New Evidence on the Information Content of Implied Volatility of S&P 500: Model-Free versus Model-Based

Weiwei Zhang (), Tiezhu Sun (), Yechi Ma () and Zilong Wang ()
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Weiwei Zhang: Changchun University of Science and Technology, Changchun, China.
Tiezhu Sun: Corresponding author. Changchun Guanghua University, Changchun, China.
Yechi Ma: School of Business, Northeast Normal University, Changchun, China.
Zilong Wang: Department of Land Economy, University of Cambridge, Cambridge, UK.

Journal for Economic Forecasting, 2021, issue 1, 109-121

Abstract: This paper provides new evidence to compare the information content of model-free implied volatility (MFIV) and model-based volatility for forecasting future volatility of the S&P 500. We choose Black and Scholes (BS) implied volatility as our model-based volatility and VIX as our measure of MFIV. By using non-overlapping monthly samples from January 2004 to June 2019, we find that both BS implied volatility and MFIV are informationally efficient and subsume information contained in the historical realized volatility for forecasting future volatility. This is the first study show that BS implied volatility and MFIV contain the same information and there is no winner for forecasting future volatility. This implied that a forecast model could include both BS implied volatility and MFIV

Keywords: Implied volatility; VIX; Realized volatility; Information; Volatility forecasts; Volatility models (search for similar items in EconPapers)
JEL-codes: C53 G12 G14 (search for similar items in EconPapers)
Date: 2021
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