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AbstractThis paper proposes a regime‐switching model to examine the correlation dynamics of the mainland Chinese and Hong Kong stock markets with high‐frequency A‐, B‐, H‐shares and Red Chip indexes. We find significant evidence of volatility persistence and asymmetries in these markets. Our model further suggests all correlations are significantly time‐varying with various patterns and co‐persistence in both low‐ and high‐correlation states. Our findings have important implications for both policymakers and investors, such as understanding the extent and nature of integration between the mainland Chinese and Hong Kong stock markets over time and developing dynamic strategies for optimal hedging and portfolio management.

AbstractIn this paper we argue that the commonly employed exposure coefficient/beta is inadequate for capturing the entire impact of exchange rate changes on firms' future operating cash flows. Instead, we employ the bivariate Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroskedasticity mean model to investigate four aspects of exchange rate exposure, including sensitivity of stock returns to exchange rate changes, sensitivity of stock returns to the volatility of exchange rate changes, sensitivity of conditional variance of returns to exchange rate volatility, and the dynamic conditional correlation between returns and exchange rate changes, respectively, using data from 10 industrial sectors in Japan. We find significant evidence of such exchange rate exposure which is not captured by the conventional measure. The diagnostic statistics confirm the adequacy of our model, and, hence, the robustness of the results.

The performance measure of funds has been an important topic in the past few decades. In recent years the conditional models on return and volatility have become popular in studying the funds' performance measure, but most of these studies focus on the US funds and a few on the Asian-based funds. The purpose of this study is to examine the volatility-timing performance of Singapore-based funds under the CPF (Central Provident Fund) Investment Scheme and non-CPF linked funds by taking into account of the currency risk effect on internationally managed funds. The CPF investment scheme was introduced in 1986 by the Singapore government in order to enhance CPF members' funds for retirement. CPF members usually withdraw money for house purchase, while male and high income earners involve in more risky investment with their CPF saving. The CPF board sets up strict admission criteria for investment products, especially for funds which tend to enter the CPF investment scheme. Fund management companies with intention to enter the CPF Investment must have at least S$500 million fund managed in Singapore with minimum three fund managers. One of fund managers must have at least 5 year experience in fund management. Moreover, foreign funds recognized by the Monetary Authority of Singapore (MAS) are allowed to apply for the inclusion of CPF investment scheme, provided that they are a member of the Investment Management Association of Singapore and also have to submit a representative agreement of foreign funds or their mangers. There are 28 fund management companies under the current CPF investment scheme. Since 1 February 2006, the revised benchmark requires new-entry funds to be above the top 25% among their global peers. Compared with the existing funds within the risk level under CPF Investment Scheme, new funds are also required to have lower-than-median expense ratio. A good historical performance for at least 3 years is desirable. In addition, sales charges for fund under CPF Investment Scheme must be less than 3% from 1 Jul 2007. Given the strict entry criteria, it is an interesting question to ask if the CPF funds are "safer and better performed funds" as people expected. In this study we empirically assess whether the funds under CPF Investment Scheme outperform non-CPF funds by examining the volatility-timing performance associated with these funds. The volatility-timing ability of CPF funds will provide CPF board with a new method for risk classification. In particular, we employ the GARCH models and modified factor models to capture the response of funds to the market abnormal conditional volatility including the week day effect. The SMB and HML factors for non-US based funds are constructed from stock market data to exclude the contribution of size effect and BE\ME effect. The results show that volatility timing is one of the factors contributing to the excess return of funds. However, the funds' volatility-timing seems to be country-specific. Most of the Japanese equity funds and global equity funds under CPF investment scheme are found to have the ability of volatility timing. This finding contrasts with the existing studies on Asian ex-Japan funds and Greater China funds. Moreover, there is no evidence that funds under CPF Investment Scheme show a better group performance of volatility timing.

AbstractThis paper examines the relationship between option trading activity and stock market volatility. Although the option market is uniquely suited for trading on volatility information, there is little analysis on how trading activity in this market is linked to stock price volatility. The bulk of the discussion tends to focus on whether trading activity in the stock market is informative about stock volatility. To analyze the information in option trading activity for stock market volatility, a sample of 15 stocks with the highest option trading volume is selected. For each stock, it is noted that the trading activities in the put and call option markets have significant explanatory power for stock market volatility. In addition, the results indicate that the call option trading activity has a stronger impact on stock volatility compared with that of the put options. Our results demonstrate that information and sentiment in the option market is useful for the estimation of stock market volatility. Also, the significance of the effects of option trading activity on stock price volatility is observed to be comparable to that of stock market trading activity. Furthermore, the persistence and asymmetric effects in the volatility of some stocks tend to disappear once option trading activity is taken into account.

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Motivations behind a country's importation of waste are categorized into the pollution haven hypothesis (PHH) and the resource hunting hypothesis (RHH). The importation of wastes can lead to environmental sustainability concerns, requiring governments to intervene when the market fails to reduce the negative externalities by strengthening and implementing environmental regulations. Motivated by China's position within a rapidly growing but environmentally damaging sector of trade, this paper has three goals: (1) to classify the primary hypothesis that governs China's flow of traded wastes; (2) to verify the heterogeneous impact of the pollution paradise motivation and resource demand motivation of waste imports from developed and developing countries, and across industries; (3) to assess the impact of domestic environmental regulations on the motives behind China's waste imports. Using 28 imported waste-varieties from 20 of China's major trade partners across 24 years, findings indicate that the flow of Chinese waste imports is relatively unresponsive under the pollution haven effect. However, the resource hunting effect from developing countries is significantly greater than what originates from developed countries, despite the laws of 2011 and 2017 established to restrict resource hunting activities. These results have important implications for improving the efficiency of China's waste sorting and recycling systems.

Most existing studies on forecasting exchange rates focus on predicting next-period returns. In contrast, this study takes the novel approach of forecasting and trading the longer-term trends (macro-cycles) of exchange rates. It proposes a unique hybrid forecast model consisting of linear regression, multilayer neural network, and combination models embedded with technical trading rules and economic fundamentals to predict the macro-cycles of the selected currencies and investigate the predicative power and market timing ability of the model. The results confirm that the combination model has a significant predictive power and market timing ability, and outperforms the benchmark models in terms of returns. The finding that the government bond yield differentials and CPI differentials are the important factors in exchange rate forecasts further implies that interest rate parity and PPP have strong influence on foreign exchange market participants.