2013/05/18 by Yue Wu, José Miguel Hernández-Lobato, Wu, Yue +3
Computer Science · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Machine Learning (stat.ML) #Methodology (stat.ME) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.1305.4268
openalex publication_date 2013/05/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.