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Motivated by the popularity of 'Run away from Beijing, Shanghai, and Guangzhou' saying in China this paper tests the determinants of subjective well-being (SWB) of long-term migrants and the extent to which migrants make optimal locational decisions in terms of SWB. China's cities are divided into 4 core first-tier cities, 15 new first-tier cities, and non-first-tier cities (94 in the sample) reflecting different levels of economic opportunity and amenities. Migrants can only be observed in their present location. Hence, we use a semi-parametric marginal treatment effect method to compare actual and counterfactual SWB outcomes. Results shows that migrants' SWB is greater in non-first-tier cities than first-tier cities; the determinants of SWB differ for male and female migrants; and, female migrants with urban hukou are typically better off in the four core first-tier cities as well as other first-tier cities. Overall, there is thus a marked gender differences in migrants' optimal locational choice. From a productivity and social cohesion perspective, these results make it imperative that public policy in China addresses the gender and spatial sorting imbalances that arises from differences in SWB determinants and optimal location as ever more educated young people join the labour force. [ABSTRACT FROM AUTHOR]

This paper examines whether the market-making system helps to improve the price discovery ability of New Third Board (NTB) market in China. We first estimate the time-varying coefficients error correction models, then apply common factor weight method to quantify the time-varying price discovery contributions, and finally explore the impacts of trading volume and volatility to price discovery contributions. Empirical results show that both markets have time-varying characteristic in terms of the magnitudes and directions of the equilibrium price adjustment due to error correction term. The Shanghai Composite Index, SZSE Component Index, and SME Index are found to lead in price discovery, while NTB exhibits the leadership on the GEM Index. Volume and volatility have significant influence on the price discovery contribution. The NTB contribution is positively related to its own trading activity, negatively related to the trading activity of Shanghai and Shenzhen stock markets, while negatively correlated with the volatility of both markets. In comparison, trading activity of SZSE Component Index and volatility of GEM Index have the greatest negative impacts on the contribution of NTB market. As an important part of China's multi-level capital market, the pricing mechanism of the NTB market needs further to be improved. [ABSTRACT FROM AUTHOR]

This article proposes a general framework of a Markov Switching GARCH model with a mixture of truncated Gaussian to model asset returns with price limits in China. Theoretically, while retaining many convenient statistical properties of the Gaussian distribution, the proposed model also assumes a flexible time-varying volatility structure to accommodate the feature of the return data under price restrictions in China, such as the clusters near the bounds (due to the 'bound effect'). Empirically, we apply the model to eight representative stocks from Shanghai and Shenzhen stock markets in China. Lastly, we find that our proposed model dominates the conventional volatility models in terms of Value-at-Risk measures. [ABSTRACT FROM AUTHOR]