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Leverage and Feedback Effects on Multifactor Wishart Stochastic Volatility for Option Pricing

Author

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  • Manabu Asai

    (Faculty of Economics Soka University, Japan and Wharton School University of Pennsylvania)

  • Michael McAleer

    (Econometric Institute Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute, The Netherlands and Institute of Economic Research Kyoto University, Japan and Department of Quantitative Economics Complutense University of Madrid, Spain)

Abstract

The paper proposes a general asymmetric multifactor Wishart stochastic volatility (AMWSV) di usion process which accommodates leverage, feedback e ects and mul- tifactor for the covariance process. The paper gives the closed-form solution for the conditional and unconditional Laplace transform of the AMWSV models. The paper also suggests estimating the AMWSV model by the generalized method of moments using information not only of stock prices but also of realized volatilities and co- volatilities. The empirical results for the bivariate data of the NASDAQ 100 and S&P 500 indices show that the general AMWSV model is preferred among several nested models.

Suggested Citation

  • Manabu Asai & Michael McAleer, 2013. "Leverage and Feedback Effects on Multifactor Wishart Stochastic Volatility for Option Pricing," KIER Working Papers 840, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:840
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    Cited by:

    1. Manabu Asai & Michael McAleer, 2017. "A fractionally integrated Wishart stochastic volatility model," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 42-59, March.
    2. Da Fonseca, José, 2016. "On moment non-explosions for Wishart-based stochastic volatility models," European Journal of Operational Research, Elsevier, vol. 254(3), pages 889-894.
    3. Hong, Hui & Bian, Zhicun & Chen, Naiwei, 2020. "Leverage effect on stochastic volatility for option pricing in Hong Kong: A simulation and empirical study," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    4. Bahmani, Mohammad & Sheikh Ahmadi, Sayed Amir & Sanginabadi, Bahram, 2013. "Return Volatility and Asymmetric News of Computer Industry stocks in Tehran Stock Exchange (TEX)," MPRA Paper 70793, University Library of Munich, Germany, revised 15 Mar 2014.
    5. Chang, Chia-Lin & McAleer, Michael, 2015. "Econometric analysis of financial derivatives: An overview," Journal of Econometrics, Elsevier, vol. 187(2), pages 403-407.
    6. M. Hakan Eratalay, 2016. "Estimation of Multivariate Stochastic Volatility Models: A Comparative Monte Carlo Study," International Econometric Review (IER), Econometric Research Association, vol. 8(2), pages 19-52, September.
    7. Chang, C-L. & McAleer, M.J., 2014. "Econometric Analysis of Financial Derivatives," Econometric Institute Research Papers EI 2015-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Baule, Rainer & Shkel, David, 2021. "Model risk and model choice in the case of barrier options and bonus certificates," Journal of Banking & Finance, Elsevier, vol. 133(C).
    9. Karmous, Aida & Boubaker, Heni & Belkacem, Lotfi, 2019. "A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

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    More about this item

    Keywords

    Multivariate Stochastic Volatility; Wishart Process; Leverage E ects; Feedback Effects; Multifactor Model; Option Pricing.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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