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Drawdown risk measures for asset portfolios with high frequency data

Author

Listed:
  • Giovanni Masala

    (Università degli Studi di Cagliari)

  • Filippo Petroni

    (Università Politecnica delle Marche)

Abstract

In this paper, we analyze Drawdown-based risk measures for an equity portfolio with high-frequency data. The returns of individual stocks are modeled through multivariate weighted-indexed semi-Markov chains with a copula dependence structure. Through this recently published model, we show that the estimate of Drawdown-based risk measures is more faithful than that obtained with the application of classic econometric models.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:annfin:v:19:y:2023:i:2:d:10.1007_s10436-022-00421-y
    DOI: 10.1007/s10436-022-00421-y
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    References listed on IDEAS

    as
    1. Guglielmo D'Amico & Filippo Petroni, 2012. "Weighted-indexed semi-Markov models for modeling financial returns," Papers 1205.2551, arXiv.org, revised Jun 2012.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Stanislaus Maier-Paape & Qiji Jim Zhu, 2018. "A General Framework for Portfolio Theory. Part II: Drawdown Risk Measures," Risks, MDPI, vol. 6(3), pages 1-31, August.
    4. Guglielmo D'Amico & Filippo Petroni, 2011. "A semi-Markov model with memory for price changes," Papers 1109.4259, arXiv.org, revised Dec 2011.
    5. Pospisil, Libor & Vecer, Jan & Hadjiliadis, Olympia, 2009. "Formulas for stopped diffusion processes with stopping times based on drawdowns and drawups," Stochastic Processes and their Applications, Elsevier, vol. 119(8), pages 2563-2578, August.
    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. Gatfaoui, Hayette, 2019. "Diversifying portfolios of U.S. stocks with crude oil and natural gas: A regime-dependent optimization with several risk measures," Energy Economics, Elsevier, vol. 80(C), pages 132-152.
    8. Torben G. Andersen & Tim Bollerslev & Per Frederiksen & Morten Ørregaard Nielsen, 2010. "Continuous-time models, realized volatilities, and testable distributional implications for daily stock returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 233-261.
    9. Martin Burda & Louis Belisle, 2019. "Copula Multivariate GARCH Model with Constrained Hamiltonian Monte Carlo," Working Papers tecipa-638, University of Toronto, Department of Economics.
    10. Hongzhong Zhang & Olympia Hadjiliadis, 2012. "Drawdowns and the Speed of Market Crash," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 739-752, September.
    11. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    12. Charwand, Mansour & Gitizadeh, Mohsen & Siano, Pierluigi, 2017. "A new active portfolio risk management for an electricity retailer based on a drawdown risk preference," Energy, Elsevier, vol. 118(C), pages 387-398.
    13. 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.
    14. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    Keywords

    Drawdown risk measure; Weighted-indexed semi-Markov models; Asset portfolio; High-frequency data; Right censoring; GARCH models;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General

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