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Forecasting the Crude Oil Prices Volatility With Stochastic Volatility Models

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  • Dondukova Oyuna
  • Liu Yaobin

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.

Suggested Citation

  • Dondukova Oyuna & Liu Yaobin, 2021. "Forecasting the Crude Oil Prices Volatility With Stochastic Volatility Models," SAGE Open, , vol. 11(3), pages 21582440211, July.
    • Handle: RePEc:sae:sagope:v:11:y:2021:i:3:p:21582440211026269
    DOI: 10.1177/21582440211026269
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    References listed on IDEAS

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    Cited by:

    1. NICOLAE Simona & GRIGORE George-Eduard & MUȘETESCU Radu-Cristian, 2022. "The Use of GARCH Autoregressive Models in Estimating and Forecasting the Crude Oil Volatility," European Journal of Interdisciplinary Studies, Bucharest Economic Academy, issue 01, March.
    2. Per Bjarte Solibakke, 2021. "Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities," JRFM, MDPI, vol. 14(11), pages 1-17, October.

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