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on Risk Management |
| By: | cipollini, andrea; missaglia, giuseppe |
| Abstract: | In this paper we use a reduced form model for the analysis of Portfolio Credit Risk. For this purpose, we fit a Dynamic Factor model, DF, to a large dataset of default rates proxies and macro-variables for Italy. Multi step ahead density and probability forecasts are obtained by employing both the direct and indirect method of prediction together with stochastic simulation of the DF model. We, first, find that the direct method is the best performer regarding the out of sample projection of financial distressful events. In a second stage of the analysis, the direct method of forecasting through principal components is shown to provide the least sensitive measures of Portfolio Credit Risk to various multifactor model specifications. Finally, the simulation results suggest that the benefits in terms of credit risk diversification tend to diminish with an increasing number of factors, especially when using the indirect method of forecasting. |
| Keywords: | Dynamic Factor Model; Forecasting; Stochastic Simulation; Risk Management; Banking |
| JEL: | G33 C53 G21 |
| Date: | 2007–05–30 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:3582&r=rmg |
| By: | Chiara Pederzoli |
| Abstract: | As emphasized by the introduction of Basel II, the macroeconomic factors strongly affect credit risk variables. In order to account for the business cycle in a forward-looking way, a macroeconomic forecast can be introduced in the estimation of credit risk variables. This work proposes to model the distribution of the default rate as a mixture distribution which accounts for a binary representation of the business cycle: the distribution changes according to the estimated probability of recession over the credit horizon considered. |
| Keywords: | default risk; Poisson mixture; business cycle |
| JEL: | G21 C1 E3 |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:mod:wcefin:07052&r=rmg |
| By: | Zoltan, Varsanyi |
| Abstract: | In this paper I examine whether the probability of default (PD) of an obligor estimated by a logit model can really be considered a good estimate of the true PD. The general answer seems to be no, although in this paper I don’t carry out a large scale (simulation) analysis. With a simple set-up I show that the logit has a high potential of ‘mixing’ probabilities, that is, as signing similar scores to obligors with quite different PDs. I demonstrate how this situation is reflected in the convexity that can often be observed in empirical ROC curves. I think that the results have important implications in the pricing of individual exposures and raise the question of the stability of estimated PDs when the value-combinations of the risk factors underlying the portfolio change. This latter issue also relates to capital calculation, model building and validation as required by the new Basel capital rules. For example, because of the concavity of the risk weight formula a bank may want to avoid PD mixing thereby reducing its capital requirement. |
| Keywords: | credit risk; logit; Basel II |
| JEL: | G21 C13 |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:3658&r=rmg |
| By: | Lawrence A. Leger (Dept of Economics, Loughborough University); Vitor Leone (DTZ, London UK) |
| Abstract: | Changes in the risk structure of stock returns may sometimes be very revealing. We examine economic variables that help explain principal components in UK stock returns, 01/1985 to 12/2001. The loading pattern on explanatory variables for the first component in a ‘bubble’ period is distinctive and consistent with a bubble/crash market. The second component shows a loading pattern on a Consumer Confidence variable in a pre-bubble period only. We observe apparently systematic changes in the structure of risk, and conjecture that Consumer Confidence captures a change in market sentiment that could be a signal for the evolution of stock prices. |
| Keywords: | Macroeconomic variables, consumer confidence, stock returns, principal components analysis |
| JEL: | G1 |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:lbo:lbowps:2007_15&r=rmg |
| By: | Jonathan Wright; Hao Zhou |
| Abstract: | We find that adding a measure of market jump volatility risk to a regression of excess bond returns on the term structure of forward rates nearly doubles the R square of the regression. Our market jump volatility measure is based on the realized jumps identified from high-frequency stock market returns using the bi-power variation technique. The significant enhancement of bond return predictability is robust to different forecasting horizons, to using non-overlapping returns and to the choice of different window sizes in computing the jump volatility. This market jump volatility factor also crowds out the price-dividend ratio in explaining much of the countercyclical movement in bond risk premia. We argue that this finding provides support for the unspanned stochastic volatility hypothesis according to which the conditional distribution of excess bond returns is affected by state variables that are not in the span of the term structure of yields and forward rates. |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2007-22&r=rmg |
| By: | Ralf Becker; Adam Clements |
| Abstract: | This paper presents a GARCH type volatility model with a time-varying unconditional volatility which is a function of macroeconomic information. It is an extension of the SPLINE GARCH model proposed by Engle and Rangel (2005). The advantage of the model proposed in this paper is that the macroeconomic information available (and/or forecasts)is used in the parameter estimation process. Based on an application of this model to S&P500 share index returns, it is demonstrated that forecasts of macroeconomic variables can be easily incorporated into volatility forecasts for share index returns. It transpires that the model proposed here can lead to significantly improved volatility forecasts compared to traditional GARCH type volatility models. |
| Keywords: | Volatility, macroeconomic data, forecast, spline, GARCH. |
| JEL: | C12 C22 G00 |
| Date: | 2007–06–14 |
| URL: | http://d.repec.org/n?u=RePEc:qut:auncer:2007-93&r=rmg |