Open AccessForecasting the Crude Oil Prices Volatility With Stochastic Volatility Models
Author(s) -
Dondukova Oyuna,
Yaobin Liu
Publication year - 2021
Publication title -
sage open
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.357
H-Index - 32
ISSN - 2158-2440
DOI - 10.1177/21582440211026269
Subject(s) - stochastic volatility , stylized fact , autoregressive conditional heteroskedasticity , econometrics , volatility (finance) , forward volatility , volatility smile , implied volatility , economics , heston model , sabr volatility model , autoregressive model , mean squared error , heteroscedasticity , mathematics , statistics , macroeconomics
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.