Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions

Nikolaos Antonakakis,Ioannis Chatziantoniou,David Gabauer

doi:10.3390/jrfm13040084

Abstract

In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible changes in the underlying structure of the data in a more flexible and robust manner. Specifically, there is neither a need to arbitrarily set the rolling-window size nor a loss of observations in the calculation of the dynamic measures of connectedness, as no rolling-window analysis is involved. Given that the proposed framework rests on multivariate Kalman filters, it is less sensitive to outliers. Furthermore, we emphasise the merits of this approach by conducting Monte Carlo simulations. We put our framework into practice by investigating dynamic connectedness measures of the four most traded foreign exchange rates, comparing the TVP-VAR results to those obtained from three different rolling-window settings. Finally, we propose uncertainty measures for both TVP-VAR-based and rolling-window VAR-based dynamic connectedness measures.

Highlights

Investigating the propagation of financial crises into the economy has been at the epicenter of academic research in recent years, especially in the aftermath of the global financial crisis of 2007–2009
In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure
We find that our proposed TVP-VAR-based measure of connectedness is similar to the averaged dynamic connectedness measures of the rolling-window VAR model; TVP-VAR values immediately adjust to underlying events, while rolling-window-based estimates either overreact or smooth out the effect

Summary

Introduction

Investigating the propagation of financial crises into the economy has been at the epicenter of academic research in recent years, especially in the aftermath of the global financial crisis of 2007–2009. Crises are unpredictable; transmission mechanisms relating to financial turmoil do share certain similarities (Reinhart and Rogoff 2008). Researchers have developed elaborate methods aiming to capture transmission mechanisms that relate to such events. A notable empirical method is the one by Diebold and Yılmaz (2009, 2012, 2014) who introduced a variety of connectedness measures based on the notion of the forecast error variance decomposition that was derived from the rolling-window VARs. In the present study, we provide an extension to the Diebold and Yılmaz (2014) connectedness approach by applying a time-varying parameter vector autoregressive model (TVP-VAR) with a time-varying covariance structure, as opposed to the constant-parameter rolling-window VAR approach.. Alternative measures of connectedness have been provided by Baruník et al (2016, 2017), Baruník and Krehlík (2018), and Geraci and Gnabo (2018)

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Conclusion