DeepAI AI Chat
Log In Sign Up

Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions

12/17/2017
by   Rudy Raymond, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

10/20/2022

A Semiparametric Approach to the Detection of Change-points in Volatility Dynamics of Financial Data

One of the most important features of financial time series data is vola...
03/10/2021

Stationary subspace analysis based on second-order statistics

In stationary subspace analysis (SSA) one assumes that the observable p-...
06/03/2020

A New Look to Three-Factor Fama-French Regression Model using Sample Innovations

The Fama-French model is widely used in assessing the portfolio's perfor...
09/19/2018

Parameter Estimation of Heavy-Tailed AR Model with Missing Data via Stochastic EM

The autoregressive (AR) model is a widely used model to understand time ...
03/04/2021

Financial Application of Extended Residual Coherence

Residual coherence is a graphical tool for selecting potential second-or...
12/15/2020

Proofs and additional experiments on Second order techniques for learning time-series with structural breaks

We provide complete proofs of the lemmas about the properties of the reg...
09/28/2020

A General Bayesian Model for Heteroskedastic Data with Fully Conjugate Full-Conditional Distributions

Models for heteroskedastic data are relevant in a wide variety of applic...