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Volatility versus downside risk: performance protection in dynamic portfolio strategies

Author

Listed:
  • Diana Barro

    (University Ca’ Foscari of Venice)

  • Elio Canestrelli

    (University Ca’ Foscari of Venice)

  • Giorgio Consigli

    (University of Bergamo)

Abstract

Volatility-based and volatility targeting approaches have become popular among equity fund managers after the introduction in 1993 of the VIX, the implied volatility index on the S&P500 at the Chicago Board of Exchange (CBOE), followed, in 2004, by futures and option contracts on the VIX: since then we have assisted to an increasing interest in risk control strategies based on market signals. In January 2000 also the FTSE implied volatility index (FTSEIVI) was introduced at the London Stock Exchange. As a result, specifically in the US, portfolio strategies based on combinations of market indices and derivatives have been proposed by Stock Exchanges and investment banks: one such example is the S&P500 protective put index (PPUT). Early in 2016, relevant to the definition of optimal bond-equity strategies, CBOE launched an Index called TYVIX/VIX featuring an investment rotation strategy based jointly on signals coming from the VIX and the 10-year Treasury Yield implied volatility (TYVIX). All these are rule-based portfolio strategies in which no optimization methods are involved. While rather effective in reducing the downside risk, those index-based portfolio approaches do not allow an optimal risk-reward trade-off and may not be sufficient to control financial risk originated by extreme market drops. To overcome these limits we propose an optimization-based approach to portfolio management jointly focusing on volatility and tail risk controls and able to accommodate effectively the return payoffs associated with option strategies, whose cost as market volatility increases may become excessive. The model is based on a mean absolute deviation formulation and tested in the US equity market over the 2000–2016 period and with a focus on three periods of high volatility, in 2000, 2001 and 2008. The results confirm that optimal volatility controls produce better risk-adjusted returns if compared with rule-based approaches. Moreover the portfolio return distribution is dynamically shaped depending on the adopted risk management approach.

Suggested Citation

  • Diana Barro & Elio Canestrelli & Giorgio Consigli, 2019. "Volatility versus downside risk: performance protection in dynamic portfolio strategies," Computational Management Science, Springer, vol. 16(3), pages 433-479, July.
    • Handle: RePEc:spr:comgts:v:16:y:2019:i:3:d:10.1007_s10287-018-0310-4
    DOI: 10.1007/s10287-018-0310-4
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    References listed on IDEAS

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    1. Ron Dembo & Dan Rosen, 1999. "The practice of portfolio replication. A practical overview of forward and inverse problems," Annals of Operations Research, Springer, vol. 85(0), pages 267-284, January.
    2. Giorgio Consigli & Daniel Kuhn & Paolo Brandimarte, 2017. "Optimal Financial Decision Making Under Uncertainty," International Series in Operations Research & Management Science, in: Giorgio Consigli & Daniel Kuhn & Paolo Brandimarte (ed.), Optimal Financial Decision Making under Uncertainty, chapter 0, pages 255-290, Springer.
    3. Guastaroba, Gianfranco & Mansini, Renata & Speranza, M. Grazia, 2009. "On the effectiveness of scenario generation techniques in single-period portfolio optimization," European Journal of Operational Research, Elsevier, vol. 192(2), pages 500-511, January.
    4. Diana Barro & Elio Canestrelli, 2009. "Tracking error: a multistage portfolio model," Annals of Operations Research, Springer, vol. 165(1), pages 47-66, January.
    5. Jeff Fleming & Chris Kirby & Barbara Ostdiek, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, February.
    6. Mansini, Renata & Ogryczak, Wlodzimierz & Speranza, M. Grazia, 2014. "Twenty years of linear programming based portfolio optimization," European Journal of Operational Research, Elsevier, vol. 234(2), pages 518-535.
    7. Gaivoronski, Alexei A. & Krylov, Sergiy & van der Wijst, Nico, 2005. "Optimal portfolio selection and dynamic benchmark tracking," European Journal of Operational Research, Elsevier, vol. 163(1), pages 115-131, May.
    8. Giorgio Consigli & Gaetano Iaquinta & Vittorio Moriggia, 2012. "Path-dependent scenario trees for multistage stochastic programmes in finance," Quantitative Finance, Taylor & Francis Journals, vol. 12(8), pages 1265-1281, July.
    9. Christie, Andrew A., 1982. "The stochastic behavior of common stock variances : Value, leverage and interest rate effects," Journal of Financial Economics, Elsevier, vol. 10(4), pages 407-432, December.
    10. Wojtek Michalowski & Włodzimierz Ogryczak, 2001. "Extending the MAD portfolio optimization model to incorporate downside risk aversion," Naval Research Logistics (NRL), John Wiley & Sons, vol. 48(3), pages 185-200, April.
    11. Hiroshi Konno & Hiroaki Yamazaki, 1991. "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, INFORMS, vol. 37(5), pages 519-531, May.
    12. Diana Barro & Elio Canestrelli, 2014. "Downside risk in multiperiod tracking error models," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(2), pages 263-283, June.
    13. M. A. H. Dempster & Igor Evstigneev & Klaus Reiner Schenk-Hoppe, 2008. "Financial markets. The joy of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 8(1), pages 1-3.
    14. Michael A. H. Dempster & Igor V. Evstigneev & Klaus R. Schenk-hoppe, 2007. "Volatility-induced financial growth," Quantitative Finance, Taylor & Francis Journals, vol. 7(2), pages 151-160.
    15. Chen, Hsuan-Chi & Chung, San-Lin & Ho, Keng-Yu, 2011. "The diversification effects of volatility-related assets," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1179-1189, May.
    16. Rudolf, Markus & Wolter, Hans-Jurgen & Zimmermann, Heinz, 1999. "A linear model for tracking error minimization," Journal of Banking & Finance, Elsevier, vol. 23(1), pages 85-103, January.
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    Cited by:

    1. Jia Liu & Zhiping Chen & Giorgio Consigli, 2021. "Interval-based stochastic dominance: theoretical framework and application to portfolio choices," Annals of Operations Research, Springer, vol. 307(1), pages 329-361, December.
    2. Davide Lauria & Giorgio Consigli & Francesca Maggioni, 2022. "Optimal chance-constrained pension fund management through dynamic stochastic control," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 967-1007, September.
    3. Julio Cezar Soares Silva & Adiel Teixeira de Almeida Filho, 2023. "A systematic literature review on solution approaches for the index tracking problem in the last decade," Papers 2306.01660, arXiv.org, revised Jun 2023.
    4. Barro, Diana & Consigli, Giorgio & Varun, Vivek, 2022. "A stochastic programming model for dynamic portfolio management with financial derivatives," Journal of Banking & Finance, Elsevier, vol. 140(C).

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