An Empirical Evaluation of Structural Credit Risk Models
Author | : Nikola A. Tarashev |
Publisher | : |
Total Pages | : 44 |
Release | : 2005 |
Genre | : |
ISBN | : |
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Author | : Nikola A. Tarashev |
Publisher | : |
Total Pages | : 44 |
Release | : 2005 |
Genre | : |
ISBN | : |
Author | : Nikola A. Tarashev |
Publisher | : |
Total Pages | : 56 |
Release | : 2005 |
Genre | : Credit |
ISBN | : |
This paper evaluates empirically the performance of six structural credit risk models by comparing the probabilities of default (PDs) they deliver to ex post default rates. In contrast to previous studies pursuing similar objectives, the paper employs firm-level data and finds that theory-based PDs tend to match closely the actual level of credit risk and to account for its time path. At the same time, nonmodelled macro variables from the financial and real sides of the economy help to substantially improve the forecasts of default rates. The finding suggests that theory-based PDs fail to fully reflect the dependence of credit risk on the business and credit cycles. Most of the upbeat conclusions regarding the performance of the PDs are due to models with endogenous default. For their part, frameworks that assume exogenous default tend to underpredict credit risk. Three borrower characteristics influence materially the predictions of the models: the leverage ratio; the default recovery rate; and the risk-free rate of return.
Author | : Mike Lauer-Grigore |
Publisher | : |
Total Pages | : |
Release | : 2015 |
Genre | : |
ISBN | : |
Author | : Andreas Makrides |
Publisher | : John Wiley & Sons |
Total Pages | : 241 |
Release | : 2020-04-09 |
Genre | : Mathematics |
ISBN | : 111972158X |
Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case, the need for literature that addresses this is self-evident. New publications are appearing, covering the need for information from all fields of science and engineering, thanks to the universal relevance of data analysis and statistics packages. This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis. The chapters included in this volume represent a cross-section of current concerns and research interests in these scientific areas. The material is divided into three parts: Financial Data Analysis and Methods, Statistics and Stochastic Data Analysis and Methods, and Demographic Methods and Data Analysis- providing the reader with both theoretical and applied information on data analysis methods, models and techniques and appropriate applications.
Author | : Andrej Sedej (matematik.) |
Publisher | : |
Total Pages | : 84 |
Release | : 2018 |
Genre | : |
ISBN | : |
Author | : Wai Ming Koo |
Publisher | : |
Total Pages | : 40 |
Release | : 2000 |
Genre | : |
ISBN | : |
Author | : Mads Gjedsted Nielsen |
Publisher | : LAP Lambert Academic Publishing |
Total Pages | : 120 |
Release | : 2011-02 |
Genre | : |
ISBN | : 9783844306118 |
Three different credit risk models are presented, implemented, and calibrated to real data. Each of which presents a different way to model the dynamics of a firm. To better examine their differences, the models are benchmarked against the much celebrated Merton's model. Generally it is shown that structural credit risk models have empirical validity. However, all is not perfect. Since structural credit risk models may have two objectives. One being to accurately predict credit spreads, and another to determine the optimal capital structure. It is argued that if the goal is the former, then future structural models need to incorporate a more exible framework that can price the many di erent types of bonds that make up a company s debt simultaneously. However, if the objective is the latter, then the future models need to better account for the high costs linked with capital restructures in times of nancial distress.
Author | : Joel Reneby |
Publisher | : |
Total Pages | : |
Release | : 2004 |
Genre | : |
ISBN | : |
Reduced form credit risk models are often thought to be better suited for pricing corporate bonds than structural models. In this paper we challenge this view; by conditioning not only on equity but also on bond and dividend information, our structural model performs well in comparison to previously tested reduced form models. Moreover, we consider pricing of bond portfolios and show that model errors are to a large extent diversifiable.
Author | : Didier Cossin |
Publisher | : John Wiley & Sons |
Total Pages | : 384 |
Release | : 2001 |
Genre | : Business & Economics |
ISBN | : |
Advanced Credit Analysis presents the latest and most advanced modelling techniques in the theory and practice of credit risk pricing and management. The book stresses the logic of theoretical models from the structural and the reduced-form kind, their applications and extensions. It shows the mathematical models that help determine optimal collateralisation and marking-to-market policies. It looks at modern credit risk management tools and the current structuring techniques available with credit derivatives.
Author | : Tomasz R. Bielecki |
Publisher | : Springer Science & Business Media |
Total Pages | : 517 |
Release | : 2013-03-14 |
Genre | : Business & Economics |
ISBN | : 3662048213 |
The motivation for the mathematical modeling studied in this text on developments in credit risk research is the bridging of the gap between mathematical theory of credit risk and the financial practice. Mathematical developments are covered thoroughly and give the structural and reduced-form approaches to credit risk modeling. Included is a detailed study of various arbitrage-free models of default term structures with several rating grades.