The volatility of natural resources implications for sustainable development: Crude oil volatility prediction based on the multivariate structural regime switching - Tang, Yusui; Ma, Feng (Resources policy, 2023)
Applying the multivariate heterogeneous autoregressive (MHAR-RV-DCC) model, this article considers the structural mutation characteristics of oil futures and the U.S. stock markets’ information fluctuations, and embeds Markov regime switching (MS) to investigate the prediction performance of the crude oil market volatility prediction model under the transition of high and low states, and explores whether the Markov mechanism conversion multivariate model constructed by nonparametric measurement variables will help the government, scholars and practitioners to judge the future market. We test the in-sample fitting and out-of-sample forecasting. In comparison, our newly proposed multivariate MHAR-RV-DCC model and MS-MHAR-RV-DCC model have stronger predictive capabilities than other high-frequency volatility prediction models, the MS-MHAR-RV-SJV-DCC has the best predictive ability. The above empirical results not only affirm the advantages of the newly constructed multivariate and Markov regime switching models in the application of crude oil futures market forecasting but also broaden the research ideas and specific methods for the characterization and prediction of crude oil market volatility in the future. •We explore the ability of cross-market volatility to predict the crude oil futures market from a multivariate perspective.•Our proposed model exhibits absolutely advantage in predicting realized volatility.•Markov regime switching, DCC-GARCH combined with HAR model can be helpful for the prediction of realized volatility.•Our results are robust although there are slight nuances for crude oil futures market volatility forecasting.