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Nested Conditional Value-at-Risk portfolio selection: A model with temporal dependence driven by market-index volatility

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  • Staino, Alessandro
  • Russo, Emilio

Abstract

In a multistage stochastic programming framework, we develop a new method for finding an approximated portfolio allocation solution to the nested Conditional Value-at-Risk model when asset log returns are stagewise dependent. We describe asset log returns through a single-factor model where the driving factor is the market-index log return modeled by a Generalized Autoregressive Conditional Heteroskedasticity process to take into account the serial dependence usually observed. To solve the nested Conditional Value-at-Risk model, we implement a backward induction scheme coupled with cubic spline interpolation that reduces the computational complexity of the optimal portfolio allocation and allows to treat problems otherwise unmanageable.

Suggested Citation

  • Staino, Alessandro & Russo, Emilio, 2020. "Nested Conditional Value-at-Risk portfolio selection: A model with temporal dependence driven by market-index volatility," European Journal of Operational Research, Elsevier, vol. 280(2), pages 741-753.
    • Handle: RePEc:eee:ejores:v:280:y:2020:i:2:p:741-753
    DOI: 10.1016/j.ejor.2019.07.032
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    Citations

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    Cited by:

    1. Guo, Sini & Gu, Jia-Wen & Ching, Wai-Ki, 2021. "Adaptive online portfolio selection with transaction costs," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1074-1086.
    2. Mercadier, Mathieu & Strobel, Frank, 2021. "A one-sided Vysochanskii-Petunin inequality with financial applications," European Journal of Operational Research, Elsevier, vol. 295(1), pages 374-377.
    3. Taras Bodnar & Mathias Lindholm & Vilhelm Niklasson & Erik Thors'en, 2020. "Bayesian Quantile-Based Portfolio Selection," Papers 2012.01819, arXiv.org.

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