IDEAS home Printed from https://ideas.repec.org/p/wpa/wuwpfi/0503010.html
   My bibliography  Save this paper

Measuring Loss Potential of Hedge Fund Strategies

Author

Listed:
  • Marcos Mailoc López de Prado

    (UBS)

  • Achim Peijan

    (UBS)

Abstract

We measure the loss potential of Hedge Funds by combining three market risk measures: VaR, Draw-Down and Time Under-The-Water. Calculations are carried out considering three different frameworks regarding Hedge Fund returns: i) Normality and time-independence, ii) Non-normality and time- independence and iii) Non-normality and time-dependence. In the case of Hedge Funds, our results clearly state that market risk may be substantially underestimated by those models which assume Normality or, even considering Non-Normality, neglect to model time- dependence. Moreover, VaR is an incomplete measure of market risk whenever the Normality assumption does not hold. In this case, VaR results must be compared with Draw-Down and Time Under-The-Water measures in order to accurately assess about Hedge Funds loss potential.

Suggested Citation

  • Marcos Mailoc López de Prado & Achim Peijan, 2005. "Measuring Loss Potential of Hedge Fund Strategies," Finance 0503010, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpfi:0503010
    Note: Type of Document - pdf; pages: 25. Journal of Alternative Investments, Vol. 7, No. 1, pp. 7-31, Summer 2004
    as

    Download full text from publisher

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/fin/papers/0503/0503010.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamilton, James D, 1991. "A Quasi-Bayesian Approach to Estimating Parameters for Mixtures of Normal Distributions," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(1), pages 27-39, January.
    2. Gaurav Amin & Harry. M Kat, 2002. "Generalization of the Sharpe Ratio and the Arbitrage-Free Pricing of Higher Moments," ICMA Centre Discussion Papers in Finance icma-dp2002-15, Henley Business School, University of Reading.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sevinc Cukurova & Jose M. Marin, 2011. "On the economics of hedge fund drawdown status: Performance, insurance selling and darwinian selection," Working Papers 2011-04, Instituto Madrileño de Estudios Avanzados (IMDEA) Ciencias Sociales.
    2. Zabarankin, Michael & Pavlikov, Konstantin & Uryasev, Stan, 2014. "Capital Asset Pricing Model (CAPM) with drawdown measure," European Journal of Operational Research, Elsevier, vol. 234(2), pages 508-517.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Engel, Charles, 1994. "Can the Markov switching model forecast exchange rates?," Journal of International Economics, Elsevier, vol. 36(1-2), pages 151-165, February.
    2. Cruz-Rodríguez, Alexis, 2005. "Ciclos Económicos Sincronizados y Uniones Monetarias en Centroamérica y la República Dominicana [Business Cycles Synchronisation and Monetary Union in Central American and the Dominican Republic]," MPRA Paper 72104, University Library of Munich, Germany.
    3. RUGE-MURCIA, Francisco J., 2010. "Estimating Nonlinear DSGE Models by the Simulated Method of Moments," Cahiers de recherche 2010-10, Universite de Montreal, Departement de sciences economiques.
    4. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    5. Hamilton, James D., 1996. "Specification testing in Markov-switching time-series models," Journal of Econometrics, Elsevier, vol. 70(1), pages 127-157, January.
    6. Su, EnDer, 2013. "Stock index hedge using trend and volatility regime switch model considering hedging cost," MPRA Paper 49190, University Library of Munich, Germany.
    7. RenÈ Garcia, 2002. "Are the Effects of Monetary Policy Asymmetric?," Economic Inquiry, Western Economic Association International, vol. 40(1), pages 102-119, January.
    8. AKA, Bédia F., 2009. "Business Cycle And Sectoral Fluctuations: A Nonlinear Model For Côte D’Ivoire," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 9(1), pages 111-126.
    9. Psaradakis, Zacharias & Sola, Martin, 1998. "Finite-sample properties of the maximum likelihood estimator in autoregressive models with Markov switching," Journal of Econometrics, Elsevier, vol. 86(2), pages 369-386, June.
    10. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2019. "Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns," Journal of Econometrics, Elsevier, vol. 213(2), pages 493-515.
    11. Alexis Cruz Rodriguez, 2011. "Prediction of Currency Crises Using a Fiscal Sustainability Indicator," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 26(2), pages 39-60, December.
    12. Ghysels, E., 1993. "A Time Series Model with Periodic Stochastic Regime Switching," Cahiers de recherche 9314, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    13. Behiye Cavusoglu & Saifullahi Sani Ibrahim & Huseyin Ozdeser, 2019. "Testing the relationship between financial sector output, employment and economic growth in North Cyprus," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-11, December.
    14. Dufrénot, Gilles & Mignon, Valérie & Péguin-Feissolle, Anne, 2011. "The effects of the subprime crisis on the Latin American financial markets: An empirical assessment," Economic Modelling, Elsevier, vol. 28(5), pages 2342-2357, September.
    15. Christian Johnson, 2001. "Un Modelo de Switching para el Crecimiento en Chile," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 38(115), pages 291-319.
    16. Layton, Allan P., 1996. "Dating and predicting phase changes in the U.S. business cycle," International Journal of Forecasting, Elsevier, vol. 12(3), pages 417-428, September.
    17. Arora, Vipin & Gomis-Porqueras, Pedro & Shi, Shuping, 2013. "The divergence between core and headline inflation: Implications for consumers’ inflation expectations," Journal of Macroeconomics, Elsevier, vol. 38(PB), pages 497-504.
    18. Massimo Guidolin & Stuart Hyde, 2008. "Equity portfolio diversification under time-varying predictability and comovements: evidence from Ireland, the US, and the UK," Working Papers 2008-005, Federal Reserve Bank of St. Louis.
    19. Trino-Manuel Niguez & Javier Perote, 2004. "Forecasting the density of asset returns," STICERD - Econometrics Paper Series 479, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    20. Allan Layton, 1997. "A new approach to dating and predicting Australian business cycle phase changes," Applied Economics, Taylor & Francis Journals, vol. 29(7), pages 861-868.

    More about this item

    Keywords

    Hedge Fund; Value-at-Risk; risk; performance; drawdown; under- the-water; normal returns; non-normal returns; time-dependence; ARMA; Monte Carlo; skewness; kurtosis; mixture of gaussian distributions; survival probability; styles; investment strategies;
    All these keywords.

    JEL classification:

    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpfi:0503010. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: EconWPA (email available below). General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.