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Bayesian Analysis of Stochastic Volatility Models
New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used toExpand
Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
Abstract The basic univariate stochastic volatility model specifies that conditional volatility follows a log-normal auto-regressive model with innovations assumed to be independent of theExpand
MCMC maximum likelihood for latent state models
Abstract This paper develops a pure simulation-based approach for computing maximum likelihood estimates in latent state variable models using Markov Chain Monte Carlo methods (MCMC). Our MCMCExpand
Optimal Portfolios in Good Times and Bad
Recent experience with emerging market investments and hedge funds has highlighted the fact that risk parameters are unstable. To address this problem, we introduce a procedure for identifyingExpand
Market Beta Dynamics and Portfolio Efficiency
This paper introduces a new estimation for the dynamics of betas. It combines two previously separate approaches in the literature, data-driven filters and parametric methods. Namely, we show how toExpand
Geometric or Arithmetic Mean: A Reconsideration
An unbiased forecast of the terminal value of a portfolio requires compounding of its initial value at its arithmetic mean return for the length of the investment period. Compounding at theExpand
Stochastic Volatility: Univariate and Multivariate Extensions
Discrete time stochastic volatility models (hereafter SVOL) are noticeably more difficult to estimate than the successful ARCH family of models. In this paper we demonstrate efficient estimation andExpand
Bayesian analysis of contingent claim model error
Abstract This paper formally incorporates parameter uncertainty and model error into the implementation of contingent claim models. We make hypotheses for the distribution of errors to allow the useExpand
Models and Priors for Multivariate Stochastic Volatility
Discrete time stochastic volatility models (hereafter SVOL) are noticeably harder to estimate than the successful ARCH family of models. In this paper, we develop methods for finite sample inference,Expand
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