Gaussian Process Volatility Model
This work addresses the need for more accurate and flexible variance prediction in financial modeling, though it appears incremental as it builds on existing Gaussian Process methods.
The authors tackled the problem of predicting time-changing variances in financial data by introducing a Gaussian Process Volatility Model, which showed significant improvement in predictive performance over standard models.
The accurate 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 variances. Moreover, function parameters are usually learned using maximum likelihood, which can lead to overfitting. To address these problems we introduce a novel model for time-changing variances using Gaussian Processes. A Gaussian Process (GP) defines a distribution over functions, which allows us to capture highly flexible functional relationships for the variances. In addition, we develop an online algorithm to perform inference. The algorithm has two main advantages. First, it takes a Bayesian approach, thereby avoiding overfitting. Second, it is much quicker than current offline inference procedures. Finally, our new model was evaluated on financial data and showed significant improvement in predictive performance over current standard models.