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Variance decomposition networks : potential pitfalls and a simple solution /
Diebold and Yilmaz (2015) recently introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. The nature of these networks and their implied rankings depend on the choice decomposition method. The standard choice is the order invariant gene...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Washington, District of Columbia] :
International Monetary Fund,
2017.
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| Colección: | IMF working paper ;
WP/17/107. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Chan-Lau, Jorge A., |e author. | |
| 245 | 1 | 0 | |a Variance decomposition networks : |b potential pitfalls and a simple solution / |c by Jorge A. Chan-Lau. |
| 264 | 1 | |a [Washington, District of Columbia] : |b International Monetary Fund, |c 2017. | |
| 264 | 4 | |c ©2017 | |
| 300 | |a 1 online resource (48 pages) : |b illustrations (some color), tables, graphs | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a IMF working paper ; |v WP/17/107 | |
| 520 | 3 | |a Diebold and Yilmaz (2015) recently introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. The nature of these networks and their implied rankings depend on the choice decomposition method. The standard choice is the order invariant generalized forecast error variance decomposition of Pesaran and Shin (1998). The shares of the forecast error variation, however, do not add to unity, making difficult to compare risk ratings and risks contributions at two different points in time. As a solution, this paper suggests using the Lanne-Nyberg (2016) decomposition, which shares the order invariance property. To illustrate the differences between both decomposition methods, I analyzed the global financial system during 2001 - 2016. The analysis shows that different decomposition methods yield substantially different systemic risk and vulnerability rankings. This suggests caution is warranted when using rankings and risk contributions for guiding financial regulation and economic policy. | |
| 505 | 0 | |a Cover; Contents; 1. Motivation; 2. Network Interconnectedness and Systemic Risk: A Brief Literature Review; 3. Methodology; A High Dimensional VAR Estimation; B. The Pesaran-Shin and Lanne-NybergGFEVDs; C. Variance Decomposition Networks; 4. A Case Study: Systemic Risk in the Global Financial Network; 5. Conclusions; References; Appendix: Selected Tables, January 2001 -- July 2016; Figures; 1. Distribution of Pesan-Shin total GFEVD contributions to the equity returns of individual firms, full sample and sub-sample periods; 2. Number of overlapping firms in the top fifty DY and CLNDY rankings. | |
| 505 | 8 | |a 3a. Banks: systemic risk rankings, probability distribution evolution3b. Life insurers: systemic risk rankings, probability distribution evolution; 3c. Property/casualty/health insurers: systemic risk rankings, probability distribution evolution; 4a. Banks: systemic vulnerability rankings, probability distribution evolution; 4b. Life insurers: systemic vulnerability rankings, probability distribution evolution; 4c. Property/casualty/health insurers: systemic vulnerability rankings, probability distribution evolution; Tables; 1. Distribution of firms per country. | |
| 505 | 8 | |a 2. Rank correlations, Diebold-Yilmaz and corrected Lanne-Nyberg-Diebold-Yilmaz rankings3a. Systemic risk rankings, all firms: January 2001 -- December 2004; 3b. Systemic risk rankings, all firms: January 2005 -- December 2008; 3c. Systemic risk rankings, all firms: January 2009 -- December 2012; 3d. Systemic risk rankings, all firms: January 2013 -- July 2016; 3e. Systemic risk rankings, all firms: January 2001 -- July 2016; 4a. Systemic vulnerability rankings, all firms: January 2001 -- December 2004; 4b. Systemic vulnerability rankings, all firms: January 2005 -- December 2008. | |
| 505 | 8 | |a 4c. Systemic vulnerability rankings, all firms: January 2009 -- December 20124d. Systemic vulnerability rankings, all firms: January 2013 -- July 2016; 4e. Systemic vulnerability rankings, all firms: January 2001 -- July 2016; 5. Top fifty systemic firms, by headquarter locations; 6. Top fifty systemic firms, by industry, in percent; A1. Advanced Asia -- systemic risk rankings; A2. Emerging markets economies -- systemic risk rankings; A3. Europe -- systemic risk rankings; A4. North America -- systemic risk rankings; A5 Advanced Asia -- systemic risk rankings. | |
| 505 | 8 | |a A6. Emerging markets economies -- systemic risk rankingsA7. Europe -- systemic risk rankings; A8. North America -- systemic risk rankings. | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Decomposition method. | |
| 650 | 0 | |a Decomposition method |x Data processing. | |
| 650 | 7 | |a MATHEMATICS |x Calculus. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Mathematical Analysis. |2 bisacsh | |
| 650 | 7 | |a Decomposition method |2 fast | |
| 650 | 7 | |a Decomposition method |x Data processing |2 fast | |
| 650 | 7 | |a All Countries. |2 imf | |
| 650 | 7 | |a Other. |2 imf | |
| 650 | 7 | |a Mortgages. |2 imf | |
| 650 | 7 | |a Time-Series Models. |2 imf | |
| 650 | 7 | |a Economic Forecasting. |2 imf | |
| 776 | 0 | 8 | |i Print version: |a Chan-Lau, Jorge A. |t Variance Decomposition Networks. |d Washington, D.C. : International Monetary Fund, ©2017 |z 9781475598407 |
| 830 | 0 | |a IMF working paper ; |v WP/17/107. | |
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