We develop a nonparametric specification test for continuous-time models using the transition density. Using a data transform and correcting for boundary bias of kernel estimators, our test is robust to serial dependence in data and provides excellent finite sample performance. Besides univariate diffusion models, our test is applicable to a wide variety of continuous-time and discretetime dynamic models, including time-inhomogeneous diffusion, GARCH, stochastic volatility, regimeswitching,jump-diffusion, and multivariate diffusion models. A class of separate inference procedures is also proposed to help gauge possible sources of model misspecification. We strongly reject a variety of univariate diffusion models for daily Eurodollar spot rates and some popular multivariate affine term structure models for monthly U.S. Treasury yields.

Technical traders base their analysis on the premise that the patterns in market prices are assumed to recur in the future, and thus, these patterns can be used for predictive purposes. This paper tests the simplest and most popular trading rules―moving average―in the Chinese stock market. Overall, our results are similar to the ones of Brock et al. (1992) and Lo et al. (2000), providing strong support for the technical strategies. In fact, technical indicators do provide incremental information, and buy signals consistently generate higher returns than sell signals. We also find that the asymmetric phenomenon between the buy and sell signals, and we attributed it to the investors’ behaviors.