Robust Score and Portmanteau Tests of Volatility Spillover

49 Pages Posted: 12 Feb 2012 Last revised: 14 May 2013

See all articles by Mike Aguilar

Mike Aguilar

Duke University

Jonathan B. Hill

University of North Carolina (UNC) at Chapel Hill – Department of Economics

Date Written: April 20, 2013

Abstract

This paper presents a variety of tests of volatility spillover that are robust to heavy tails generated by large errors or GARCH-type feedback. The tests are couched in a general conditional heteroskedasticity framework with idiosyncratic shocks that are only required to have a finite variance if they are independent. We negligibly trim test equations, or components of the equations, and construct heavy tail robust score and portmanteau statistics. We develop the tail-trimmed sample correlation coefficient for robust inference, and prove that its Gaussian limit under the null hypothesis of no spillover has the same standardization irrespective of tail thickness. Further, if spillover occurs within a specified horizon, our test statistics obtain power of one asymptotically. A Monte Carlo study shows our tests provide significant improvements over extant GARCH-based tests of spillover, and we apply the tests to financial returns data.

Keywords: volatility spillover, heavy tails, tail trimming, robust inference

JEL Classification: C13, C20, C22

Suggested Citation

Aguilar, Mike and Hill, Jonathan B., Robust Score and Portmanteau Tests of Volatility Spillover (April 20, 2013). Available at SSRN: https://ssrn.com/abstract=2003387 or http://dx.doi.org/10.2139/ssrn.2003387

Mike Aguilar

Duke University ( email )

Durham, NC 27708
United States

Jonathan B. Hill (Contact Author)

University of North Carolina (UNC) at Chapel Hill – Department of Economics ( email )

102 Ridge Road
Chapel Hill, NC NC 27514
United States