Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent work. Dynamic panel data models have become increasingly popular in macroeconomics to study common relationships across countries or regions. This paper estimates dynamic panel...

There was a discussion in the years 1964-66 in this journal about the best way to estimate growth rates. Inferential and descriptive methods were carefully carried out and discussed but the opinion of the protagonists remained different. This gap between thinking about theoretical and...

The purpose of this paper is to introduce a stochastic volatility model for option pricing that exhibits Lévy jump behavior. For this model, we derive the general formula for a European call option. A well known particular case of this class of models is the Bates model, for which the jumps are...

We provide a new framework for estimating the systematic and idiosyncratic jump tail risks in financial asset prices. Our estimates are based on in-fill asymptotics for directly identifying the jumps, together with Extreme Value Theory (EVT) approximations and methods-of-moments for assessing...

This paper proposes and calibrates a consistent multi-factor affine term structure mortality model for longevity risk applications. We show that this model is appropriate for fitting historical mortality rates. Without traded mortality instruments the choice of risk-neutral measure is not unique...

The aim of these notes is to revisit sequential Monte Carlo (SMC) sampling. SMC sampling is a powerful simulation tool for solving non-linear and/or non-Gaussian state space models. We illustrate this with several examples.

In this paper, we describe and compare two simulated Maximum Likelihood estimation methods for a basic stochastic volatility model. For both methods, the likelihood function is estimated using importance sampling techniques. Based on a Monte Carlo study, we assess which method is more effective....

In this paper, we describe and compare two simulated Maximum Likelihood estimation methods for a basic stochastic volatility model. For both methods, the likelihood function is estimated using importance sampling techniques. Based on a Monte Carlo study, we assess which method is more effective....

This paper investigates the time-varying behavior of systematic risk for 18 pan-European sectors. Using weekly data over the period 1987-2005, six different modeling techniques in addition to the standard constant coefficient model are employed: a bivariate t-GARCH(1,1) model, two Kalman filter...