Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms

• Date: 01 August 2020
• Published date: 01 August 2020
• Author: David Gabauer
• DOI: http://doi.org/10.1002/for.2648

Received: 21 May 2019 Revised: 30 October 2019 Accepted: 3 January 2020

DOI: 10.1002/for.2648

RESEARCH ARTICLE

Volatility impulse response analysis for DCC-GARCH

models: The role of volatility transmission mechanisms

David Gabauer

Institute of Applied Statistics, Johannes

Kepler University,Linz, Austria

Correspondence

Institute of Applied Statistics, Johannes

Kepler University,Altenbergerstraße 69,

Linz, Austria.

Email: david.gabauer@hotmail.com

Abstract

This study introduces volatility impulse response functions (VIRF) for dynamic

conditional correlation–generalized autoregressive conditional heteroskedastic-

ity (DCC-GARCH) models. In addition, the implications with respect to network

analysis—using the connectedness approach of Diebold and Y𝜄lmaz (Journal

of Econometrics, 2014, 182(1), 119–134)—is discussed. The main advantages

of this framework are (i) that the time-varying dynamics do not underlie a

rolling-window approach and (ii) that it allows us to test whether the propaga-

tion mechanism is time varying or not. An empirical analysis on the volatility

transmission mechanism across foreign exchange rate returnsis illustrated. The

results indicate that the Swiss franc and the euro are net transmitters of shocks,

whereas the British pound and the Japanese yen are net volatility receivers

of shocks. Finally, the findings suggest a high degree of comovement across

European currencies, which has important portfolio and risk management

implications.

KEYWORDS

volatility impulse response functions, volatility spillovers, variance decomposition, dynamic

connectedness, exchange rates

1INTRODUCTION

Recent global economic developments have revived inter-

est in propagation mechanisms that explain how economic

shocks spread internationally. The transmission of shocks

among economic entities is now becoming of major inter-

est and concern, given that the effects of the aftermath

of the Great Recession (2009) are still rippling through

the world economy. Hence investigating the transmission

mechanism is essential for policymakers to construct an

early-warning system revealing the most important trans-

mission channels.

That is why many researchers have developed method-

ologies in an attempt to capture this transmission process.

A notable study, among many, is by (Diebold & Yilmaz,

2009, 2012, 2014), who introduce a dynamic connect-

edness procedure based on the notion of forecast error

variance decomposition from vector autoregressions

(VARs). This VAR-based network methodology has

already attracted significant attention in the economic lit-

erature, and has been applied to the investigation of issues

such as stock market interdependencies, business cycle

synchronization, and volatility spillovers (see, among

others,Antonakakis & Gabauer, 2017; Antonakakis,

Gabauer, Gupta, & Plakandaras, 2018; Baruník, Koˇ

cenda,

& Vácha, 2016; Bubák, Koˇ

cenda, & Žikeš, 2011; Corbet,

Meegan, Larkin, Lucey, & Yarovaya, 2018; Demirer,

Diebold, Liu, & Yilmaz, 2018; Gabauer & Gupta, 2018;

Greenwood-Nimmo, Nguyen, & Shin, 2015; Klößner &

Wagner, 2014; Wiesen, Beaumont, Norrbin, & Srivastava,

2018; Zhang & Broadstock, 2018).

This is an open access article under the terms of the Creative Commons AttributionLicense, which permits use, distribution and reproduction in any medium, providedthe

original work is properly cited.

© 2020 The Authors. Journal of Forecasting published by John Wiley & Sons Ltd

Journal of Forecasting. 2020;39:788–796.

wileyonlinelibrary.com/journal/for788