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Estimating the Recovery Rate of Stock Prices Using Hierarchical Bayesian Regession Models

In this article, we will fit a hierarchical regression model to the recovery rate and drop rate of stock prices, as well as EPS, price level and volume. A Bayesian approach is used to estimate the interested parameters of our model and predict the future value of the recovery rate. Both estimation and prediction are carried out by Gibbs Sampling algorithm. We will also do the convergence diagnosis by Gelman-Rubin diagnostic plots. The simulation result shows the Bayesian approach make our model and prediction more effective compared to classical least square method.