Model Free Inference on Multivariate Time Series with Conditional Correlations

Abstract

New results on volatility modeling and forecasting are presented based on the NoVaS transformation approach. Our main contribution is that we extend the NoVaS methodology to modeling and forecasting conditional correlation, thus allowing NoVaS to work in a multivariate setting as well. We present exact results on the use of univariate transformations and on their combination for joint modeling of the conditional correlations: we show how the NoVaS transformed series can be combined and the likelihood function of the product can be expressed explicitly, thus allowing for optimization and correlation modeling. While this keeps the original “model-free” spirit of NoVaS it also makes the new multivariate NoVaS approach for correlations “semi-parametric”, which is why we introduce an alternative using cross validation. We also present a number of auxiliary results regarding the empirical implementation of NoVaS based on different criteria for distributional matching. We illustrate our findings using simulated and real-world data, and evaluate our methodology in the context of portfolio management.

A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

“…Research on the correlation [6] between different entities could help improve the efficiency of research targets. For example, we could obtain the composition of distributed resource services by researching the correlation between resource services from different organizations to improve resource utilization [7].…”