Parametric and nonparametric models and methods in financial econometrics

TL;DR

This paper reviews parametric and nonparametric models and methods in financial econometrics, emphasizing dependence structures, estimation techniques, model validation, and large sample properties.

Contribution

It provides a comprehensive overview of models, dependence structures, validation techniques, and tools for large sample analysis in financial econometrics.

Findings

Detailed analysis of dependence structures in discrete samples

Comparison of various model validation techniques

Discussion on tools for large sample properties

Abstract

Financial econometrics has become an increasingly popular research field. In this paper we review a few parametric and nonparametric models and methods used in this area. After introducing several widely used continuous-time and discrete-time models, we study in detail dependence structures of discrete samples, including Markovian property, hidden Markovian structure, contaminated observations, and random samples. We then discuss several popular parametric and nonparametric estimation methods. To avoid model mis-specification, model validation plays a key role in financial modeling. We discuss several model validation techniques, including pseudo-likelihood ratio test, nonparametric curve regression based test, residuals based test, generalized likelihood ratio test, simultaneous confidence band construction, and density based test. Finally, we briefly touch on tools for studying large sample properties.