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Implementing and testing the Maximum Drawdown at Risk

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  • Mendes, Beatriz Vaz de Melo
  • Lavrado, Rafael Coelho

Abstract

Financial managers are mainly concerned about long lasting accumulated large losses which may lead to massive money withdrawals. To assess this risk feeling we compute the Maximum Drawdown, the largest price loss of an investment during some fixed time period. The Maximum Drawdown at Risk has become an important risk measure for commodity trading advisors, hedge funds managers, and regulators. In this study we propose an estimation methodology based on Monte Carlo simulations and empirically validate the procedure using international stock indices. We find that this tool provides more accurate market risk control and may be used to manage portfolio exposure, being useful to practitioners and financial analysts.

Suggested Citation

  • Mendes, Beatriz Vaz de Melo & Lavrado, Rafael Coelho, 2017. "Implementing and testing the Maximum Drawdown at Risk," Finance Research Letters, Elsevier, vol. 22(C), pages 95-100.
    • Handle: RePEc:eee:finlet:v:22:y:2017:i:c:p:95-100
    DOI: 10.1016/j.frl.2017.06.001
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    References listed on IDEAS

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    1. Ioannis Karatzas & Jaksa Cvitanic, 1999. "On dynamic measures of risk," Finance and Stochastics, Springer, vol. 3(4), pages 451-482.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Libor Pospisil & Jan Vecer, 2010. "Portfolio sensitivity to changes in the maximum and the maximum drawdown," Quantitative Finance, Taylor & Francis Journals, vol. 10(6), pages 617-627.
    4. Riccardo Rebonato & Valerio Gaspari, 2006. "Analysis of drawdowns and drawups in the US$ interest-rate market," Quantitative Finance, Taylor & Francis Journals, vol. 6(4), pages 297-326.
    5. Foort Hamelink & Martin Hoesli, 2004. "Maximum drawdown and the allocation to real estate," Journal of Property Research, Taylor & Francis Journals, vol. 21(1), pages 5-29, January.
    6. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Sanford J. Grossman & Zhongquan Zhou, 1993. "Optimal Investment Strategies For Controlling Drawdowns," Mathematical Finance, Wiley Blackwell, vol. 3(3), pages 241-276, July.
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    Cited by:

    1. Giovanni Masala & Filippo Petroni, 2023. "Drawdown risk measures for asset portfolios with high frequency data," Annals of Finance, Springer, vol. 19(2), pages 265-289, June.
    2. Ashraf, Dawood & Rizwan, Muhammad Suhail & Ahmad, Ghufran, 2022. "Islamic equity investments and the COVID-19 pandemic," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    3. Seyed Mehrzad Asaad Sajadi & Pouya Khodaee & Ehsan Hajizadeh & Sabri Farhadi & Sohaib Dastgoshade & Bo Du, 2022. "Deep Learning-Based Methods for Forecasting Brent Crude Oil Return Considering COVID-19 Pandemic Effect," Energies, MDPI, vol. 15(21), pages 1-23, October.
    4. Md Iftekhar Hasan Chowdhury & Faruk Balli & Anne de Bruin, 2022. "Islamic equity markets versus their conventional counterparts in the COVID‐19 age: Reaction, resilience, and recovery," International Review of Finance, International Review of Finance Ltd., vol. 22(2), pages 315-324, June.
    5. Drenovak, Mikica & Ranković, Vladimir & Urošević, Branko & Jelic, Ranko, 2022. "Mean-Maximum Drawdown Optimization of Buy-and-Hold Portfolios Using a Multi-objective Evolutionary Algorithm," Finance Research Letters, Elsevier, vol. 46(PA).
    6. Dorfleitner, Gregor & Fischer, Lukas & Lung, Carina & Willmertinger, Philipp & Stang, Nico & Dietrich, Natalie, 2018. "To follow or not to follow – An empirical analysis of the returns of actors on social trading platforms," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 160-171.
    7. Hassan, M. Kabir & Chowdhury, Md Iftekhar Hasan & Balli, Faruk & Hasan, Rashedul, 2022. "A note on COVID-19 instigated maximum drawdown in Islamic markets versus conventional counterparts," Finance Research Letters, Elsevier, vol. 46(PB).

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    More about this item

    Keywords

    Risk management; Maximum drawdown; ARMA-GARCH; Simulations;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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