UP STAT 2022 10th Joint Conference

1.  A Mixture of Heterogeneous Models with Dirichlet Time-Varying

Understanding stock market volatility is a major task for analysts, policy makers, and investors. Due to the complexity of stock market data, development of efficient models for predicting is far from trivial. In this work, we provide a way to analyze this kind of data using regression mixture models. We introduce a novel mixture of heterogeneous models with mixing weights characterized by an autoregressive structure. In comparison to the static mixture, the proposed model is based on time-dependent weights which allows one to learn whether the data-generating mechanism changes over time. A Bayesian approach based on MCMC algorithm is adopted. Through extensive analysis in both real and simulated data settings, we show the potential usefulness of our mixture model defined in a dynamic fashion over its static counterpart. We illustrate and compare their performance in the context of the stock market expectation of a 30-day forward-looking volatility expressed by Chicago Board Options Exchange's Volatility Index.

L'Université Laval