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on Risk Management |
| By: | Günter Franke (Department of Economics, University of Konstanz); Jan Pieter Krahnen (Center for Financial Studies, Goethe-University Frankfurt) |
| Abstract: | This paper contributes to the economics of financial institutions risk management by exploring how loan securitization affects their default risk, their systematic risk, and their stock prices. In a typical CDO transaction a bank retains through a first loss piece a very high proportion of the default losses, and transfers only the extreme losses to other market participants. The size of the first loss piece is largely driven by the average default probability of the securitized assets. If the bank sells loans in a true sale transaction, it may use the proceeds to expand its loan business, thereby affecting systematic risk. For a sample of European CDO issues, we find an increase of the banks’ betas, but no significant stock price effect around the announcement of a CDO issue. |
| Date: | 2005–08–18 |
| URL: | http://d.repec.org/n?u=RePEc:knz:cofedp:0504&r=rmg |
| By: | Stephen Satchel (School of Finance and Economics, University of Technology, Sydney); Wei Xia (Birbeck College, University of London) |
| Abstract: | This paper constructs and compares various total return world stock indices based on daily data. Due to diversification these indices are noticeably similar. A diversification theorem identifies any diversified portfolio as a proxy for the growth optimal portfolio. The paper constructs a diversified world stock index that outperforms a number of other indices and argues that it is a good proxy for the growth optimal portfolio. This has applications to derivative pricing and investment management. |
| Keywords: | validation; credit analysis; rating model; ROC; Basel II |
| Date: | 2006–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:uts:rpaper:181&r=rmg |
| By: | Klaus Abberger (IFO Munich) |
| Abstract: | The procedures of estimating prediction intervals for ARMA processes can be divided into model based methods and empirical methods. Model based methods require knowledge of the model and the underlying innovation distribution. Empirical methods are based on the sample forecast errors. In this paper we apply nonparametric quantile regression to the empirical forecast errors using lead time as regressor. With this method there is no need for a distribution assumption. But for the data pattern in this case a double kernel method which allows smoothing in two directions is required. An estimation algorithm is presented and applied to some simulation examples. |
| Keywords: | Forecasting, Prediction intervals, Non normal distributions, Nonparametric estimation, Quantile regression |
| URL: | http://d.repec.org/n?u=RePEc:knz:cofedp:0202&r=rmg |
| By: | Günter Franke (Department of Economics, University of Konstanz); Harris Schlesinger (University of Alabama); Richard C. Stapleton (University of Manchester and University of Melbourne) |
| Abstract: | Although there has been much attention in recent years on the effects of additive background risks, the same is not true for its multiplicative counterpart. We consider random wealth of the multiplicative form xy, where x and y are statistically independent random variables. We assume that x is endogenous to the economic agent, but that y is an exogenous and nontradable background risk, which represents a type of market incompleteness. Our main focus is on how the presence of the multiplicative background risk y affects risk-taking behavior for decisions on the choice of x. We characterize conditions on preferences that lead to more cautious behavior. |
| Keywords: | multiplicative risks, background risk, incomplete markets, standard risk aversion, affiliated utility function, multiplicative risk vulnerability |
| JEL: | D81 |
| Date: | 2005–05 |
| URL: | http://d.repec.org/n?u=RePEc:knz:cofedp:0305&r=rmg |