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Hedge Fund Replication From Replication To Forecasting
 

Hedge Fund Replication From Replication To Forecasting

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Case Study Presentation at the Hedge Fund Replication and Alternative Beta conference on 27th – 29th November 2007 Ritz-Carlton Hotel, Hong Kong, China

Case Study Presentation at the Hedge Fund Replication and Alternative Beta conference on 27th – 29th November 2007 Ritz-Carlton Hotel, Hong Kong, China

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    Hedge Fund Replication From Replication To Forecasting Hedge Fund Replication From Replication To Forecasting Presentation Transcript

    • s 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 1
    • s 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 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. 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 2 Dr. Miroslav Mitev
    • s 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 3 Dr. Miroslav Mitev
    • 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 December 07 4 Dr. Miroslav Mitev
    • s 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 December 07 5 Dr. Miroslav Mitev
    • s 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 December 07 6 Dr. Miroslav Mitev
    • s 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 7 Dr. Miroslav Mitev
    • s 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 8 Dr. Miroslav Mitev
    • s Synthetic replication – presenting the results From Hedge Fund Replication to Hedge Fund Forecasting December 07 9 A negative compound alpha -1.71% for the observed period! Dr. Miroslav Mitev
    • s Synthetic replication – presenting the results From Hedge Fund Replication to Hedge Fund Forecasting December 07 10 Dr. Miroslav Mitev
    • s Synthetic Replication – presenting the results From Hedge Fund Replication to Hedge Fund Forecasting December 07 11 Dr. Miroslav Mitev
    • 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 12 Dr. Miroslav Mitev
    • 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 13 Dr. Miroslav Mitev
    • s 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 14 Dr. Miroslav Mitev
    • s 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! 15 Dr. Miroslav Mitev
    • 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 16 Dr. Miroslav Mitev
    • s Universe of liquid futures used for the replication From Hedge Fund Replication to Hedge Fund Forecasting December 07 17 Dr. Miroslav Mitev
    • s 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. 18 Dr. Miroslav Mitev
    • 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. December 07 19 Dr. Miroslav Mitev