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220928 ||| eng |
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|a 9781513536170
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| 100 |
1 |
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|a Espinoza, Raphael
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| 245 |
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|a Systemic Risk Modeling: How Theory Can Meet Statistics
|c Raphael Espinoza, Miguel Segoviano, Ji Yan
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2020
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| 300 |
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|a 39 pages
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| 653 |
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|a Systemic risk
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| 653 |
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|a Financial Risk and Risk Management
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| 653 |
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|a Goodwill
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| 653 |
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|a Micro Finance Institutions
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| 653 |
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|a Industries: Financial Services
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| 653 |
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|a Financial Forecasting and Simulation
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| 653 |
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|a Banks and Banking
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| 653 |
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|a Loans
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| 653 |
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|a Banks and banking
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| 653 |
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|a Banking
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| 653 |
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|a Finance: General
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| 653 |
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|a Depository Institutions
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| 653 |
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|a Financial risk management
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| 653 |
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|a Semiparametric and Nonparametric Methods
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| 653 |
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|a Consumer loans
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| 653 |
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|a Financing Policy
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| 653 |
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|a Capital and Ownership Structure
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| 653 |
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|a Mortgages
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| 653 |
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|a General Financial Markets: Government Policy and Regulation
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| 653 |
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|a Interbank markets
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| 653 |
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|a Finance
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| 653 |
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|a Banks
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| 653 |
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|a International finance
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| 653 |
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|a Value of Firms
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| 653 |
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|a General Financial Markets: General (includes Measurement and Data)
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| 700 |
1 |
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|a Segoviano, Miguel
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| 700 |
1 |
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|a Yan, Ji
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
0 |
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|a IMF Working Papers
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| 028 |
5 |
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|a 10.5089/9781513536170.001
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| 856 |
4 |
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|u https://elibrary.imf.org/view/journals/001/2020/054/001.2020.issue-054-en.xml?cid=49244-com-dsp-marc
|x Verlag
|3 Volltext
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|a 330
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| 520 |
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|a We propose a framework to link empirical models of systemic risk to theoretical network/ general equilibrium models used to understand the channels of transmission of systemic risk. The theoretical model allows for systemic risk due to interbank counterparty risk, common asset exposures/fire sales, and a “Minsky" cycle of optimism. The empirical model uses stock market and CDS spreads data to estimate a multivariate density of equity returns and to compute the expected equity return for each bank, conditional on a bad macro-outcome. Theses “cross-sectional" moments are used to re-calibrate the theoretical model and estimate the importance of the Minsky cycle of optimism in driving systemic risk
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