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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 to…
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The horseshoe estimator for sparse signals

This paper proposes a new approach to sparsity, called the horseshoe estimator, which arises from a prior based on multivariate-normal scale mixtures. We describe the estimator's advantages over…
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Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables

We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of Pólya–Gamma distributions, which are constructed…
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The Impact of Jumps in Volatility and Returns

This paper examines continuous-time stochastic volatility models incorporating jumps in returns and volatility. We develop a likelihood-based estimation strategy and provide estimates of parameters,…
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Bayesian analysis of stochastic volatility models with fat-tails and correlated errors

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Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction

We study the classic problem of choosing a prior distribution for a location parameter β = (β1, . . . , βp) as p grows large. First, we study the standard “global-local shrinkage” approach, based on…
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Handling Sparsity via the Horseshoe

This paper presents a general, fully Bayesian framework for sparse supervised-learning problems based on the horseshoe prior, which is a member of the family of multivariate scale mixtures of normals and closely related to widely used approaches for sparse Bayesian learning.
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Particle Filtering

This chapter provides an overview of particle filters. Particle filters generate approximations to filtering distributions and are commonly used in non-linear and/or non-Gaussian state space models.…
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A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling

Abstract A solution to multivariate state-space modeling, forecasting, and smoothing is discussed. We allow for the possibilities of nonnormal errors and nonlinear functionals in the state equation,…
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A Bayesian analysis of the multinomial probit model with fully identified parameters

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