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on Risk Management |
| By: | Jan Pieter Krahnen; Christian Wilde |
| Abstract: | Modern bank management comprises both classical lending business and transfer of asset risk to capital markets through securitization. Sound knowledge of the risks involved in securitization transactions is a prerequisite for solid risk management. This paper aims to resolve a part of the opaqueness surrounding credit-risk allocation to tranches that represent claims of different seniority on a reference portfolio. In particular, this paper analyzes the allocation of credit risk to different tranches of a CDO transaction when the underlying asset returns are driven by a common macro factor and an idiosyncratic component. Junior and senior tranches are found to be nearly orthogonal, motivating a search for the where about of systematic risk in CDO transactions. We propose a metric for capturing the allocation of systematic risk to tranches. First, in contrast to a widely-held claim, we show that (extreme) tail risk in standard CDO transactions is held by all tranches. While junior tranches take on all types of systematic risk, senior tranches take on almost no non-tail risk. This is in stark contrast to an untranched bond portfolio of the same rating quality, which on average suffers substantial losses for all realizations of the macro factor. Second, given tranching, a shock to the risk of the underlying asset portfolio (e.g. a rise in asset correlation or in mean portfolio loss) has the strongest impact, in relative terms, on the exposure of senior tranche CDO-investors. Our findings can be used to explain major stylized facts observed in credit markets. |
| JEL: | G21 G28 |
| Date: | 2008–04 |
| URL: | http://d.repec.org/n?u=RePEc:fra:franaf:187&r=rmg |
| By: | Dominique Guegan (Centre d'Economie de la Sorbonne et Paris School of Economics) |
| Abstract: | In this paper we deal with the problem of non-stationarity encountered in a lot of data sets, mainly in financial and economics domains, coming from the presence of multiple seasonnalities, jumps, volatility, distorsion, aggregation, etc. Existence of non-stationarity involves spurious behaviors in estimated statistics as soon as we work with finite samples. We illustrate this fact using Markov switching processes, Stopbreak models and SETAR processes. Thus, working with a theoretical framework based on the existence of an invariant measure for a whole sample is not satisfactory. Empirically alternative strategies have been developed introducing dynamics inside modelling mainly through the parameter with the use of rolling windows. A specific framework has not yet been proposed to study such non-invariant data sets. The question is difficult. Here, we address a discussion on this topic proposing the concept of meta-distribution which can be used to improve risk management strategies or forecasts. |
| Keywords: | Non-stationarity, switching processes, SETAR processes, jumps, forecast, risk management, copula, probability distribution function. |
| JEL: | C32 C51 G12 |
| Date: | 2008–03 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:b08026&r=rmg |
| By: | Christian T. Brownlees (Università degli Studi di Firenze, Dipartimento di Statistica); Giampiero Gallo (Università degli Studi di Firenze, Dipartimento di Statistica "G. Parenti") |
| Abstract: | In this paper we address the issue of forecasting Value–at–Risk (VaR) using different volatility measures: realized volatility, bipower realized volatility, two scales realized volatility, realized kernel as well as the daily range. We propose a dynamic model with a flexible trend specification bonded with a penalized maximum likelihood estimation strategy: the P-Spline Multiplicative Error Model. Exploiting UHFD volatility measures, VaR predictive ability is considerably improved upon relative to a baseline GARCH but not so relative to the range; there are relevant gains from modeling volatility trends and using realized kernels that are robust to dependent microstructure noise. |
| Keywords: | Volatility Measures, VaR Forecasting, GARCH, MEM, P-Spline. |
| JEL: | C22 C51 C52 C53 |
| Date: | 2008–02 |
| URL: | http://d.repec.org/n?u=RePEc:fir:econom:wp2008_03&r=rmg |
| By: | Christian T. Brownlees (Università degli Studi di Firenze, Dipartimento di Statistica); Giampiero Gallo (Università degli Studi di Firenze, Dipartimento di Statistica "G. Parenti") |
| Abstract: | This paper assesses the performance of volatility forecasting using focused selection and combination strategies to include relevant explanatory variables in the forecasting model. The focused selection/combination strategies consist of picking up the model that minimizes the estimated risk (e.g. MSE) of a given smooth function of the parameters of interest to the forecaster. The proposed focused methods are compared with other strategies, including the well established AIC and BIC. The methodology is applied to a daily recursive 1--step ahead value--at--risk (VaR) forecasting exercise of 4 widely traded New York Stock Exchange stocks. Results show that VaR forecasts can significantly be improved upon using focused forecast strategies for the selection of relevant exogenous information. The set of explanatory variables that helps improving prediction is stock dependent. Traditional information criteria do not appear to be helpful in suggesting the inclusion of explanatory variables that actually improve prediction significantly. In line with recent theoretical findings, the predictive performance of the BIC appears to be modest. |
| Keywords: | Forecasting, Shrinkage Estimation, FIC, MEM, GARCH, ACD |
| JEL: | C22 C51 C53 |
| Date: | 2007–05 |
| URL: | http://d.repec.org/n?u=RePEc:fir:econom:wp2007_04&r=rmg |
| By: | Araújo, Eurilton |
| Date: | 2008–10 |
| URL: | http://d.repec.org/n?u=RePEc:ibm:ibmecp:wpe_111&r=rmg |