The paper studies a factor GARCH model and develops test procedures which can be used to test the number of factors needed to model the conditional heteroskedasticity in the considered time series vector. Assuming normally distributed errors the parameters of the model can be straightforwardly estimated by the method of maximum likelihood. Inefficient but computationally simple preliminary esti...

This paper studies belief heterogeneity in a benchmark competitive asset market: a market for Arrow-Debreu securities. We show that differences in agents’ beliefs lead to a systematic pricing pattern, the favorite longshot bias (FLB): securities with a low payout probability are overpriced while securities with high probability payout are underpriced. We apply demand estimation techniques to be...

This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and suggests the use of devolatized returns computed as returns standardized by realized volatilities rather than by GARCH type volatility estimates. The t-DCC estimation procedure is applied to a portfolio of daily returns on currency futures, government bonds and...

This paper develops a real-time structural model of price formation, and uses it to investigate the dynamics of effective quotes and bid-ask spreads between consecutive trades. There is some evidence that the effective bid-ask spreads increase over time when no orders arrive. The effective quotes are found to change over time when no orders arrive. The dynamics of bid and ask are different, and...

The goal of this paper is to provide a comprehensive empirical analysis of majority rule and Tiebout sorting within a system of local jurisdictions. The idea behind the estimation procedure is to investigate whether observed levels of public expenditures satisfy necessary conditions implied by majority rule in a general equilibrium model of residential choice. The estimator controls for observe...

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new methodology for estimating factors from large datasets based on state space models, discuss its theoretical properties and compare its performance with that of two alternative estimation approaches ...

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...

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...

This paper provides a simple unified framework for assessing the empirical linkages between returns and realized and implied volatilities. First, we show that whereas the volatility feedback effect as measured by the sign of the correlation between contemporaneous return and realized volatility depends importantly on the underlying structural model parameters, the correlation between return and...

In this paper we discuss a general framework for analyzing labor supply behavior in the presence of complicated budgetand quantity constraints of which some may be unobservable. The point of departure is that an individual’s labor supply decision can be considered as a choice from a set of discrete alternatives (jobs). These jobs are characterized by attributes such as hours of work, sector spe...