Modeling energy price dynamics: GARCH versus stochastic volatility

Abstract

We compare a number of GARCH and stochastic volatility (SV) models using nine series of oil, petroleum product and natural gas prices in a formal Bayesian model comparison exercise. The competing models include the standard models of GARCH(1,1) and SV with an AR(1) log-volatility process and more flexible models with jumps, volatility in mean and moving average innovations. We find that: (1) SV models generally compare favorably to their GARCH counterparts; (2) the jump component substantially improves the performance of the standard GARCH, but is unimportant for the SV model; (3) the volatility feedback channel seems to be superfluous; and (4) the moving average component markedly improves the fit of both GARCH and SV models. Overall, the SV model with moving average innovations is the best model for all nine series.

Time-Varying Volatility Feedback of Energy Prices: Evidence from Crude Oil, Petroleum Products, and Natural Gas Using a TVP-SVM Model

“…However, they cannot detect the time-varying volatility feedback and provide more information that is useful for investors in the energy market. Our empirical results are not consistent with the finding of Chan and Grant (2016). They employ the constant coefficient SVM model and provide evidence that WTI crude oil price volatility affects WTI returns negatively during the sample period, from 3 January 1997 to 6 February 2015.…”

“…As one of the world's most frequently traded commodities, huge and sharp volatile energy prices often result in a significant impact on production capacity, which causes further economic fluctuations [7]. And it will influence the energy price itself if energy price volatility persists [8]. Moreover, there are significant substantial risks to both producers and consumers via unlimited increases in the costs of inventory, transportation, and production [9].…”

“…At the present time, a great deal of literature has been concerned with the measuring of energy price volatility, volatility forecasting [10][11][12][13][14], and the volatility spillover effect of energy prices on other financial markets, such as the stock market, the non-energy commodity market and exchange rate market [15][16][17][18][19][20]. However, it is surprising that there is little literature investigating the volatility feedback of energy prices [8,21,22]. For example, Chan and Grant [8] proved that the stochastic volatility in mean (SVM) model outperforms the GARCH-M model in evaluating the volatility feedback of energy prices and show that crude oil (WTI and Brent), petroleum products, and gas price volatility have a negative side for their returns.…”

“…However, it is surprising that there is little literature investigating the volatility feedback of energy prices [8,21,22]. For example, Chan and Grant [8] proved that the stochastic volatility in mean (SVM) model outperforms the GARCH-M model in evaluating the volatility feedback of energy prices and show that crude oil (WTI and Brent), petroleum products, and gas price volatility have a negative side for their returns. However, there are few studies that investigate the volatility feedback of petroleum products and natural gas prices and consider the structural breaks of energy prices when estimating the feedbacks of energy price.…”

Efficient Bayesian Inference for Nonlinear State Space Models With Univariate Autoregressive State Equation

“…Time varying volatility is a stylized fact of financial time series. In this context, the stochastic volatility model has also shown better performance than the frequently used GARCH models (Engle (1982), Bollerslev (1986)) for several data sets (Yu (2002), Chan and Grant (2016)). To allow for heavy tails and skewness, other distributions have been considered in the observation equation.…”