Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions
This work addresses the need for more accurate financial time-series prediction models, particularly for applications involving market volatility and heavy-tailed data, though it is incremental as it builds on existing DyBM frameworks.
The authors tackled the limitations of Gaussian Dynamic Boltzmann Machines (DyBM) in financial time-series prediction by extending it to handle time-varying variance and generalized Gaussian distributions, resulting in significant performance improvements.
Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is generated by a Gaussian distribution whose first-order moment (mean) dynamically changes over time but its second-order moment (variance) is fixed. However, in many financial applications, the assumption is quite limiting in two aspects. First, even when the data follows a Gaussian distribution, its variance may change over time. Such variance is also related to important temporal economic indicators such as the market volatility. Second, financial time-series data often requires learning datasets generated by the generalized Gaussian distribution with an additional shape parameter that is important to approximate heavy-tailed distributions. Addressing those aspects, we show how to extend DyBM that results in significant performance improvement in predicting financial time-series data.