OPUS 4 Latest Documents RSS FeedLatest documents
http://publikationen.ub.uni-frankfurt.de/index/index/
Mon, 20 Oct 2014 15:27:07 +0200Mon, 20 Oct 2014 15:27:07 +0200Marginalized predictive likelihood comparisons of linear Gaussian state-space models with applications to DSGE, DSGE-VAR, and VAR models
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/35110
he predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models with Bayesian methods, and proposes to utilize a missing observations consistent Kalman filter in the process of achieving this objective. As an empirical application, we analyze euro area data and compare the density forecast performance of a DSGE model to DSGE-VARs and reduced-form linear Gaussian models.Anders Warne; Günter Coenen; Kai Christoffelworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/35110Mon, 20 Oct 2014 15:27:07 +0200Evaluating credit risk models: a critique and a new proposal
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/34990
Evaluating the quality of credit portfolio risk models is an important question for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data and to produce measures of forecast accuracy. We first show that their proposal disregards crosssectional dependence in simulated subportfolios, which renders standard statistical inference invalid. We proceed by suggesting another evaluation methodology which draws on the concept of likelihood ratio tests. Specifically, we compare the predictive quality of alternative models by comparing the probabilities that observed data have been generated by these models. The distribution of the test statistic can be derived through Monte Carlo simulation. To exploit differences in cross-sectional predictions of alternative models, the test can be based on a linear combination of subportfolio statistics. In the construction of the test, the weight of a subportfolio depends on the difference in the loss distributions which alternative models predict for this particular portfolio. This makes efficient use of the data, and reduces computational burden. Monte Carlo simulations suggest that the power of the tests is satisfactory.
JEL classification: G2; G28; C52Hergen Frerichs; Gunter Löfflerreporthttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/34990Wed, 17 Sep 2014 14:23:49 +0200Evaluating credit risk models : a critique and a proposal
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/35041
Evaluating the quality of credit portfolio risk models is an important issue for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data. We show that their proposal disregards cross-sectional dependence in resampled portfolios, which renders standard statistical inference invalid. We proceed by suggesting the Berkowitz (1999) procedure, which relies on standard likelihood ratio tests performed on transformed default data. We simulate the power of this approach in various settings including one in which the test is extended to incorporate cross-sectional information. To compare the predictive ability of alternative models, we propose to use either Bonferroni bounds or the likelihood-ratio of the two models. Monte Carlo simulations show that a default history of ten years can be sufficient to resolve uncertainties currently present in credit risk modeling.Hergen Frerichs; Gunter Löfflerconferenceobjecthttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/35041Wed, 17 Sep 2014 14:10:53 +0200Surprising comparative properties of monetary models : results from a new model database
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/27872
In this paper we investigate the comparative properties of empirically-estimated monetary models of the U.S. economy using a new database of models designed for such investigations. We focus on three representative models due to Christiano, Eichenbaum, Evans (2005), Smets and Wouters (2007) and Taylor (1993a). Although these models differ in terms of structure, estimation method, sample period, and data vintage, we find surprisingly similar economic impacts of unanticipated changes in the federal funds rate. However, optimized monetary policy rules differ across models and lack robustness. Model averaging offers an effective strategy for improving the robustness of policy rules.John B. Taylor; Volker Wielandworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/27872Tue, 05 Feb 2013 10:41:16 +0100The new keynesian approach to dynamic general equilibrium modeling: models, methods, and macroeconomic policy evaluation
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/25256
This chapter aims to provide a hands-on approach to New Keynesian models and their uses for macroeconomic policy analysis. It starts by reviewing the origins of the New Keynesian approach, the key model ingredients and representative models. Building blocks of current-generation dynamic stochastic general equilibrium (DSGE) models are discussed in detail. These models address the famous Lucas critique by deriving behavioral equations systematically from the optimizing and forward-looking decision-making of households and firms subject to well-defined constraints. State-of-the-art methods for solving and estimating such models are reviewed and presented in examples. The chapter goes beyond the mere presentation of the most popular benchmark model by providing a framework for model comparison along with a database that includes a wide variety of macroeconomic models. Thus, it offers a convenient approach for comparing new models to available benchmarks and for investigating whether particular policy recommendations are robust to model uncertainty. Such robustness analysis is illustrated by evaluating the performance of simple monetary policy rules across a range of recently-estimated models including some with financial market imperfections and by reviewing recent comparative findings regarding the magnitude of government spending multipliers. The chapter concludes with a discussion of important objectives for on-going and future research using the New Keynesian framework.Sebastian Schmidt; Volker Wielandworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/25256Thu, 19 Jul 2012 10:24:02 +0200Value-at-Risk and expected shortfall for rare events
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/5739
We show that the use of correlations for modeling dependencies may lead to counterintuitive behavior of risk measures, such as Value-at-Risk (VaR) and Expected Short- fall (ES), when the risk of very rare events is assessed via Monte-Carlo techniques. The phenomenon is demonstrated for mixture models adapted from credit risk analysis as well as for common Poisson-shock models used in reliability theory. An obvious implication of this finding pertains to the analysis of operational risk. The alleged incentive suggested by the New Basel Capital Accord (Basel II), amely decreasing minimum capital requirements by allowing for less than perfect correlation, may not necessarily be attainable.Stefan Mittnik; Tina Yenerworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/5739Thu, 07 Aug 2008 16:46:18 +0200Evaluating internal credit rating systems depending on bank size
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3682
Under a new Basel capital accord, bank regulators might use quantitative measures when evaluating the eligibility of internal credit rating systems for the internal ratings based approach. Based on data from Deutsche Bundesbank and using a simulation approach, we find that it is possible to identify strongly inferior rating systems out-of time based on statistics that measure either the quality of ranking borrowers from good to bad, or the quality of individual default probability forecasts. Banks do not significantly improve system quality if they use credit scores instead of ratings, or logistic regression default probability estimates instead of historical data. Banks that are not able to discriminate between high- and low-risk borrowers increase their average capital requirements due to the concavity of the capital requirements function.Hergen Frerichs; Mark Wahrenburgworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3682Fri, 07 Oct 2005 08:52:23 +0200