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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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    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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