Oil price volatility forecasting: Threshold effect from stock market volatility

Yan Chen, Gaoxiu Qiao and Feipeng Zhang

Technological Forecasting and Social Change, 2022, vol. 180, issue C

Abstract: Stock market volatility, which is usually considered a proxy for the general economy, contains important information for the crude oil market. In this paper, we investigate the incremental benefit of stock market volatility over oil volatility using the S&P 500 index and WTI oil prices for the period from January 1990 to December 2021. The threshold autoregressive regression (TAR) model is used to capture the nonlinear threshold effect of stock market shock on oil market volatility. From empirical analysis, both in-sample and out-of-sample results highlight the prediction superiority and effectiveness of the nonlinear threshold regression model, which indicates the valuable strong threshold effects of stock volatility for oil volatility forecasting. Moreover, the additional effects of stock volatility in terms of bad volatility forecasting further confirm the effectiveness of the nonlinear TAR model and the information content of stock volatility. This study will prove useful for policy-makers to formulate reasonable policies and for investors to avoid risk.

Keywords: Threshold autoregressive regression model; Oil price volatility; Out-of-sample forecasting; Threshold effect (search for similar items in EconPapers)
JEL-codes: G13 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:180:y:2022:i:c:s0040162522002311

DOI: 10.1016/j.techfore.2022.121704

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