WTI, Brent or implied volatility index: Perspective of volatility spillover from oil market to Chinese stock market.

This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.

A stochastic dynamic programming model for the optimal policy mix of the carbon tax and decarbonization subsidy.

Carbon tax and decarbonization subsidy are an effective policy mix in reducing carbon emissions. However, there is a research gap between the deterministic and static analysis related to carbon reduction policy instruments and the dynamic green transition influenced by stochastic factors. This research investigates the optimal dynamic carbon reduction strategies that develop green technologies, increase abatement inputs, and reduce carbon emissions by applying the stochastic optimal control theory. Firms that are incentivized by decarbonization subsidies and regulated by carbon tax choose optimal closed-loop control strategies of abatement inputs to achieve profit-maximizing objectives with carbon reduction constraints. The explicit solutions of the optimal carbon tax and decarbonization subsidy are provided. The simulation results illustrate that the optimal policy mix is feasible in the effective period when the carbon emission decreases significantly, which indicates that the abatement policy mix can effectively promote carbon reduction. Our results reveal that the dynamic optimal policy mix is conducive to achieving carbon abatement goals with capital uncertainty. The government should implement a dynamic carbon tax and decarbonization subsidy policy mix simultaneously associated with optimal closed-loop carbon reduction strategies. Firms with asymmetric decarbonization efficiency can transfer progressively into a cleaner productive pattern.

Does oil price uncertainty matter in stock market volatility forecasting?

We analyze whether oil price uncertainty and U.S. stock uncertainty can simultaneously provide additional information to volatility forecast of six major stock indexes. For model settings, we find not only the uncertainty information of previous day, but that of previous week and month will also provide incremental predictive power for the stock market volatility. Based on that, from in-sample and out-of-sample perspective, the empirical evidences imply separately incorporating oil price uncertainty into the model can significantly improve the stock market volatility forecasting performance, but the improvements vanish after controlling the effects of volatility spillover from U.S. stock market while the effect of U.S. stock uncertainty is nonnegligible and sustainable for stock volatility forecasting. We confirm this finding from average and dynamic perspective. We further proceed the process in longer-horizon volatility forecasting, the evidences cannot overturn our conclusion. This conclusion implies that we should be cautious about the stock volatility predictability based on the oil price uncertainty, which further provide some important implications for researchers, regulators and investors.

Stochastic gradient boosting frequency-severity model of insurance claims.

The standard GLM and GAM frequency-severity models assume independence between the claim frequency and severity. To overcome restrictions of linear or additive forms and to relax the independence assumption, we develop a data-driven dependent frequency-severity model, where we combine a stochastic gradient boosting algorithm and a profile likelihood approach to estimate parameters for both of the claim frequency and average claim severity distributions, and where we introduce the dependence between the claim frequency and severity by treating the claim frequency as a predictor in the regression model for the average claim severity. The model can flexibly capture the nonlinear relation between the claim frequency (severity) and predictors and complex interactions among predictors and can fully capture the nonlinear dependence between the claim frequency and severity. A simulation study shows excellent prediction performance of our model. Then, we demonstrate the application of our model with a French auto insurance claim data. The results show that our model is superior to other state-of-the-art models.