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VIX valuation and its futures pricing through a generalized affine realized volatility model with hidden components and jump

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  • Wang, Qi
  • Wang, Zerong

Abstract

In this paper, we provide several theoretically relevant and empirically significant improvements to the general affine realized volatility (GARV) model of Christoffersen et al. (2014). We impose hidden volatility components in both the return-based conditional variance and the realized variance and augment their combination with another jump component. This new composition nests within a common framework several empirically well-tested models such as the GARV model mentioned above. To facilitate practical implementations we obtain the closed-form formulas to evaluate VIX and its futures through a variance-dependent kernel. Our empirical studies demonstrate that the volatility-component specification provides a further evident improvement in VIX forecasting and its futures pricing across maturity and volatility levels; more importantly, these hybrid and hidden features turn out to be complements rather than substitutes, and their prominence is further intensified by the jump.

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  • 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).
  • Handle: RePEc:eee:jbfina:v:116:y:2020:i:c:s0378426620301114
    DOI: 10.1016/j.jbankfin.2020.105845
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    Cited by:

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    2. Qi Wang & Zerong Wang, 2021. "VIX futures and its closed‐form pricing through an affine GARCH model with realized variance," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 135-156, January.
    3. Chen, Xiaoyi & Feng, JianFen & Wang, Tianyi, 2023. "Pricing VIX futures: A framework with random level shifts," Finance Research Letters, Elsevier, vol. 52(C).
    4. Li, Shaoyu & Zhang, Yuanyuan & Zhu, Chunhui, 2021. "A closed-form exact solution for pricing fixed-income variance swaps with affine-jump model," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    5. Gongyue Jiang & Gaoxiu Qiao & Feng Ma & Lu Wang, 2022. "Directly pricing VIX futures with observable dynamic jumps based on high‐frequency VIX," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1518-1548, August.
    6. Mora-Valencia, Andrés & Rodríguez-Raga, Santiago & Vanegas, Esteban, 2021. "Skew index: Descriptive analysis, predictive power, and short-term forecast," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    7. Fangsheng Yin & Yang Bian & Tianyi Wang, 2021. "A short cut: Directly pricing VIX futures with discrete‐time long memory model and asymmetric jumps," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(4), pages 458-477, April.
    8. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.

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    More about this item

    Keywords

    GARCH; Realized volatility; Volatility component; VIX Futures pricing;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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