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Analytical Solutions to Multi-period Credit Portfolio Management A Macroeconomic Approach
dc.contributor.author | Suárez-Lledó Grande, José | es-ES |
dc.contributor.author | Licari, Juan | es-ES |
dc.contributor.author | Ordóñez, Gustavo | es-ES |
dc.date.accessioned | 2018-02-20T14:48:08Z | |
dc.date.available | 2018-02-20T14:48:08Z | |
dc.date.issued | 03/08/2015 | es_ES |
dc.identifier | https://www.moodysanalytics.com/-/media/article/2015/2015-21-07-moodys-analytics-risk-perspective-risk-data-management.pdf | es_ES |
dc.identifier.uri | http://hdl.handle.net/11531/25935 | |
dc.description | Revista electrónica | es_ES |
dc.description.abstract | In this paper we develop a quantitative framework that allows us to calculate portfolio credit risk metrics analytically under multi-period and multi-credit-state environments. Of particular interest is the calculation of (intra-period and cumulative) expected and unexpected portfolio losses. These closed-form, exact formulas enable us to perform risk-based pricing, portfolio optimisation, risk-concentration analysis, stress and reverse stress-testing in a very precise and efficient way (without having to wait for days, if not weeks, for Monte Carlo methods to handle a multi-period, multi-credit-state set-up). Our innovative approach hinges on dynamic scenario generation using structural macroeconomic models. These forward-looking simulations are then linked to credit and market risk parameters, providing an opportunity to leverage the current wave of investment and developments around stress-testing methods. Moreover, the use of general equilibrium, stochastic macro models to build the scenarios gives us the ability to integrate default and mark-to-market risks under a single, unified multi-period framework. https://www.moodysanalytics.com/-/media/article/2015/2015-21-07-moodys-analytics-risk-perspective-risk-data-management.pdf | es-ES |
dc.description.abstract | In this paper we develop a quantitative framework that allows us to calculate portfolio credit risk metrics analytically under multi-period and multi-credit-state environments. Of particular interest is the calculation of (intra-period and cumulative) expected and unexpected portfolio losses. These closed-form, exact formulas enable us to perform risk-based pricing, portfolio optimisation, risk-concentration analysis, stress and reverse stress-testing in a very precise and efficient way (without having to wait for days, if not weeks, for Monte Carlo methods to handle a multi-period, multi-credit-state set-up). Our innovative approach hinges on dynamic scenario generation using structural macroeconomic models. These forward-looking simulations are then linked to credit and market risk parameters, providing an opportunity to leverage the current wave of investment and developments around stress-testing methods. Moreover, the use of general equilibrium, stochastic macro models to build the scenarios gives us the ability to integrate default and mark-to-market risks under a single, unified multi-period framework. https://www.moodysanalytics.com/-/media/article/2015/2015-21-07-moodys-analytics-risk-perspective-risk-data-management.pdf | en-GB |
dc.format.mimetype | application/pdf | es_ES |
dc.language.iso | en-GB | es_ES |
dc.rights | es_ES | |
dc.rights.uri | es_ES | |
dc.source | Número: 5 Volumen: 5 Pagina Inicio: 110 Pagina Fin: 150 | es_ES |
dc.title | Analytical Solutions to Multi-period Credit Portfolio Management A Macroeconomic Approach | es_ES |
dc.type | info:eu-repo/semantics/other | es_ES |
dc.description.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.rights.holder | El documento formará parte de la publicación de un libro sobre risk management en los próximos meses y puede estar sujeto a derechos de copyright | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | es_ES |
dc.keywords | Risk Management, Credit Portfolio, Bayesian, Simulation | es-ES |
dc.keywords | Risk Management, Credit Portfolio, Bayesian, Simulation | en-GB |
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