Journal Article10.1017/S026646660999048X
Characteristic function–based testing for multifactor continuous-time markov models via nonparametric regression
Bin Chen,Yongmiao Hong +1 more
23
TL;DR: In this paper, a nonparametric regression-based goodness-of-fit test for multifactor continuous-time Markov models using the conditional characteristic function has been developed, which often has a convenient closed form or can be approximated accurately.
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Abstract: We develop a nonparametric regression-based goodness-of-fit test for multifactor continuous-time Markov models using the conditional characteristic function, which often has a convenient closed form or can be approximated accurately for many popular continuous-time Markov models in economics and finance An omnibus test fully utilizes the information in the joint conditional distribution of the underlying processes and hence has power against a vast class of continuous-time alternatives in the multifactor framework A class of easy-to-interpret diagnostic procedures is also proposed to gauge possible sources of model misspecification All the proposed test statistics have a convenient asymptotic N(0, 1) distribution under correct model specification, and all asymptotic results allow for some data-dependent bandwidth Simulations show that in finite samples, our tests have reasonable size, thanks to the dimension reduction in nonparametric regression, and good power against a variety of alternatives, including misspecifications in the joint dynamics, but the dynamics of each individual component is correctly specified This feature is not attainable by some existing tests A parametric bootstrap improves the finite-sample performance of proposed tests but with a higher computational cost
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