BFS 2002

Poster Presentation

We propose two nonparametric transition density-based specification tests for continuous-time diffusion models. By introducing an appropriate data transform and correcting the boundary bias of kernel estimators, our tests are robust to persistent dependence in the data and provide reliable inferences for sample sizes often encountered in empirical finance. Simulation studies show that our tests have reasonable size and good power against a variety of alternatives in finite samples even for data with highly persistent dependence. Besides the single-factor diffusion models, our tests can be applied to a broad class of dynamic economic models, such as discrete time series models, time-inhomogeneous diffusion models, stochastic volatility models, jump-diffusion models, and multi-factor term structure models. When applied to daily Eurodollar interest rates, our tests overwhelmingly reject some popular spot rate models, including single-factor diffusion models, GARCH models, regime-swtiching models and jump-diffusion models.