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

被引:885
作者
Antonakakis, Nikolaos [1 ,2 ]
Chatziantoniou, Ioannis [2 ]
Gabauer, David [1 ,3 ]
机构
[1] Webster Vienna Private Univ, Dept Business & Management, Praterstr 23, A-1020 Vienna, Austria
[2] Univ Portsmouth, Portsmouth Business Sch, Econ & Finance Subject Grp, Portland St, Portsmouth PO1 3DE, Hants, England
[3] Johannes Kepler Univ Linz, Inst Appl Stat, Altenbergerstr 69, A-4040 Linz, Austria
关键词
TVP-VAR; dynamic connectedness; Monte Carlo simulation; IMPULSE-RESPONSE ANALYSIS; VOLATILITY SPILLOVERS; CONFIDENCE-INTERVALS; EFFICIENT TESTS; UNCERTAINTY; TRANSMISSION; NORMALITY; CRISIS; RETURN;
D O I
10.3390/jrfm13040084
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yilmaz (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.
引用
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页数:23
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