This paper introduces cointegrating mixed data sampling (CoMiDaS) regressions, generalizing nonlinear MiDaS regressions in the extant literature. Under a linear mixed-frequency data-generating process, MiDaS regressions provide a parsimoniously parameterized nonlinear alternative when the linear forecasting model is over-parameterized and may be infeasible. In spite of potential correlation of ...

We model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study the contemporaneous and intertemporal relationship between expected returns and risk in a flexible statistical framework and without relying on exogenous predictors. We find a strong and robust negative correlation between the innovations to the conditional moments that leads t...

One important contribution has been made by Benoit Mandelbrot, the famous father of fractals who proposed a multi-fractal model of asset returns (MMAR), a theory which inherits all the hallmarks of Mandelbrot’s earlier work that has emerged since the 1970s. As a new formalization of stochastic models for the volatility dynamics of asset prices, it preserves the hierarchical multiplicative struc...

This paper motivates and introduces a two-stage method for 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 recently developed in Barndorff-Nielsen and Shephard (2002), to provide a regression model for estimating the parameters in the diffusion function. In the second stage t...

A two-step generalized method of moments estimation procedure can be made robust to heteroskedasticity and autocorrelation in the data by using a nonparametric estimator of the optimal weighting matrix. This paper addresses the issue of choosing the corresponding smoothing parameter (or bandwidth) so that the resulting point estimate is optimal in a certain sense. We derive an asymptotically op...

This paper develops a new framework and tools, and reexamines Fama-French regressions. For Fama-French portfolios, we consider a continuous-time factor model with a specific error component structure implied by the underlying asset pricing theory. The model is then analyzed as a continuous-time multivariate regression with a general martingale differential error, allowing for time-varying and s...

The aim of this study is to develop a bias-correction method for realized variance (RV) estimation, where the equilibrium price process is contaminated with market microstructure noise, such as bid-ask bounces and price changes discreteness. Though RV constitutes the simplest estimator of daily integrated variance, it remains strongly biased and many estimators proposed in previous studies requ...

Panel data in many econometric applications exhibit a nested (hierarchical) structure. For example, data on "rms may be grouped by industry, or data on air pollution may be grouped by observation station within a city, city within a country, and by country. In these cases, one can control for unobserved group and sub-group e!ects using a nested-error component model. A double-nested unbalanced ...

In parametric models a su cient condition for local identi cation is that the vector of moment conditions is di erentiable at the true parameter with full rank derivative matrix. We show that there are corresponding su cient conditions for nonparametric models. A nonparametric rank condition and di erentiability of the moment conditions with respect to a certain norm imply local identi cation. ...

The objective of this paper is to identify and estimate network formation models using observed data on network structure. We characterize network formation as a simultaneous-move game, where the utility from forming a link depends on the structure of the network, thereby generating strategic interactions between links. Because a unique equilibrium may not exist, the parameters are not necessar...