|
on Risk Management |
| By: | Markus K. Brunnermeier; Motohiro Yogo |
| Abstract: | When a firm is unable to rollover its debt, it may have to seek more expensive sources of financing or even liquidate its assets. This paper provides a normative analysis of minimizing such rollover risk, through the optimal dynamic choice of the maturity structure of debt. The objective of a firm with long-term assets is to maximize the effective maturity of its liabilities across several refinancing cycles, rather than to maximize the maturity of the current bonds outstanding. An advantage of short-term financing is that a firm, while in good financial health, can readjust its maturity structure more quickly in response to changes in its asset value. |
| JEL: | G32 G33 |
| Date: | 2009–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:14727&r=rmg |
| By: | Chollete, Lorán (Dept. of Finance and Management Science, Norwegian School of Economics and Business Administration) |
| Abstract: | What drives extreme economic events? Motivated by recent theory, and events in US subprime markets, we begin to open the black box of extremes. Specifically, we extend standard economic analysis of extreme risk, allowing for dynamics and endogeneity. We explain how endogenous extremes may arise in an economy of individuals who engage in resource transfers. Our model suggests that susceptibility to extremes depends on differences in marginal substitution rates. Using over a century of daily stock price data, we construct empirical probabilities of extremes, and document interesting dynamic behavior. We find evidence that extremes are endogenous. This latter finding raises the possibility that control of extremes is a public good, and that extreme events may be an important market failure for regulators and central banks to correct. |
| Keywords: | Extreme Event; Subprime Market; Dynamics; Endogeneity; Public Good; Central Bank Policy |
| JEL: | C10 D62 E44 E51 G18 H23 H41 |
| Date: | 2009–02–10 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:nhhfms:2008_025&r=rmg |
| By: | Jiří Witzany (University of Economics, Prague, Czech Republic) |
| Abstract: | The goal of the Basle II regulatory formula is to model the unexpected loss on a loan portfolio. The regulatory formula is based on an asymptotic portfolio unexpected default rate estimation that is multiplied by an estimate of the loss given default parameter. This simplification leads to a surprising phenomenon when the resulting regulatory capital depends on the definition of default that plays the role of a frontier between the unexpected default rate estimate and the LGD parameter whose unexpected development is not modeled at all or only partially. We study the phenomenon in the context of single-factor models where default and loss given default are driven by one systemic factor and by one or more idiosyncratic factors. In this theoretical framework we propose and analyze a relatively simple remedy of the problem requiring that the LGD parameter be estimated as a quantile on the required probability level. |
| Keywords: | credit risk, correlation, recovery rate, regulatory capital |
| JEL: | G21 G28 C14 |
| Date: | 2009–02 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2009_09&r=rmg |
| By: | Olli Castrén (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main.); Trevor Fitzpatrick (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main.); Matthias Sydow (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.) |
| Abstract: | In terms of regulatory and economic capital, credit risk is the most significant risk faced by banks. We implement a credit risk model - based on publicly available information - with the aim of developing a tool to monitor credit risk in a sample of large and complex banking groups (LCBGs) in the EU. The results indicate varying credit risk profiles across these LCBGs and over time. Furthermore, the results show that large negative shocks to real GDP have the largest impact on the credit risk profiles of banks in the sample. Notwithstanding some caveats, the results demonstrate the potential value of this approach for monitoring financial stability. JEL Classification: C02, C19, C52, C61, E32. |
| Keywords: | Portfolio credit risk measurement, stress testing, macroeconomic shock measurement. |
| Date: | 2009–02 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:200901002&r=rmg |