Relative Efficiency of DCC Estimates via Different Algorithms

Abstract

Volatility plays an important role in capturing the variability in any series, hence multivariate volatility models helps us in figuring out the spillovers across variables. The observation of such spillovers plays a very crucial role in financial decision making. Starting from the pioneering works of [1] and [2] ARCH/ GARCH type models and their multivariate extensions are widely used for capturing volatility spillovers. Dynamic Conditional Correlation (DCC) model proposed by [3] is one of the most widely used multivariate GARCH model. As GARCH type models are non - linear in nature, hence their solutions vary across the algorithms used for their computation. Different algorithms are used in different software and thus give varying results. In this study, we are going to compute and compare DCC model estimates through different software. In our study SAS results would be considered as baseline estimates and the competing results would be obtained from Stata and R. We will model the DCC estimates between returns on NYSE and PSX indices data for the time period of September 2001 to August 2016.This study would help us to determine the relative efficiency and accuracy of algorithms used for estimating the DCC model.