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Volatility Trading: What Is the Role of the Long-Run Volatility Component?

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  • Zhou, Guofu
  • Zhu, Yingzi

Abstract

We study an investor’s asset allocation problem with a recursive utility and with tradable volatility that follows a 2-factor stochastic volatility model. Consistent with previous findings under the additive utility, we show that the investor can benefit substantially from volatility trading due to hedging demand. Unlike existing studies, we find that the impact of elasticity of intertemporal substitution (EIS) on investment decisions is of 1st-order importance. Moreover, the investor can incur significant economic losses due to model and/or parameter misspecifications where the EIS better captures the investor’s attitude toward risk than the risk aversion parameter.

Suggested Citation

  • Zhou, Guofu & Zhu, Yingzi, 2012. "Volatility Trading: What Is the Role of the Long-Run Volatility Component?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(2), pages 273-307, April.
    • Handle: RePEc:cup:jfinqa:v:47:y:2012:i:02:p:273-307_00
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    Citations

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    Cited by:

    1. Warren Bailey & Lin Zheng & Yinggang Zhou, 2012. "What Makes the VIX Tick?," Working Papers 222012, Hong Kong Institute for Monetary Research.
    2. Olesya V. Grishchenko & Zhaogang Song & Hao Zhou, 2015. "Term Structure of Interest Rates with Short-run and Long-run Risks," Finance and Economics Discussion Series 2015-95, Board of Governors of the Federal Reserve System (U.S.).
    3. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    4. Rytchkov, Oleg, 2016. "Time-Varying Margin Requirements and Optimal Portfolio Choice," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(2), pages 655-683, April.
    5. Dew-Becker, Ian & Giglio, Stefano & Le, Anh & Rodriguez, Marius, 2017. "The price of variance risk," Journal of Financial Economics, Elsevier, vol. 123(2), pages 225-250.
    6. Moreira, Alan & Muir, Tyler, 2019. "Should Long-Term Investors Time Volatility?," Journal of Financial Economics, Elsevier, vol. 131(3), pages 507-527.
    7. Chen, Xingjiang & Ruan, Xinfeng & Zhang, Wenjun, 2021. "Dynamic portfolio choice and information trading with recursive utility," Economic Modelling, Elsevier, vol. 98(C), pages 154-167.
    8. Song, Zhaogang & Xiu, Dacheng, 2016. "A tale of two option markets: Pricing kernels and volatility risk," Journal of Econometrics, Elsevier, vol. 190(1), pages 176-196.
    9. Victor Troster & José Penalva & Abderrahim Taamouti & Dominik Wied, 2021. "Cointegration, information transmission, and the lead‐lag effect between industry portfolios and the stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1291-1309, November.
    10. Marcos Escobar & Sebastian Ferrando & Alexey Rubtsov, 2017. "Optimal investment under multi-factor stochastic volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 241-260, February.
    11. Wang, Qi & Wang, Zerong, 2020. "VIX valuation and its futures pricing through a generalized affine realized volatility model with hidden components and jump," Journal of Banking & Finance, Elsevier, vol. 116(C).

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