The speculative nature of both stock and housing markets in China has attracted the attention of observers. However, while stock market data are easily available, the low frequency and low quality of publicly available housing price data hampers the study of the relationship between the two markets. We use original hedonic weekly resale housing prices of a major Chinese housing market and study them in conjunction with Shanghai's stock market index in the second half of the 2000s. The use of the Phillips et al. (2015 a,b) recursive explosive-root test enables us to detect and date speculative episodes in both markets. We then implement the Greenaway-McGrevy and Phillips (2016) methodology to detect the presence of migration between the two types of bubbles. We detect significant migration from the stock to the housing market bubble in 2009 and a temporary spillover in 2007.

During the past few years, monetary policy communication has become a hot topic in as far as it seems to have become a very relevant way for central banks to guide markets, beyond actual monetary policy decisions. This paper investigates this issue empirically for the case of Chile. More specifically, using data from 2005 to 2014 and a Component GARCH model, we assess whether changes in the communication of the Central Bank of Chile generates in particular a permanent or temporary change in the volatility of interest rates, after controlling for changes in monetary policy instruments. Our results show that the volatility in interest rate futures in Chile’s swap markets increases following the Central Bank’s communication. However, the impact tends to be temporary instead of permanent and only statistically significant in the pre-crisis period. All in all, our results indicate a reduced relevance of Central Bank’s communication for short term swap markets which may reflect that market participants have learned to anticipate changes in monetary policy communication, especially after the global financial crisis.

Traditional beta is only a linear measure of overall market risk and places equal emphasis on upside and downside risks, but actually the latter is always much stronger probably due to the trading mechanism like short-sale constraints. Therefore, this paper employs the nonlinear measure, tail dependence, to measure the extreme downside risks that individual stocks crash together with the whole market and investigates whether such tail dependence risks will affect stock returns. Our empirical evidence based on Shanghai A shares confirms that most stocks display nonnegligible tail dependence with the whole market, and, more importantly, such tail dependence risks can indeed provide additional information beyond beta and other factors for asset pricing. In cross-sectional regression, it is proved that this tail dependence does help to explain monthly returns on Shanghai A shares, whereas the time-series regression further indicates that mimicking portfolio returns for tail dependence can capture strong common variation of Shanghai A stock returns.

This paper takes a two-stage estimation approach to investigate the direct and indirect determinants of the capacity of power supply in China, with reference to the Organization for Economic Cooperation and Development countries. In the first stage we investigate the determinants of demand for electric consumption and in the second stage we test the impact of demand for consumption on capacity. Our study shows that the direct impact on capacity growth is mainly of GDP growth, which is a China-specific effect, and load factor, which is a non-China specific effect. Capacity investment is driven by the demand for power relative to the utilization of existing capacity. Furthermore, power prices and the industrial structure of an economy are the indirect determinants of capacity through their impacts on demand. The industrial structure has a strong influence on the power demand in China, since the country has accelerated its industrialization with more investment in heavy industry that further fuels the demand for power and therefore supply capacity.