This paper introduces a new family of Bayesian semi-parametric models for the conditional distribution of daily stock index returns. The proposed models capture key stylized facts of such returns, namely heavy tails, asymmetry, volatility clustering, and leverage. A Bayesian nonparametric prior...

This paper presents a method for Bayesian nonparametric analysis of the return distribution in a stochastic volatility model. The distribution of the logarithm of the squared return is flexibly modelled using an infinite mixture of Normal distributions. This allows efficient Markov chain Monte...

We consider jointly modelling a finite collection of quantiles over time under a Bayesian nonparametric framework. Formal Bayesian inference on quantiles is challenging since we need access to both the quantile function and the likelihood (which is given by the derivative of the inverse quantile...

We explore time variation in the shape of the conditional return distribution using a model of multiple quantiles. We propose a joint model of scale (proxied by the interquartile range) and other quantiles standardised by the scale. The model allows us to capture the scale and shape of the...