The energy consumption structure has a crucial influence on the sustainable development of
the economy and on the environment, and it has drawn the attention of scholars and …

This study explores the relation between Chinese oil stock price volatility and the COVID-19
pandemic using an autoregressive conditional heteroskedasticity model and its …

In this paper, we propose the Real-Time Realized GARCH model incorporating the high-
frequency intraday information and current return information simultaneously to model and …

Crude oil price volatility prediction are important for energy policymaking and investment risk
avoidance, and have attracted a great deal of global attention. In recent years, although …

The complexity and uncertainty of the financial market mainly stem from the rich market
internal transaction information and a wide range effect of external factors. To this end, this …

Cryptocurrencies have increasingly attracted the attention of several players interested in
crypto assets. Their rapid growth and dynamic nature require robust methods for modeling …

The unprecedented outbreak of Corona Virus Disease 2019 (COVID-19) has resulted in
extreme volatility in stock markets. This study mainly examines the predictive ability of the …

This study focuses on the volatility prediction and option volatility investment. By
investigating the traditional Volatility Prediction Model and machine learning algorithms, this …

This study raises the problem of modeling conditional volatility under the random shocks'
normality assumption violation. To obtain more accurate estimates of GARCH process …

We propose a forecast combination scheme that employs time-varying weights, which
depend on the financial decision for which the forecasts are used. Combination weights are …