This paper details particle Markov chain Monte Carlo (PMCMC) techniques for analysis of unobserved component time series models using several economic data sets. PMCMC provides a very compelling, computationally fast and efficient framework for estimation and model comparison. For instance, we...

As hedge fund replication based on factor models has encountered growing interest among professionals and academics, and despite the launch of numerous products (indexes and mutual funds) in the past year, it faced many critics. In this paper, we consider three of the main critiques, namely the...

This paper explores the role of consumption habits using an estimated nonlinear dynamic stochastic general equilibrium (DSGE) model with heteroscedastic shocks. It finds that habits interact with time-varying volatility to produce a better and more plausible fit to the data. They accentuate the...

In the Schumpeterian creative disruption age, the authors firmly believe that an increasing application of electronic technologies in the finances opens a big number of new unlimited opportunities toward a new era of the ultra high frequency electronic trading in the foreign currencies exchange...

This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation...

In the Schumpeterian creative disruption age, the authors firmly believe that an increasing application of electronic technologies in the finances opens a big number of new unlimited opportunities toward a new era of the ultra high frequency electronic trading in the foreign currencies exchange...

This article describes a new approximation method for dynamic stochastic general equilibrium (DSGE) models. The method allows nonlinear models to be estimated efficiently and relatively quickly with the fully-adapted particle filter. The article demonstrates the method by estimating, on US data,...

Given a random sample from a parametric model, we show how indirect inference estimators based on appropriate nonparametric density estimators (i.e., simulation-based minimum distance estimators) can be constructed that, under mild assumptions, are asymptotically normal with variance-covarince...