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Nakajima, J. (2009) Bayesian Analysis of GARCH and Stochastic Volatility: Modeling Leverage, Jumps and Heavy-Tails for Financial Time Series. Technical Report Mimeo, Department of Statistical Science, Duke University, Durham.

has been cited by the following article:

• TITLE: Forecasting Crude Oil Price Volatility by Heston Model

  AUTHORS: Patrick Kandege Mwanakatwe, Joanitha Daniel, Kulwa Roberth Nzungu

  KEYWORDS: Stochastic Volatility, Euler-Maruyama Scheme, Heston Model, Crude Oil Price Volatility

  JOURNAL NAME: Journal of Mathematical Finance, Vol.13 No.3, August 29, 2023

  ABSTRACT: Given the significance and complexity of forecasting the crude oil price volatility, this paper introduces the Heston model to predict volatility dynamics of crude oil price. The high-frequency intra-day data of the West Texas Intermediate (WTI) market serves to model the problem. Furthermore, the study used the Euler-Maruyama scheme to simulate Heston model using an error analysis. On the other hand, the study used the mean square error (MSE), the mean average error (MAE), and the root means square error (RMSE) for the forecast accuracy of the GARCH-type models. The results of the error analysis indicated that Heston’s stochastic volatility model is more consistent with oil performance data than traditional GARCH-class models. The study’s findings demonstrated that the Heston model is more economical in terms of setup and is capable of handling stylized facts.