Forecasting the Crude Oil Prices Volatility With Stochastic Volatility Models

Dondukova Oyuna and Liu Yaobin

SAGE Open, 2021, vol. 11, issue 3, 21582440211026269

Abstract: In this article, the stochastic volatility model is introduced to forecast crude oil volatility by using data from the West Texas Intermediate (WTI) and Brent markets. Not only that the model can capture stylized facts of multiskilling, extended memory, and structural breaks in volatility, it is also more frugal in parameterizations. The Euler–Maruyama scheme was applied to approximate the Heston model. On the contrary, the root mean square error (RMSE) and the mean average error (MAE) were used to approximate the generalized autoregressive conditional heteroskedasticity (GARCH)–type models (symmetric and asymmetric). Based on the approximation results obtained, the study established that the stochastic volatility model fits oil return data better than the traditional GARCH-class models.

Keywords: mathematical and quantitative methods in economics; economic science; social sciences; science; math; & technology; curriculum; education; financial economics; computational/mathematical psychology; experimental psychology; psychology; crude oil price; stochastic volatility models; price volatility (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:11:y:2021:i:3:p:21582440211026269

DOI: 10.1177/21582440211026269

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