How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study |
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| Affiliation: | 1. University of Portsmouth, Economics and Finance Subject Group, Portsmouth Business School, Portland Street, Portsmouth PO1 3DE, United Kingdom;2. Webster Vienna Private University, Palais Wenkhaim, Praterstrasse 23, 1020 Vienna, Austria;3. Vienna University of Economics and Business, Department of Economics, Institute for International Economics, Welthandelsplatz 1, 1020 Vienna, Austria;4. Austrian Institute of Economic Research (WIFO), Arsenal, Objekt 20, A-1030 Vienna, Austria;1. Department of Finance and Accounting, University of Tunis El Manar, B.P.248, C.P. 2092 Tunis Cedex, Tunisia;2. Department of Economics and Finance, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman;3. International Finance Group Tunisia (IFGT), University of Tunis el Manar, Tunisia;4. Department of Finance and Accounting, Institut Supérieur de Comptabilité et d''Adminitration des Entreprises, université de la Manouba, Tunisia;5. Department of Business Administration, Pusan National University, Busan 609-735, Republic of Korea |
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| Abstract: | This paper replicates the Diebold and Yilmaz (2012) study on the connectedness of the commodity market and three other financial markets: the stock market, the bond market, and the FX market, based on the Generalized Forecast Error Variance Decomposition, GEFVD. We show that the net spillover indices (of directional connectedness), used to assess the net contribution of one market to overall risk in the system, are sensitive to the normalization scheme applied to the GEFVD. We show that, considering data generating processes characterized by different degrees of persistence and covariance, a scalar-based normalization of the Generalized Forecast Error Variance Decomposition is preferable to the row normalization suggested by Diebold and Yilmaz since it yields net spillovers free of sign and ranking errors. |
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| Keywords: | Causality Normalization schemes Generalized forecast error variance decomposition Spillover Simulation Vector autoregression models |
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