Yue Wu
now at University of Washington
Publications
Gaussian Process Volatility Model
Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani, December 2014. (In Advances in Neural Information Processing Systems 28). Montreal, Canada.
Abstract▼ URL
The prediction of time-changing variances is an important task in the modeling of financial data. Standard econometric models are often limited as they assume rigid functional relationships for the evolution of the variance. Moreover, functional parameters are usually learned by maximum likelihood, which can lead to overfitting. To address these problems we introduce GP-Vol, a novel non-parametric model for time-changing variances based on Gaussian Processes. This new model can capture highly flexible functional relationships for the variances. Furthermore, we introduce a new online algorithm for fast inference in GP-Vol. This method is much faster than current offline inference procedures and it avoids overfitting problems by following a fully Bayesian approach. Experiments with financial data show that GP-Vol performs significantly better than current standard alternatives.
Dynamic Covariance Models for Multivariate Financial Time Series
Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani, June 2013. (In 30th International Conference on Machine Learning). Atlanta, Georgia, USA.
Abstract▼ URL
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.