Predicting market crashes: a bayesian approach with high-frequency data

Abstract

Market crashes are harbingers of economic turmoil, yet their sudden and devastating nature poses a significant challenge to financial prediction. While traditional models often struggle to capture the complex dynamics leading to a crash, high-frequency data offers a rich tapestry of information with the potential to improve forecasting accuracy. This paper proposes a novel Bayesian approach that leverages high-frequency data to predict market crashes. We utilize a hierarchical framework that incorporates both time-series and cross-sectional information, effectively capturing the intricate interplay between individual asset movements and the overall market sentiment. By employing advanced Bayesian techniques, we overcome the inherent challenges associated with high-dimensional data and parameter estimation. Our approach is evaluated on a comprehensive dataset encompassing various asset classes and historical crashes. The results demonstrate a significant improvement in crash prediction accuracy compared to benchmark models, highlighting the efficacy of the Bayesian framework and the power of high-frequency data in elucidating pre-crash dynamics.