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Measuring Dynamic Connectedness with Large Bayesian VAR Models
| Content Provider | Scilit |
|---|---|
| Author | Korobilis, Dimitris Mname Yilmaz, Kamil Mname |
| Copyright Year | 2018 |
| Description | Journal: Ssrn Electronic Journal We estimate a large Bayesian time-varying parameter vector autoregressive (TVP-VAR) model of daily stock return volatilities for 35 U.S. and European financial institutions. Based on that model we extract a connectedness index in the spirit of Diebold and Yilmaz (2014) (DYCI). We show that the connectedness index from the TVP-VAR model captures abrupt turning points better than the one obtained from rolling-windows VAR estimates. As the TVP-VAR based DYCI shows more pronounced jumps during important crisis moments, it captures the intensification of tensions in financial markets more accurately and timely than the rolling-windows based DYCI. Finally, we show that the TVP- VAR-based index performs better in forecasting systemic events in the American and European financial sectors as well. |
| Related Links | http://eaf.ku.edu.tr/sites/eaf.ku.edu.tr/files/erf_wp_1802.pdf https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3099725 |
| ISSN | 10914358 |
| e-ISSN | 15565068 |
| DOI | 10.2139/ssrn.3099725 |
| Journal | Ssrn Electronic Journal |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2018-01-10 |
| Access Restriction | Open |
| Subject Keyword | Journal: Ssrn Electronic Journal Mathematical Social Sciences Vector Autoregression Time-varying Parameter Model Rolling Window Estimation Financial Institutions |
| Content Type | Text |
| Resource Type | Article |
| Subject | Public Health, Environmental and Occupational Health Psychiatry and Mental Health |
