It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with .xed eects is inconsistent under .xed time series sample size (T) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in...

With the availability of ultra high frequency financial data, the task of finding an appropriate econometric model to describe the movement of financial variables at the tick-by-tick level has become an important goal in financial econometric research. The task has both theoretical and empirical...

In recent years there has been increased interest in using nonparametric methods to deal with various aspects of financial data. The paper by Fan overviews some nonparametric techniques that have been used in the financial econometric literature, focusing on estimation and inference for...

A recursive regression methodology is used to analyze the bubble characteristics of various financial time series during the subprime crisis. The methods provide a technology for identifying bubble behavior and consistent dating of their origination and collapse. Seven relevant financial series...

A new methodology is proposed to estimate theortical prices of financial contingent-claims whose values are dependent on some other underlying financial assets. In the literature the preferred choice of estimator is usually maximum likelihood (ML). ML has strong asymptotic justification but is...

This paper motivates and introduces a two-stage method of estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as developed in Jacod (1994) and Barndorff-Nielsen and Shephard (2002),...