Empirical Stylized Facts Modelling and Forecast Evaluations for Energy and Stock Markets

Abstract

This paper applies autoregressive heteroscedasticity (ARCH) family models for the purpose of comparing stylized facts such as volatility clustering, leverage effect, long memory volatility and risk-return tradeoff for energy and stock markets. Empirical results have found that the presences of volatility clustering in both markets and the impact of volatility shocks to the conditional volatility display hyperbolic rather than exponential rate of decay. Meanwhile, only stock markets denote the leverage effect, which implies that ‘bad’ news has a greater impact on volatility than ‘good’ news at the same magnitude. Additionally, empirical results also highlighted that Kerosene and Brent crude oil are the only energy commodities exhibit risk-return tradeoff. For forecast evaluations, the FIAPARCH model indicates superior out of sample forecasts over short and long time horizon for stock markets. Nevertheless, FIAPARCH model suits better over long term as compared to short term for energy markets. Finally, the Superior Predictive Ability (SPA) tests suggested that overall asymmetric long memory GARCH models display higher forecasting accuracy than the standard GARCH models.

Keywords:

• Stylized facts
• Energy markets
• Stock markets
• Comparison
• ARCH