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on Risk Management |
| By: | Marianna Lyra; Johannes Paha; Sandra Paterlini; Peter Winker |
| Abstract: | Basel II imposes regulatory capital on banks related to the default risk of their credit portfolio. Banks using an internal rating approach compute the regulatory capital from pooled probabilities of default. These pooled probabilities can be calculated by clustering credit borrowers into different buckets and computing the mean PD for each bucket. The clustering problem can become very complex when Basel II regulations and real-world constraints are taken into account. Search heuristics have already proven remarkable performance in tackling this problem as complex as it is. A Threshold Accepting algorithm is proposed, which exploits the inherent discrete nature of the clustering problem. This algorithm is found to outperform alternative methodologies already proposed in the literature, such as standard k-means and Differential Evolution. Besides considering several clustering objectives for a given number of buckets, we extend the analysis further by introducing new methods to determine the optimal number of buckets in which to cluster banks' clients. |
| Keywords: | credit risk, probability of default, clustering, Threshold Accepting, Differential Evolution |
| Date: | 2008–09 |
| URL: | http://d.repec.org/n?u=RePEc:mod:recent:023&r=rmg |
| By: | Visser, Marcel P. |
| Abstract: | This paper decomposes volatility proxies according to upward and downward price movements in high-frequency financial data, and uses this decomposition for forecasting volatility. The paper introduces a simple Garch-type discrete time model that incorporates such high-frequency based statistics into a forecast equation for daily volatility. Analysis of S&P 500 index tick data over the years 1988-2006 shows that taking into account the downward movements improves forecast accuracy significantly. The R2 statistic for evaluating daily volatility forecasts attains a value of 0.80, both for in-sample and out-of-sample prediction. |
| Keywords: | volatility proxy; downward absolute power variation; log-Garch; volatility asymmetry; leverage effect; SP500; volatility forecasting; high-frequency data |
| JEL: | C53 C22 G10 |
| Date: | 2008–10–14 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:11100&r=rmg |
| By: | Stefan Kooths; Matthias Rieger |
| Abstract: | The ongoing financial crisis so far cost the German financial sector 38 billion Euros due to losses on its mortgage-related subprime bank exposures. This paper looks for the impact of these losses on the real sector of the economy. First, the financial sector is looked at as part of the overall macro economy in order to identify the direct impact of the write-offs and devaluations of financial assets on value-added and employment in the financial industry. In the second part of the paper the financial sector's role as enabler of real investment is analyzed. So far, there is no significant evidence that the credit creation capacity of the German banking system as a whole was negatively affected (as indicated by stable money multiplier and base equity ratio values). In particular, the flow of credit to non-financial businesses remains intact despite heavy turmoil within the financial sector. Also, the overall interest rate for corporate lending did hardly in-crease. Econometrically, a switching disequilibrium model and a market-clearing approached were setup to test for excess demand during the crisis and any general impact of the crisis on the credit market respectively. The statistical tests turned out to be little helpful for quantifying any major effect. We conclude that despite the substantial financial losses there is no major negative spill-over from the banking sector to the real economy in Germany. |
| Keywords: | Subprime crisis, credit market, financial sector, value-added |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp825&r=rmg |
| By: | Christophe Hurlin (Laboratoire d'Economie d'Orléans - Université d'Orléans - CNRS : FRE2783); Gilbert Colletaz (Laboratoire d'Economie d'Orléans - Université d'Orléans - CNRS : FRE2783); Sessi Tokpavi (Laboratoire d'Economie d'Orléans - Université d'Orléans - CNRS : FRE2783); Bertrand Candelon (Laboratoire d'Economie d'Orléans - Université d'Orléans - CNRS : FRE2783) |
| Abstract: | This paper proposes a new duration-based backtesting procedure for VaR forecasts. The GMM test framework proposed by Bontemps (2006) to test for the distributional assumption (i.e. the geometric distribution) is applied to the case of the VaR forecasts validity. Using simple J-statistic based on the moments defined by the orthonormal polynomials associated with the geometric distribution, this new approach tackles most of the drawbacks usually associated to duration based backtesting procedures. First, its implementation is extremely easy. Second, it allows for a separate test for unconditional coverage, independence and conditional coverage hypothesis (Christoffersen, 1998). Third, feasibility of the tests is improved. Fourth, Monte-Carlo simulations show that for realistic sample sizes, our GMM test outperforms traditional duration based test. An empirical application for Nasdaq returns confirms that using GMM test leads to major consequences for the ex-post evaluation of the risk by regulation authorities. Without any doubt, this paper provides a strong support for the empirical application of duration-based tests for VaR forecasts. |
| Keywords: | Value-at-Risk; backtesting; GMM; duration-based test |
| Date: | 2008–10–10 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-00329495_v1&r=rmg |