Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models

Abstract

Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot markets for risk managers. Capturing conditional distribution, fat tails and price spikes properly is crucial to the correct measurement of risk. This paper is an attempt to model volatility of energy futures under different distributions. In empirical analysis, we estimate the volatility of Natural Gas Futures, Brent Oil Futures and Heating Oil Futures through GARCH and APARCH models under gev, gat and alpha-stable distributions. We also applied various VaR analyses, Gaussian, Historical and Modified (Cornish-Fisher) VaR, for each variable. Results suggest that the APARCH model largely outperforms the GARCH model, and gat distribution performs better in modeling fat tails in returns. Our results also indicate that the correct volatility level, in gat distribution, is higher than those suggested under normal distribution with rates of 56%, 45% and 67% for Natural Gas Futures, Brent Oil Futures and Heating Oil Futures, respectively. Implemented VaR analyses also support this conclusion. Additionally, VaR test results demonstrate that energy futures display riskier behavior than S&P 500 returns. Our findings suggest that for optimum risk management and trading strategies, risk managers should consider alternative distributions in their models. According to our results, prices in energy markets are wilder than the perception of normal distribution. In this regard, regulators and policy makers should enhance transparency and competitiveness in the energy markets to protect consumers.

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Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model

“…Therefore, we need a way to find out how much risk will be borne in investing. Thus, the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model is one of the models used to analyze investment risk (Hidayatullah & Qudratullah, 2017) (Gunay & Khaki, 2018).…”

“…To model data that has heteroscedasticity, APARCH model introduced by Granger, Ding, and Eagle can be used in 1993 (Ilupeju, 2016). An important point in the APARCH model is to change the second order of the error value in a flexible form and has an asymmetric coefficient on the difference between the effects of good news and bad news (Gunay & Khaki, 2018) (Irene, Wijaya, & Muhayani, 2020).…”

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