Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model

Pan, Zhiyuan; Wang, Yudong; Liu, Li; Wang, Qing

doi:10.1002/fut.22003

Cited by 33 publications

(19 citation statements)

References 70 publications

(128 reference statements)

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“…This study uses the Chicago Board Options Exchange's (CBOE) Volatility Index (VIX) as a measure of the United States (US) stock market's expectation of volatility. This index is commonly known as the "fear index" or "fear gauge" (Ding et al, 2021;Simon & Wiggins III, 2001;Whaley, 2000Whaley, , 2009, and has been used to study volatility transmission during previous crisis periods (Cheuathonghua et al, 2019;Rodriguez-Nieto & Mollick, 2020), as well as to measure the transmission of volatility between financial entities (Kang et al, 2019;Pan et al, 2019). One key contribution of this paper is that we establish how changes in correlation patterns between European banks and the VIX occurred during the GFC and COVID-19 periods.…”

Section: Introductionmentioning

confidence: 96%

Volatility impacts on the European banking sector: GFC and COVID-19

et al. 2022

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This paper analyses the volatility transmission between European Global Systematically Important Banks (GSIBs) and implied stock market volatility. A Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model is applied to determine the dynamic correlation between returns of Europe’s GSIBs and the world’s most prominent measure of market “fear”, the CBOE Volatility Index (VIX). The results identify a higher negative co-relationship between the VIX and GSIB returns during the COVID-19 period compared with the Global Financial Crisis (GFC), with one-day lagged changes in the VIX negatively Granger-causing bank returns. The asymmetric impact of changes in implied volatility is examined by quantile regressions, with the findings showing that in the lower quartile–where extreme negative bank returns are present–jumps in the VIX are highly significant. This effect is more pronounced during COVID-19 than during the GFC. Additional robustness analysis shows that these findings are consistent during the periods of the Swine Flu and Zika virus epidemics.

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Section: Introductionmentioning

confidence: 96%

Volatility impacts on the European banking sector: GFC and COVID-19

et al. 2022

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“…Furthermore, the spot variance

{h}_{t+1}=\frac{VI{X}_{t}^{2}/({100}^{2}\times 252)-a}{b}

combining the information implied in option prices (Kanniainen et al, 2014; Pan et al, 2019) is extracted from the VIX formula, which is different from the return implied proxy of the spot variance updating rule.…”

Section: Methodsmentioning

confidence: 99%

“…Using the expectation formula of the square root function (Schürger, 2002), which has been used to derive the closed-form pricing formula of volatility swap or VIX futures prices (Broadie & Jain, 2008;Zhu & Lian, 2012) combining the information implied in option prices (Kanniainen et al, 2014;Pan et al, 2019) is extracted from the VIX formula, which is different from the return implied proxy of the spot variance updating rule.…”

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confidence: 99%

Directly pricing VIX futures with observable dynamic jumps based on high‐frequency VIX

Jiang

Qiao

et al. 2022

Journal of Futures Markets

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This paper proposes to study volatility index (VIX) futures pricing by directly modeling the logarithmic VIX while incorporating observable dynamic jumps of the VIX, which are derived based on VIX high-frequency data. The impacts of several different interday and intraday jump tests for VIX futures prices are investigated. We obtain the analytical expression by deducing the forward iteration relations of the lagged logarithm VIX, as well as the conditional variance and jump intensity, and use the maximum likelihood method to estimate the parameters under the risk-neutral measure. The empirical results prove the superiority of our newly proposed model (especially the model based on the LM jump test), which indicates that considering the heteroscedasticity effect of conditional variance, introducing VIX high-frequency data information and separating realized jump variation from the realized variance are very important to obtain much more accurate VIX futures pricing.

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“…The goal of this paper is to provide evidence of the superior forecasting performances of the modified HAR-RV models compared with the general approaches. The DM test proposed by Diebold and Mariano (1995) is an appropriate method for one-to-one comparative evaluation and has been widely used by a large number of studies on volatility forecasting (see, e.g., Asai et al, 2020;Chortareas et al, 2011;Kang & Yoon, 2013; E. M. H. Lin et al, 2012;Pan et al, 2017Pan et al, , 2019Qu et al, 2018;Sévi, 2014;Shen & Ritter, 2016;Yao et al, 2019;.…”

Section: Forecasting Evaluationmentioning

confidence: 99%

Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach

Wen

Zhang

et al. 2021

Journal of Forecasting

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In this study, we propose a new family of the heterogeneous autoregressive realized volatility (HAR-RV) models by considering truncated methods for predicting the RV in China's stock market. By adopting three types of critical values to recognize extremely large values of RV, we show that the modified models are simple but efficient to consistently deliver stronger in-sample and out-of-sample forecasting performances than those of existing methods. Models that take truncated approaches into account can generate substantial economic gains in applications. We further provide evidence that the superiority of our proposed models is derived from the reduced variance of the measurement errors during days including truncated RVs. Additionally, the improved performances of the modified models still hold after considering the effects of jump components and leverage, as well as a wide range of extensions and robustness analyses. K E Y W O R D SChina's stock market, HAR-RV model, truncated method, volatility forecasting | INTRODUCTIONVolatility and accurate forecasts of volatility in the stock market are crucial for asset pricing and risk management. Given the helpful role of high-frequency data in forecasting volatility, realized volatility (RV) based on intraday data, as pioneered by Andersen and Bollerslev (1998), receives much attention with regard to modeling and predicting financial market volatility (see, e.g.,

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Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model

Cited by 33 publications

References 70 publications

Volatility impacts on the European banking sector: GFC and COVID-19

Volatility impacts on the European banking sector: GFC and COVID-19

Directly pricing VIX futures with observable dynamic jumps based on high‐frequency VIX

Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach

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