Dynamic Dependence Modeling in financial time series

TL;DR

This paper introduces two novel methods for dynamic dependence modeling in financial time series, compares their performance with existing techniques, and demonstrates their effectiveness in risk measurement for major stock indices.

Contribution

The paper proposes Accelerated Moving Window and Bottom-up methods for detecting copula changes, advancing dynamic dependence modeling in finance.

Findings

The new methods outperform existing techniques in simulated data.

Dynamic modeling improves risk measurement accuracy.

Application to stock indices validates practical utility.

Abstract

This paper explores the dependence modeling of financial assets in a dynamic way and its critical role in measuring risk. Two new methods, called Accelerated Moving Window method and Bottom-up method are proposed to detect the change of copula. The performance of these two methods together with Binary Segmentation \cite{vostrikova1981detection} and Moving Window method \cite{guegan2009forecasting} is compared based on simulated data. The best-performing method is applied to Standard \& Poor 500 and Nasdaq indices. Value-at-Risk and Expected Shortfall are computed from the dynamic and the static model respectively to illustrate the effectiveness of the best method as well as the importance of dynamic dependence modeling through backtesting.