This study reconsiders the timing of the equity risk factors by using the flexible neural networks specified for image recognition to determine the timing weights. The performance of each factor is visualized to be standardized price and volatility charts and `learned' by flexible image recognition methods with timing weights as outputs. The performance of all groups of factors can be significantly improved by using these ``deep learning--based'' timing weights. In addition, visualizing the volatility of factors and using deep learning methods to predict volatility can significantly improve the performance of the volatility-managed portfolio for most categories of factors. Our further investigation reveals that the timing success of our method hinges on its ability in identifying ex ante regime switches such as jumps and crashes of the factors and its predictability on future macroeconomic risk.

In this paper we use the new developed Markov Switching Unit Root test to examine the status of real and financial integration of China, Japan, the European Union, and the United States based on the empirical validity of real interest parity, uncovered interest parity, and relative purchasing power parity. We found strong evidence in favour of those parity conditions and hence concluded that real and financial integration between China and other four countries was well established.

A government policy aimed at the reduction of state shares in state-owned enterprises (SOE) triggered a crash in Chinas stock market. The sustained depression and spillover even after the policy adjustments were over constitute a puzzle the so-called state-share paradox. The empirical study finds evidence in two dimensions. First, a regime switching model with an absorbing state suggests that government policy switches the regime to liquidity black holes. Second, there is no evidence of light-to-liquidity during the crash, suggesting to model the crash as an aggregate phenomenon of the whole market. To carefully match the evidence, a theoretical model is set up within the framework of market microstructure. The state-share paradox is not a simply instance of news-driven crash. The model shows that Chinas stock market has distinctive features of liquidity production and price discovery. The irregularities of a representative liquidity supporter generate an inverted-S demand curve and give rise to potential liquidity black holes. Multiple equilibria and the resulting large drop in prices arise from supply dynamics of short-run investors, who buy the stock from the primary market liquidate their long positions in the secondary market. This study contributes a rational panics hypothesis to the literature. The rational panics hypothesis is neither an rational model with noise traders, nor a standard rational expectation model under the asymmetric information framework. It is based on homogeneous agents with incomplete information, and is consistent with the evidence of absorbing regime switching and the recent literature on state-dependent preference. Our findings have larger implications for ine¢ ciency of Chinas stock market.

A government policy aimed at the reduction of state shares in state-owned enterprises (SOE) triggered a crash in the Chinese stock market. The sustained depression and spillover even after the policy adjustments were over constitute a puzzle---the so called "state-share paradox". The empirical study finds evidence in two dimensions. First, a regime switching model with an absorbing state suggests that government policy switches the regime to liquidity black holes. Second, there is no evidence of flight-to-liquidity during the crash, suggesting to model the crash as an aggregate phenomenon of the whole market. To carefully match the evidence, a theoretical model is set up within the framework of market microstructure. The model shows that the Chinese stock market has distinctive features of liquidity production and price discovery. The irregularities generate an inverted-S demand curve, gives rise to potential liquidity black holes, and are key features to explain the state-share paradox. This study contributes a rational panics hypothesis to the literature. The rational panics hypothesis is neither a herding model with or without behavioral assumptions, nor a standard rational expectation model under the asymmetric information framework. It is based on homogeneous agents with incomplete information, and is consistent with the evidence of absorbing regime switching and the recent literature on state-dependent preference. Our findings have larger implications for theoretical modeling and policy design.

We provide a comprehensive analysis of the out-of-sample performance of a wide variety of spot rate models in forecasting the probability density of future interest rates. While the most parsimonious models perform best in forecasting the conditional mean of many financial time series, we find that the spot rate models that incorporate conditional heteroskedasticity and excess kurtosis or heavy-tails have better density forecasts. GARCH significantly improves the modeling of the conditional variance and kurtosis, while regime switching and jumps improve the modeling of the marginal density of interest rates. Our analysis shows that the sophisticated spot rate models in the existing literature are important for applications involving density forecasts of interest rates.

We propose two nonparametric transition density-based speciÞcation tests for continuous-time models. Unlike the marginal density used in the literature, the transition density can capture the full dynamics of a continuous-time process. To address the concerns of the Þnite sample perfor- mance of nonparametric methods in the literature, we introduce an appropriate data transfor- mation and correct the boundary bias of kernel estimators. As a result, our tests are robust to persistent dependence in data and provide reliable inferences for sample sizes often encountered in empirical Þnance. Simulation studies show that even for data with highly persistent depen- dence, our tests have reasonable size and good power against a variety of alternatives in Þnite samples. Besides one-factor diffusion models, our tests can be applied to a broad class of dynamic models, including discrete-time dynamic models, time-inhomogeneous diffusion models, stochas- tic volatility models, jump-diffusion models, and multi-factor diffusion models. When applied to Eurodollar interest rates, our tests overwhelmingly reject a variety of popular one-factor diffusion models. We Þnd that introducing nonlinear drift does not signiÞcantly improve the goodness of Þt, and the main reason for the rejection of one-factor diffusion models is the violation of the Markov assumption. Some popular non-Markovian models with GARCH, regime switching and jumps perform signiÞcantly better than one-factor diffusion models, but they are still far from being adequate to fully capture the interest rate dynamics. Our study shows that, contrary to the general perception in the literature, nonparametric methods are a reliable and powerful tool for analyzing Þnancial data.