Parameters measuring bank risk and their estimation

The paper develops estimation of three parameters of banking risk based on an explicit model of expected utility maximization by financial institutions subject to the classical technology restrictions of neoclassical production theory. The parameters are risk aversion, prudence or downside risk aversion and generalized risk resulting from a factor model of loan prices. The model can be estimated using standard econometric techniques, like GMM for dynamic panel data and latent factor analysis for the estimation of covariance matrices. An explicit functional form for the utility function is not needed and we show how measures of risk aversion and prudence (downside risk aversion) can be derived and estimated from the model. The model is estimated using data for Eurozone countries and we focus particularly on (i) the use of the modeling approach as a device close to an “early warning mechanism”, (ii) the bank- and country-specific estimates of risk aversion and prudence (downside risk aversion), and (iii) the derivation of a generalized measure of risk that relies on loan-price uncertainty. Moreover, the model provides estimates of loan price distortions and thus, allocative efficiency

Kernel alternatives to aproximate operational severity distribution: an empirical application

The estimation of severity loss distribution is one the main topic in operational risk estimation. Numerous parametric estimations have been suggested although very few work for both high frequency small losses and low frequency big losses. In this paper several estimation are explored. The good performance of the double transformation kernel estimation in the context of operational risk severity is worthy of a special mention. This method is based on the work of Bolancé and Guillén (2009), it was initially proposed in the context of the cost of claims insurance, and it means an advance in operational risk research

Comparative analysis of alternative credit risk models : an application on German middle market loan portfolios

In recent years new methods and models have been developed to quantify credit risk on a portfolio basis. CreditMetrics (tm), CreditRisk+, CreditPortfolio (tm) are among the best known and many others are similar to them. At first glance they are quite different in their approaches and methodologies. A comparison of these models especially with regard to their applicability on typical middle market loan portfolios is in the focus of this study. The analysis shows that differences in the results of an application of the models on a certain loan portfolio is mainly due to different approaches in approximating default correlations. That is especially true for typically non-rated medium-sized counterparties. On the other hand distributional assumptions or different solution techniques in the models are more or less compatible.Seit einigen Jahren finden sich in Wissenschaft und Bankpraxis neue Methoden und Modelle, um Risiken von Kreditportfolios zu messen. Zu den bekanntesten Vertretern gehören CreditMetrics(tm) , CreditRisk+ und CreditPortfolioView(tm) , welche sich auf den ersten Blick stark im Ansatz und in der Methodik unterscheiden. Im Mittelpunkt der vorliegenden Studie steht ein Vergleich dieser Modelle und zwar insbesondere hinsichtlich ihrer Anwendbarkeit auf ein typisches Portfolio aus mittelständischen Bankkrediten. Die Analyse zeigt, dass Unterschiede in den Ergebnissen zweier Modelle für ein und dasselbe Portfolio vor allem auf unterschiedliche Verfahren in der Approximation von Ausfallkorrelationen zurückzuführen sind. Dies gilt insbesondere für Kredite an nicht-geratete mittelständische Unternehmen

Loan portfolio loss distribution: Basel II unifactorial approach vs. Non parametric estimations

This paper analyzes the measurement of credit risk capital requirements under the new Basel Accord (Basel II): the Internal Rating Based approach (IRB). It focuses in the analytical formula for its calculation, since its derivation to the main assumptions behind it. We also estimate the credit loss distribution for the Uruguayan portfolio in the period 1999-2006, using a non parametric technique, the bootstrap. Its main advantage is that we don’t need to make any assumptions about the form of the distribution. Finally, we compare the requirements obtained using the IRB with the estimated ones, as an approximation of the application of the IRB in the Uruguayan financial system

Implementing Loss Distribution Approach for Operational Risk

To quantify the operational risk capital charge under the current regulatory framework for banking supervision, referred to as Basel II, many banks adopt the Loss Distribution Approach. There are many modeling issues that should be resolved to use the approach in practice. In this paper we review the quantitative methods suggested in literature for implementation of the approach. In particular, the use of the Bayesian inference method that allows to take expert judgement and parameter uncertainty into account, modeling dependence and inclusion of insurance are discussed

Real-Time Time-Varying Equilibrium Interest Rates: Evidence on the Czech Republic

This paper examines (real-time) equilibrium interest rates in the Czech Republic in 2001:1- 2005:12 estimating various specifications of simple Taylor-type monetary policy rules. First, we estimate it using GMM. Second, we apply structural time-varying coefficient model with endogenous regressors to evaluate fluctuations of equilibrium interest rate over time. The results suggest that there is substantial interest rate smoothing and central bank primarily responds to inflation (forecast) developments. The estimated parameters seem to sustain the equilibrium determinacy. We find that the equilibrium interest rates gradually decreased over sample period to the levels comparable to those of in the euro area reflecting capital accumulation, smaller risk premium and successful disinflation in the Czech economy.http://deepblue.lib.umich.edu/bitstream/2027.42/57228/1/wp848 .pd