文章基本信息

标题：Model Free Inference on Multivariate Time Series with Conditional Correlations
本地全文：下载
作者：Dimitrios Thomakos ; Johannes Klepsch ; Dimitris N. Politis 等
期刊名称：Stats
电子版ISSN：2571-905X
出版年度：2020
卷号：3
期号：4
页码：484-509
DOI：10.3390/stats3040031
出版社：MDPI AG
摘要：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.