Investor sentiment is a research focus in behavior finance. This paper chooses five proxy variables according to China’s reality and uses a two-step principal component analysis to construct an investor sentiment index. The five proxy variables are the number of new stock accounts, turnover ratio, margin balance, net active purchasing amount, and investor attention. In the final part of this study, using the price data from the Shanghai and Shenzhen Security Exchange, this paper investigates the dynamic relationship between investor sentiment and stock market realized volatility based on the thermal optimal path. The empirical results show that when the market fluctuates severely, investor sentiment leads stock market realized volatility over one or two steps. The prediction power is also checked. The results indicate that investor sentiment indeed forecasts the realized volatility. This research supports regulators and financial institutions in taking advanced measures.

This paper investigates how oil’s future price implies the bunker price through cointegration analysis first. A cointegration test confirms the long-run equilibrium condition of bunker and oil future prices. Based on the cointegration relationship, we construct VECM model to forecast bunker prices. In addition, we also consider ARMA, ARMAX, and VAR models for certifying whether considering the long-run equilibrium between bunker and oil future prices is helpful in prediction. One-step-ahead and four-step-ahead forecasting are considered and two out-of-sample datasets are used. The empirical results show that the increase in the value of the error correction term in the VECM model has the effect of pulling down the bunker return. VECM performs better than other models in prediction. The Crude Oil Future Contract 1 has better forecasting performance for bunker prices with VECM in the 1-step-ahead forecast, while Crude Oil Future Contract 3 performs slightly better than Crude Oil Future Contract 1 in the 4-step-ahead forecast.