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Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model
Author(s) -
Pan Zhiyuan,
Wang Yudong,
Liu Li,
Wang Qing
Publication year - 2019
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.22003
Subject(s) - autoregressive conditional heteroskedasticity , econometrics , economics , volatility (finance) , implied volatility , valuation of options , volatility smile , spillover effect , heteroscedasticity , forward volatility , autoregressive model , stochastic volatility , volatility swap , financial economics , microeconomics
We develop a new generalized autoregressive conditional heteroskedasticity (GARCH) model that accounts for the information spillover between two markets. This model is used to detect the usefulness of the CBOE volatility index (VIX) for improving the performance of volatility forecasting and option pricing. We find the significant ability of VIX to predict stock volatility both in‐sample and out‐of‐sample. VIX information also helps to greatly reduce the option pricing error. The proposed volatility spillover GARCH model performs better than the related approaches proposed by Kanniainen et al. (2014, J Bank Finance , 43, pp. 200‐211) and P. Christoffersen et al. (2014, J Financ Quant Anal , 49, pp. 663–697).