|
on Risk Management |
| By: | Jan Pieter Krahnen (Center for Financial Studies and University of Frankfurt); Christian Wilde (University of Frankfurt) |
| Abstract: | Large banks often sell part of their loan portfolio in the form of collateralized debt obligations (CDO) to investors. In this paper we raise the question whether credit asset securitization affects the cyclicality (or commonality) of bank equity values. The commonality of bank equity values reflects a major component of systemic risks in the banking market, caused by correlated defaults of loans in the banks’ loan books. Our simulations take into account the major stylized fact of CDO transactions, the nonproportional nature of risk sharing that goes along with tranching. We provide a theoretical framework for the risk transfer through securitization that builds on a macro risk factor and an idiosyncratic risk factor, allowing an identification of the types of risk that the individual tranche holders bear. This allows conclusions about the risk positions of issuing banks after risk transfer. Building on the strict subordination of tranches, we first evaluate the correlation properties both within and across risk classes. We then determine the effect of securitization on the systematic risk of all tranches, and derive its effect on the issuing bank’s equity beta. The simulation results show that under plausible assumptions concerning bank reinvestment behaviour and capital structure choice, the issuing intermediary’s systematic risk tends to rise. We discuss the implications of our findings for financial stability supervision. |
| JEL: | G28 |
| Date: | 2006–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:cfs:cfswop:wp200604&r=rmg |
| By: | Toker Doganoglu (University of Munich); Christoph Hartz (University of Munich); Stefan Mittnik (University of Munich, Center for Financial Studies and ifo) |
| Abstract: | Assumptions about the dynamic and distributional behavior of risk factors are crucial for the construction of optimal portfolios and for risk assessment. Although asset returns are generally characterized by conditionally varying volatilities and fat tails, the normal distribution with constant variance continues to be the standard framework in portfolio management. Here we propose a practical approach to portfolio selection. It takes both the conditionally varying volatility and the fat-tailedness of risk factors explicitly into account, while retaining analytical tractability and ease of implementation. An application to a portfolio of nine German DAX stocks illustrates that the model is strongly favored by the data and that it is practically implementable. |
| Keywords: | Multivariate Stable Distribution, Index Model, Portfolio Optimization, Value-at-Risk, Model Adequacy |
| JEL: | C13 C32 G11 G14 G18 |
| Date: | 2006–11–03 |
| URL: | http://d.repec.org/n?u=RePEc:cfs:cfswop:wp200624&r=rmg |
| By: | Petr Jakubík (Czech National Bank; Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic) |
| Abstract: | This paper deals with credit risk in the Czech aggregate economy. It follows structural Merton's approach. A latent factor model is employed within this framework. Estimation of this model can help to understand relation between credit risk and macroeconomic indicators. The credit risk model of the Czech aggregate economy was estimated in this manner for purpose of stress testing. The results of this study can be used for stress testing of banking sector. |
| Keywords: | banking, credit risk, latent factor model, default rate, stress test |
| JEL: | G21 G28 G33 |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2007_11&r=rmg |
| By: | Caiado, Jorge; Crato, Nuno |
| Abstract: | In this paper, we introduce a volatility-based method for clustering analysis of financial time series. Using the generalized autoregressive conditional heteroskedasticity (GARCH) models we estimate the distances between the stock return volatilities. The proposed method uses the volatility behavior of the time series and solves the problem of different lengths. As an illustrative example, we investigate the similarities among major international stock markets using daily return series with different sample sizes from 1966 to 2006. From cluster analysis, most European markets countries, United States and Canada appear close together, and most Asian/Pacific markets and the South/Middle American markets appear in a distinct cluster. After the terrorist attack on September 11, 2001, the European stock markets have become more homogenous, and North American markets, Japan and Australia seem to come closer. |
| Keywords: | Cluster analysis; GARCH; International stock markets; Volatility. |
| JEL: | C32 G15 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:2074&r=rmg |
| By: | Huisman, R.; Mahieu, R.J.; Schlichter, F. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University) |
| Abstract: | This paper deals with the question how an electricity end-consumer or distribution company should structure its portfolio with energy forward contracts. This paper introduces a one period framework to determine optimal positions in peak and off-peak contracts in order to purchase future consumption volume. In this framework, the end-consumer or distribution company is assumed to minimize expected costs of purchasing respecting an ex-ante risk limit defined in terms of Value at Risk. Based on prices from the German EEX market, it is shown that a risk-loving agent is able to obtain lower expected costs than for a risk-averse agent. |
| Keywords: | Electricity prices;Mean variance;Hedge ratios;Forward risk premium; |
| Date: | 2007–02–21 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureri:30009941&r=rmg |
| By: | Caiado, Jorge; Crato, Nuno; Peña, Daniel |
| Abstract: | Previous studies have investigated the comovements of international equity returns by using mean correlations, cointegration, common factor analysis, and other approaches. This paper investigates the evolution of the affinity among major euro and non-euro area stock markets in the period 1966-2006 by using distance-based methods for clustering analysis of time series. A periodogram-based metric for mean and squared returns is used to compute distances between the series. This method solves the shortcoming of unequal sample sizes found for different countries. Then, by using dendrogram and multidimensional scaling techniques based on the computed distances, we display clusters for the series of returns and volatilities. The data were divided into two sample periods: previous and subsequent to the introduction of the euro as an electronic currency. For market returns, euro-area countries do not seem to come closer after the introduction of the euro. There is some identity that is maintained after 1998. For squared returns, we found a clear change with the introduction of the euro. Up to 1998, there is a weak linkage among euro area countries. After 1998, the euro area stock markets volatilities have become considerably more homogenous. For reference, we explored also the correlations among the series. We found that some stock markets within the European Monetary Union are strongly correlated in returns and in squared returns, and that some euro and non-euro area markets are not correlated in returns, but are weakly correlated in squared returns. |
| Keywords: | Cluster analysis; Euro area; International stock markets; Returns and squared returns; Periodogram; Volatility. |
| JEL: | G15 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:2069&r=rmg |
| By: | Christoph Hartz (University of Munich); Stefan Mittnik (University of Munich, Center for Financial Studies and ifo); Marc S. Paolella (University of Zurich) |
| Abstract: | A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step, fully data driven. The results for several different financial asset returns over a long out-of-sample forecasting period, as well as use of simulated data, strongly support use of the new method, and the performance is not sensitive to the choice of L. |
| Keywords: | Bootstrap, GARCH, Value-at-Risk |
| JEL: | C22 C53 C63 G12 |
| Date: | 2006–11–03 |
| URL: | http://d.repec.org/n?u=RePEc:cfs:cfswop:wp200623&r=rmg |
| By: | Torben G. Andersen; Luca Benzoni |
| Abstract: | We investigate whether bonds span the volatility risk in the U.S. Treasury market, as predicted by most 'affine' term structure models. To this end, we construct powerful and model-free empirical measures of the quadratic yield variation for a cross-section of fixed-maturity zero-coupon bonds ("realized yield volatility") through the use of high-frequency data. We find that the yield curve fails to span yield volatility, as the systematic volatility factors are largely unrelated to the cross-section of yields. We conclude that a broad class of affine diffusive, Gaussian-quadratic and affine jump-diffusive models is incapable of accommodating the observed yield volatility dynamics. An important implication is that the bond markets per se are incomplete and yield volatility risk cannot be hedged by taking positions solely in the Treasury bond market. We also advocate using the empirical realized yield volatility measures more broadly as a basis for specification testing and (parametric) model selection within the term structure literature. |
| JEL: | C14 C32 G12 |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:12962&r=rmg |
| By: | Kilian, Lutz; Park, Cheolbeom |
| Abstract: | While there is a strong presumption in the financial press that oil prices drive the stock market, the empirical evidence on the impact of oil price shocks on stock prices has been mixed. This paper shows that the response of aggregate stock returns may differ greatly depending on whether the increase in the price of crude oil is driven by demand or supply shocks in the crude oil market. The conventional wisdom that higher oil prices necessarily cause lower returns is shown to apply only to oil-market specific demand shocks such as increases in the precautionary demand for crude oil that reflect fears about the availability of future oil supplies. In contrast, positive shocks to the global aggregate demand for industrial commodities are shown to cause both higher real oil prices and higher stock prices. Shocks to the global production of crude oil, while not trivial, are far less important for understanding changes in stock prices than shocks to global aggregate demand and shocks to the precautionary demand for oil. Further insights can be gained from the responses of industry-specific stock returns to demand and supply shocks in the crude oil market. We identify the sectors most sensitive to these shocks and study the opportunities for adjusting one’s portfolio in response to oil market disturbances. |
| Keywords: | Demand shocks; Oil prices; Stock returns; Supply Shocks |
| JEL: | G12 Q43 |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:6166&r=rmg |
| By: | Jules H. van Binsbergen; Michael W. Brandt |
| Abstract: | We study the impact of regulations on the investment decisions of a defined benefits pension plan. We assess the influence of ex ante (preventive) and ex post (punitive) risk constraints on the gains to dynamic, as opposed to myopic, decision making. We find that preventive measures, such as Value-at-Risk constraints, tend to decrease the gains to dynamic investment. In contrast, punitive constraints, such as mandatory additional contributions from the sponsor when the plan becomes underfunded, lead to very large utility gains from solving the dynamic program. We also show that financial reporting rules have real effects on investment behavior. For example, the current requirement to discount liabilities at a rolling average of yields, as opposed to at current yields, induces grossly suboptimal investment decisions. |
| JEL: | G0 G11 G23 |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:12970&r=rmg |