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Separating the impact of macroeconomic variables and global frailty in event data

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  • James Wolter
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    Abstract

    Global frailty is an unobserved macroeconomic variable.� In event data contexts, this unobserved variable is assumed to impact the hazard rate of event arrivals.� Attempts to identify and estimate the path of frailty are complicated when observed macroeconomic variables also impact hazard rates.� It is possible that the impact of the observed macro variables and global frailty can be confused and identification can fail.� In this paper I show that, under appropriate assumptions, the path of global frailty and the impact of observed macro variables can both be recovered.� This approach differs from previous work in that I do not assume frailty follows a specific stochastic process form.� Previous studies identify global frailty by assuming a stochastic form and using a filtering approach.� However, chosen stochastic forms are arbitrary and can potentially lead to poor results.� The method in this paper shows how to recover frailty without these assumptions.� This can serve as a model check to filtering�approaches.� The methods are applied to simulations and an application to corporate default.

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    Bibliographic Info

    Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 667.

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    Date of creation: 10 Jul 2013
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    Handle: RePEc:oxf:wpaper:667

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    1. repec:cup:cbooks:9780521496032 is not listed on IDEAS
    2. Creal, Drew & Schwaab, Bernd & Koopman, Siem Jan & Lucas, André, 2013. "Observation driven mixed-measurement dynamic factor models with an application to credit risk," Working Paper Series, European Central Bank 1626, European Central Bank.
    3. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, American Finance Association, vol. 62(1), pages 93-117, 02.
    4. Darrell Duffie & Leandro Siata & Ke Wang, 2006. "Multi-Period Corporate Default Prediction With Stochastic Covariates," NBER Working Papers 11962, National Bureau of Economic Research, Inc.
    5. Christopher Mayer & Karen Pence & Shane M. Sherlund, 2009. "The Rise in Mortgage Defaults," Journal of Economic Perspectives, American Economic Association, American Economic Association, vol. 23(1), pages 27-50, Winter.
    6. Azizpour, Shahriar & Giesecke, Kay & Kim, Baeho, 2011. "Premia for correlated default risk," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 35(8), pages 1340-1357, August.
    7. Darrell Duffie & Andreas Eckner & Guillaume Horel & Leandro Saita, 2009. "Frailty Correlated Default," Journal of Finance, American Finance Association, American Finance Association, vol. 64(5), pages 2089-2123, October.
    8. Lando, David & Nielsen, Mads Stenbo, 2010. "Correlation in corporate defaults: Contagion or conditional independence?," Journal of Financial Intermediation, Elsevier, Elsevier, vol. 19(3), pages 355-372, July.
    9. Koopman, Siem Jan & Lucas, André & Schwaab, Bernd, 2011. "Modeling frailty-correlated defaults using many macroeconomic covariates," Journal of Econometrics, Elsevier, Elsevier, vol. 162(2), pages 312-325, June.
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