical properties similar to those of hedge funds or hedge fund indices
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Dr. Miroslav Mitev

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Replication of hedge fund returns – does it really work?
From Hedge
Fund Replication
REPLICATION
to Hedge Fund HEDGE FUNDS DIRECT INVESTMENT FEASABILITY
APPROACHES
Forecasting
• Absolute Returns • Capital • Funds of Funds • Hedge Fund
Requirements Returns are
• Non-Directional • Mechanical rule- Available
Returns • Long Holding based Trading
Periods • Opportunities on
• Diversification • Multi - Factor International
Benefits • Management and Modeling Markets
Incentive Fees
• Distinctive Risk • Copula Approach • Determine Return
Profile • Legal Requirements Driving Factors
• Transparency of • “Reverse
Risks involved Engineering”
• Disclosed Hedge
Funds
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Dr. Miroslav Mitev

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The magic behind – how to replicate?
fin4cast two stages integrated multi-factor Hedge Fund Replication Approach
From Hedge
Fund Replication
Liquid Futures Factor Pre-Selection Replication Methodology
to Hedge Fund
Forecasting Sub-pool 1
• RWOLS (Restricted and Weighted
54 Factors
• Reuters • 45 Traditional Factors and
Ordinary Least Squared)
• Thomson 3 Spreads
• RWLAD (Restricted and Weighted
Financial 13 Commodities
Least Absolute Deviation)
• Bloomberg 9 Stock Indices Sub-pool 2
1 vola Index 27 Factors
6 Bond Indices
11 Currencies
5 Money Markets
Sub-pool 3
• 6 Alternative Factors Factor Selection - Search
22 Factors
Mechanical Trading Algorithms (Best Descriptive
Rules (MTRs) Models)
• Heuristic Search Algorithm
Dynamic Selection of the best Replication Strategies
• Greedy Forward Search
• Cross validation
• Average Strategy
• Fast Stepwise Local Search
• R-squared Selection Strategy
• Tracking Error Selection Strategy
• Absolute Deviations Selection Strategy
Dynamic Portfolio Construction
Measuring the Results
• Multimodel Inference –
• Statistical Properties Weighted Average
• Replication Accuracy
Approach
December 07 • Stable Portfolio Development
• Dynamic Leverage Factor
• Distribution Features
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Dr. Miroslav Mitev

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Limits of hedge fund replication – good to know
From Hedge
Fund Replication
Replication of hedge fund indices (average return of hedge funds), but NOT
to Hedge Fund
Forecasting a single hedge fund
Quality of replication vary considerably among different hedge fund styles,
i.e. Global Macro, Long/Short Equity or Market Neutral
Replication results very among different providers of hedge fund indices,
i.e. HFR or CS/Tremont
Quality of replication suffers from:
the lack of liquid instruments to replicate specific risk premia, i.e. emerging market
and M&A
the time lag to adjust the model’s coefficients with respect to “external shocks” and
regime switches
the time lag of the data availability, i.e. 15th of each month
the low frequency of the available data, i.e. monthy returns
the short history, i.e. just 167 data points since January 1994 for CS/Tremont Hedge
Fund Composite Index
December 07
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Dr. Miroslav Mitev

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Synthetic replication – presenting the results
From Hedge
Fund Replication
to Hedge Fund
Forecasting
December 07
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A negative compound alpha -1.71% for the observed period!
Dr. Miroslav Mitev

10. s
Synthetic replication – presenting the results
From Hedge
Fund Replication
to Hedge Fund
Forecasting
December 07
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Dr. Miroslav Mitev

11. s
Synthetic Replication – presenting the results
From Hedge
Fund Replication
to Hedge Fund
Forecasting
December 07
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Dr. Miroslav Mitev

12. s
From indexation to replication – what‘s next? Creating a
From Hedge
new and active hedge fund benchmark!
Fund Replication
to Hedge Fund
Forecasting
Building of forecast models to predict the direction of the
monthly returns of the CS/Tremont Composite
Combining the results of the forecast models with the results of
the replication models by adjusting the model’s coefficients:
if the return forecast is positive the coefficients stay the same as
for the replication model
if the return forecast is negative the coefficients are multiplied
by -1
The objective is:
☺ to create a new and active hedge fund benchmark
☺ to out-perform the average of the hedge funds by generating
positive returns during periods of negative returns of
CS/Tremont Composite
December 07
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Dr. Miroslav Mitev

13. s
From indexation to replication – what‘s next? Creating a
From Hedge
new and active hedge fund benchmark!
Fund Replication
to Hedge Fund
Forecasting
December 07
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Dr. Miroslav Mitev

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From indexation to replication – what‘s next? Creating a
From Hedge
new and active hedge fund benchmark!
Fund Replication
to Hedge Fund
Forecasting
☺ The new active hedge fund benchmark out-performed the CS/Tremont
Composite Index by 13.34% during the observed period!
December 07
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Dr. Miroslav Mitev

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Conclusion
From Hedge
Fund Replication Does replication of hedge funds returns really work?
to Hedge Fund
Strong evidence from the recent academic research supports the motion
Forecasting
Replication approaches – mechanical rule-based, multi-factor and copula
How to replicate?
Replication of CS/Tremont Hedge Fund Composite Index using fin4cast two stage multi-factor
integrated Hedge Fund Replication Approach
Good to know:
Replication works for average hedge fund returns, but not for a single hedge funds
Replication quality varies among differnt hedge fund styles and index providers
Replication lags behind due to time lag of the data availability and adjustment of the model‘s
coefficients
What are the results?
☺ Our results give strong evidence that the synthetic hedge fund portfolio is able to replicate the
statistical properties of the monthly returns of the CS/Tremont Hedge Fund Index with respect to
the month-to-month return and the standard deviation
☺ Our findings show that the compound alpha of the CS/Tremont Index compared to the cost and
interest rate adjusted returns of the synthetic portfolio is negative
New idea about an active hedge fund benchmark was born:
December 07 Combination of return replication and return forecast!
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Dr. Miroslav Mitev

16. s
Research outlook
From Hedge
Fund Replication
What’s next?
to Hedge Fund
Forecasting
Extensive research for the creation of new and active hedge fund
benchmarks for different hedge fund styles
Building of qualitative mathematical forecast models for different
hedge fund indices
Intensive live-testing of new and active hedge fund benchmarks
December 07
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Dr. Miroslav Mitev

17. s
Universe of liquid futures used for the replication
From Hedge
Fund Replication
to Hedge Fund
Forecasting
December 07
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Dr. Miroslav Mitev

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References
From Hedge
Fund Replication Ackermann, C., McEnally, R. and Ravenscraft, D. (1998); The performance of hedge funds: risk, return and incentives
to Hedge Fund
An, H. and Gu, L. (1985); On the selection of regression variables; Acta Mathematicae Applicatae Sinica, Vol. 2, No. 1 (pp. 27-36)
Forecasting
An, H. and Gu, L. (1989); Fast stepwise procedures of selection of variables by using AIC and BIC criteria; Acta Mathematicae Applicatae Sinica, Vol. 5,
No. 1 (pp. 60-67)
Burnham, K. and Anderson, D.R. (1998); Model selection and inference: a practical information-theoretic approach, Springer Verlag
Crepaz, M., (2007): Replication of Hedge Fund Returns, Diploma Thesis, Vienna University of Economics and Business Administration.
Dorfleitner, G., (2003): Why the return notion matters. International Journal of Theoretical and Applied Finance, Vol. 6, No.1, pp. 73-86, 2003
Fung, W. and Hsieh D. (1997); Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds; The Review of Financial Studies, No.
2, (pp. 275-302)
Fung, W. and Hsieh D. (1999); A Primer on Hedge Funds, Journal of Empirical Finance, 6, (pp. 309-331)
Fung, W. and Hsieh, D. (2004); Hedge Fund Benchmarks: A Risk Based Approach. Financial Analyst Journal
Fung, W., Hsieh D., Naik, N. and Ramadorai, T. (2006): Hedge Funds: Performance, Risk and Capital Formation.
Gams, K., Kuehrer, M. and Mitev, M. (2006); Hedge Fund Replication using Fin4Cast Technology, Siemens Fin4Cast Working Paper
Gams, K., Kuehrer M. and Mitev, M. (2007): Synthetic Replicaton, The Hedgefund Journal, October 2007
Hasanhodzic, Jasmina and Lo, Andrew W. (2006): Can Hedge-Fund Returns Be Replicated? The Linear Case.
Jaeger, Lars and Wagner, Christian (2005): Factor Modeling and Benchmarking of Hedge Funds: Can passive investments in hedge fund strategies
deliver?, Journal of Alternative Investments.
Kat, H. and H. Palaro (2005); Who Needs Hedge Funds? A Copula-Based Approach to Hedge Fund Return Replication, Working Paper 27, Alternative
Investment Research Centre, Cass Business School
Kuehrer, M. and Mitev, M. (2007); Forecasting the future return of the oil price, The Hedgefund Journal, May 2007
December 07
Schneeweis Thomas, Kazemi Hossein and Karavas Vassilis (2003): Eurex Derivative Products in Alternative Investments: The Case for Hedge Funds.
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Dr. Miroslav Mitev

19. s
Biography
From Hedge
Fund Replication
Dr. Miroslav Mitev
to Hedge Fund Siemens AG Österreich
Forecasting Siemens IT Solutions and Services PSE/fin4cast
Phone: +43 (0) 51707 46253
Fax: +43 (0) 51707 56465
Mobile: +43 (0) 676 9050903
Email: miroslav.mitev@siemens.com
Dr Miroslav Mitev is a managing director and head of quantitative securities research and portfolio management. 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.
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Dr. Miroslav Mitev