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Economic policy uncertainty (EPU) has important implications for crude oil market. To explore the implications, this paper investigates the impact of EPU on the crude oil return volatility and which EPU index has the most forecasting power in crude oil market. To this end, we employ the GARCH-MIDAS model which can incorporate lower frequency EPU index variable with higher frequency crude oil return variable effectively. We find that EPU has a positive and significant impact on the crude oil return volatility, but the effect is short-lived and the decay period is about one year. Particularly, our results show that the US EPU index has the best forecasting power for crude oil return volatility over the long-term, whereas China EPU index has the best forecasting performance in the past one year. Our findings have important implications on risk management for investors in crude oil market. JEL classification: C32, C58, E32, Q41, Q47, Keywords: Economic policy uncertainty, EPU index, Crude-oil return volatility, GARCH-MIDAS

This paper explores the impacts of stock market volatility on stock-bond and stock-gold correlations and compares the effectiveness of bonds with gold hedging the stock market risk. We propose an extensive DCC-MIDAS model, which takes the changes in stock market volatility as exogenously predetermined and accounts for its asymmetric impacts. The results show strong evidence that stock market volatility significantly and asymmetrically affects stock-bond and stock-gold correlations in different ways. Moreover, we prove that government bonds are more effective than gold in hedging stock return volatility, especially in the period of stock market turbulence. Our results suggest that government bonds are overall a better hedge for stock investors.

Feed-in tariff (FIT) is a broadly used policy instrument around the world, in which the payment level is always designed to decline gradually in order to track and encourage technological improvement in renewable energy. However, the potential adverse effects of tariff degression on renewable energy research and development (R&D) investment have not been sufficiently addressed. In this paper, we explore the impact of FIT payment degression on renewable energy R&D investment and analyze the efficient FIT payment structure for R&D investment. We form a modeling framework that captures the dynamics of FIT payment levels and the inventor's strategic reactions to tariff changes. In this framework, we model the inventor's optimal R&D investment decisions based on the canonical real option approach and simulate the impact of FIT payment degression on R&D investment and the efficient FIT payment structure under different R&D characteristics using the case of the solar photovoltaic (PV) industry. Our results emphasize the adverse effect of tariff degression on the commercial value of new renewable technology and hence the inventor's incentive in R&D investment, especially when facing more technical uncertainty and financial constraints. Our model could help policymakers to design an efficient FIT payment structure through which the cost of the FIT program is restricted to a minimum level but without influencing the inventor's R&D investment decisions.

By employing multivariate GARCH models to stock returns and AR-GARCH models with change points identified by ICSS algorithm to predicted dynamics conditional correlations, this paper seeks to analyze information shocks on daily return dynamics in three Chinese segmented stock markets, namely Shanghai, Shenzhen and Hong Kong, during the sample period 2014-2016. The paper finds the significant evidence of contagion in the time-varying correlation between Shanghai and Shenzhen stock market as well as the statistically and economically significant decrease in the correlation series of Shanghai-Hong Kong and Shenzhen and Hong Kong after the burst of the stock market bubble and the onset of Shanghai-Hong Kong Stock Connect, respectively, which proves that the constant conditional correlation model, even following the heteroskedasticity-adjusted method of Forbes & Rigobon (2002), may fail to capture the time-varying feature, to reflect the reality of dynamic market conditions or to arrive at a correct conclusion on financial contagion in the stock markets.

This study investigates whether and with whom R&D cooperation can alleviate the adverse influence of financial constraints on firms’ innovation performance. Specifically, three different types of R&D cooperation are considered in this study, i.e., cooperation with suppliers, customers and research institutes. Using the data of manufacturing firms from Chinese Enterprise Survey, we find that R&D cooperation can improve firms’ innovation performance effectively when facing financial constraints. Furthermore, we find that R&D cooperation with customers are more effective than cooperation with suppliers and research institutes in mitigating the negative effect of financial constraints on new product development, while R&D cooperation with suppliers are more effective than cooperation with customers and research institutes in improving technological processes. Overall, our findings provide direct evidence that R&D cooperation can be an effective strategy to improve innovation performance when firms face financial constraints.

Using DCC, ADCC and GO-GARCH models, this paper examines whether the Shanghai-Hong Kong Stock Connect program drives market comovement between Shanghai and Hong Kong. We distinguish financial liberalization induced market comovement from that induced by other factors through comparing time-varying market correlations of Shanghai-Hong Kong with those of Shenzhen-Hong Kong. The results of the three variants of multivariate GARCH models consistently show that, if we ignore the period of market crash, the market correlation between Shanghai and Hong Kong does not significantly increase after the launch of the program. Furthermore, inconsistent with theoretical prediction, we find that the correlation between Hong Kong and financially non-liberalized Shenzhen market increases much more than that between Hong Kong and financially liberalized Shanghai market in the market turbulence. The results implicate the Shanghai-Hong Kong Stock Connect program is not the main fundamental force that drives market comovement between Shanghai and Hong Kong in the short run. This finding is further supported by the results of optimal hedge ratios and downside risk measures, which hold important risk management implications for investors in these markets.