How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study
•This paper replicates the Diebold and Yilmaz, DY, (2012) study on financial markets connectedness.•The markets are the commodity and the stock, bond, FX for the US.•Similar to DY, we use use the Generalized Forecast Error Variance Decomposition, GEFVD.•We compare normalization schemes to GEVD.•We s...
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| Veröffentlicht in: | Energy economics 2019-10, Vol.84, p.104536, Article 104536 |
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| creator | Caloia, Francesco Giuseppe Cipollini, Andrea Muzzioli, Silvia |
| description | •This paper replicates the Diebold and Yilmaz, DY, (2012) study on financial markets connectedness.•The markets are the commodity and the stock, bond, FX for the US.•Similar to DY, we use use the Generalized Forecast Error Variance Decomposition, GEFVD.•We compare normalization schemes to GEVD.•We show that a scalar-based normalization is preferable to the row normalization suggested by DY.
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. |
| doi_str_mv | 10.1016/j.eneco.2019.104536 |
| format | Article |
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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.</description><identifier>ISSN: 0140-9883</identifier><identifier>EISSN: 1873-6181</identifier><identifier>DOI: 10.1016/j.eneco.2019.104536</identifier><language>eng</language><publisher>Kidlington: Elsevier B.V</publisher><subject>Analysis of covariance ; Bond markets ; Bonds ; Causality ; Commodity markets ; Connectedness ; Covariance ; Decomposition ; Economic forecasting ; Energy economics ; Errors ; Generalized forecast error variance decomposition ; Normalization ; Normalization schemes ; Ratings & rankings ; Securities markets ; Simulation ; Spillover ; Stock exchanges ; Variance ; Vector autoregression models</subject><ispartof>Energy economics, 2019-10, Vol.84, p.104536, Article 104536</ispartof><rights>2019 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. Oct 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c507t-c2649153b9c61c3eebd579679b77f4b37fabd8676d58d3f3cc858dbe51251cea3</citedby><cites>FETCH-LOGICAL-c507t-c2649153b9c61c3eebd579679b77f4b37fabd8676d58d3f3cc858dbe51251cea3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.eneco.2019.104536$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27866,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Caloia, Francesco Giuseppe</creatorcontrib><creatorcontrib>Cipollini, Andrea</creatorcontrib><creatorcontrib>Muzzioli, Silvia</creatorcontrib><title>How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study</title><title>Energy economics</title><description>•This paper replicates the Diebold and Yilmaz, DY, (2012) study on financial markets connectedness.•The markets are the commodity and the stock, bond, FX for the US.•Similar to DY, we use use the Generalized Forecast Error Variance Decomposition, GEFVD.•We compare normalization schemes to GEVD.•We show that a scalar-based normalization is preferable to the row normalization suggested by DY.
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.</description><subject>Analysis of covariance</subject><subject>Bond markets</subject><subject>Bonds</subject><subject>Causality</subject><subject>Commodity markets</subject><subject>Connectedness</subject><subject>Covariance</subject><subject>Decomposition</subject><subject>Economic forecasting</subject><subject>Energy economics</subject><subject>Errors</subject><subject>Generalized forecast error variance decomposition</subject><subject>Normalization</subject><subject>Normalization schemes</subject><subject>Ratings & rankings</subject><subject>Securities markets</subject><subject>Simulation</subject><subject>Spillover</subject><subject>Stock exchanges</subject><subject>Variance</subject><subject>Vector autoregression models</subject><issn>0140-9883</issn><issn>1873-6181</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>7TQ</sourceid><recordid>eNp9kD9PwzAQxS0EEqXwCVgsscCQYsex4wwIVeWvVIkFBibLsS-qqzQudlrUfnpcwsx0p9N77-5-CF1SMqGEitvlBDowfpITWqVJwZk4QiMqS5YJKukxGhFakKySkp2isxiXhBAuuByhxYv_xtbjzoeVbt1e9853OJoFrCBi3TRgetxBj-Pata3fQoj3eIoDrFtnBrFvcL8A_OCg9q3FurP407UrvcfX6Z78Bsd-Y3fn6KTRbYSLvzpGH0-P77OXbP72_DqbzjPDSdlnJhdFRTmrKyOoYQC15WUlyqouy6aoWdno2kpRCsulZQ0zRqamBk5zTg1oNkZXQ-46-K8NxF4t_SZ0aaXKGS8kTeF5UrFBZYKPMUCj1sGtdNgpStQBqVqqX6TqgFQNSJPrbnBBemDrIKhoHHQGrAuJk7Le_ev_AZl6f9M</recordid><startdate>20191001</startdate><enddate>20191001</enddate><creator>Caloia, Francesco Giuseppe</creator><creator>Cipollini, Andrea</creator><creator>Muzzioli, Silvia</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TA</scope><scope>7TQ</scope><scope>8BJ</scope><scope>8FD</scope><scope>C1K</scope><scope>DHY</scope><scope>DON</scope><scope>FQK</scope><scope>JBE</scope><scope>JG9</scope><scope>SOI</scope></search><sort><creationdate>20191001</creationdate><title>How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study</title><author>Caloia, Francesco Giuseppe ; Cipollini, Andrea ; Muzzioli, Silvia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c507t-c2649153b9c61c3eebd579679b77f4b37fabd8676d58d3f3cc858dbe51251cea3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Analysis of covariance</topic><topic>Bond markets</topic><topic>Bonds</topic><topic>Causality</topic><topic>Commodity markets</topic><topic>Connectedness</topic><topic>Covariance</topic><topic>Decomposition</topic><topic>Economic forecasting</topic><topic>Energy economics</topic><topic>Errors</topic><topic>Generalized forecast error variance decomposition</topic><topic>Normalization</topic><topic>Normalization schemes</topic><topic>Ratings & rankings</topic><topic>Securities markets</topic><topic>Simulation</topic><topic>Spillover</topic><topic>Stock exchanges</topic><topic>Variance</topic><topic>Vector autoregression models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Caloia, Francesco Giuseppe</creatorcontrib><creatorcontrib>Cipollini, Andrea</creatorcontrib><creatorcontrib>Muzzioli, Silvia</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Materials Business File</collection><collection>PAIS Index</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>PAIS International</collection><collection>PAIS International (Ovid)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>Materials Research Database</collection><collection>Environment Abstracts</collection><jtitle>Energy economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Caloia, Francesco Giuseppe</au><au>Cipollini, Andrea</au><au>Muzzioli, Silvia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study</atitle><jtitle>Energy economics</jtitle><date>2019-10-01</date><risdate>2019</risdate><volume>84</volume><spage>104536</spage><pages>104536-</pages><artnum>104536</artnum><issn>0140-9883</issn><eissn>1873-6181</eissn><abstract>•This paper replicates the Diebold and Yilmaz, DY, (2012) study on financial markets connectedness.•The markets are the commodity and the stock, bond, FX for the US.•Similar to DY, we use use the Generalized Forecast Error Variance Decomposition, GEFVD.•We compare normalization schemes to GEVD.•We show that a scalar-based normalization is preferable to the row normalization suggested by DY.
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.</abstract><cop>Kidlington</cop><pub>Elsevier B.V</pub><doi>10.1016/j.eneco.2019.104536</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis of covariance Bond markets Bonds Causality Commodity markets Connectedness Covariance Decomposition Economic forecasting Energy economics Errors Generalized forecast error variance decomposition Normalization Normalization schemes Ratings & rankings Securities markets Simulation Spillover Stock exchanges Variance Vector autoregression models |
| title | How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study |
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