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We propose a dynamically consistent framework that allows joint valuation and estimation of stock options and credit default swaps written on the same reference company. We model default as controlled by a Cox process with a stochastic arrival rate. When default occurs, the stock price drops to...
This article proposes a new Bayesian Markov chain Monte Carlo (MCMC) methodology for estimation of a wide class of multidimensional jump-diffusion models. Our approach is based on the closed-form (CF) likelihood approximations of At-Sahalia(2002,2008). The CF likelihood approximation does not...
This paper presents a Markov chain Monte Carlo (MCMC)-based estimation procedure for the COGARCH(1,1) model driven by a compound Poisson process. The COGARCH model is a continuous-time analogue to the discrete-time GARCH model and captures many of the stylized facts of financial time series, as...
Abstract We provide a Bayesian analysis of pair-copula constructions (PCCs) (Aas et al., 2009), which outperform many other multivariate copula constructions in modeling dependencies in financial data. We use bivariate t-copulas as building blocks in a PCC to allow extreme events in bivariate...
In this paper, we show the effects that outliers have on estimation and inference for autoregressive conditional heteroskedasticity (ARCH) models. We propose for a wide class of ARCH models commonly estimated, an empirically tractable solution to this problem by replacing outliers with their...
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