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on Risk Management |
| By: | Michael S. Gibson |
| Abstract: | The striking growth of credit derivatives suggests that market participants find them to be useful tools for risk management. I illustrate the value of credit derivatives with three examples. A commercial bank can use credit derivatives to manage the risk of its loan portfolio. An investment bank can use credit derivatives to manage the risks it incurs when underwriting securities. An investor, such as an insurance company, asset manager, or hedge fund, can use credit derivatives to align its credit risk exposure with its desired credit risk profile.> However, credit derivatives pose risk management challenges of their own. I discuss five of these challenges. Credit derivatives can transform credit risk in intricate ways that may not be easy to understand. They can create counterparty credit risk that itself must be managed. Complex credit derivatives rely on complex models, leading to model risk. Credit rating agencies interpret this complexity for investors, but their ratings can be misunderstood, creating rating agency risk. The settlement of a credit derivative contract following a default can have its own complications, creating settlement risk. For the credit derivatives market to continue its rapid growth, market participants must meet these risk management challenges. |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2007-47&r=rmg |
| By: | Michele Bonollo; Davide Morandi; Chiara Pederzoli; Costanza Torricelli |
| Abstract: | The increasing use of internal market models for market risk assessment and management promotes, in compliance with Basel II, better risk management practices but introduces at the same time the so called model risk. In the light of the many open issues connected to market risk, the aim of this paper is twofold. First, it offers a formal analysis of model risk which is aimed to clarify quantification issues and to illustrate the architecture of a control process for this type of risk. An important building block of such an architecture is the so called market parameters control process, which is the focus of the present paper and consists of two different phases: the definition of the data sources and the data retrieval forms, and the definition of the techniques for valuing variables (i.e. input model data) based on market data. Second, this paper proposes a market parameters control process and its implementation within an important Italian bank, namely Gruppo Banco Popolare. Specifically, by focusing on equity market risk, this paper illustrates the whole organization process needed to set up and implement the market parameters control techniques, which imply first controlling for integrity (existence, domain, homogeneity) and outliers and then performing benchmarking activities. Special emphasis is placed on the so-called second level parameters, which do not have official quotes and still are fundamental especially in valuing non linear positions (e.g. volatility). These activities are based on mathematical-statistical models, whose implementation has required the development of specific software and IT solutions and the adoption of an articulate organizational structure. |
| Keywords: | model risk; market parameters; control process |
| JEL: | G21 C10 |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:mod:wcefin:07102&r=rmg |
| By: | Kovačić, Zlatko |
| Abstract: | This paper investigates the behavior of stock returns in an emerging stock market namely, the Macedonian Stock Exchange, focusing on the relationship between returns and conditional volatility. The conditional mean follows a GARCH-M model, while for the conditional variance one symmetric (GARCH) and four asymmetric GARCH types of models (EGARCH, GJR, TARCH and PGARCH) were tested. We examine how accurately these GARCH models forecast volatility under various error distributions. Three distributions were assumed, i.e. Gaussian, Student-t and Generalized Error Distribution. The empirical results show the following: (i) the Macedonian stock returns time series display stylized facts such as volatility clustering, high kurtosis, and low starting and slow-decaying autocorrelation function of squared returns; (ii) the asymmetric models show a little evidence on the existence of leverage effect; (iii) the estimated mean equation provide only a weak evidence on the existence of risk premium; (iv) the results are quite robust across different error distributions; and (v) GARCH models with non-Gaussian error distributions are superior to their counterparts estimated under normality in terms of their in-sample and out-of-sample forecasting accuracy. |
| Keywords: | Stock market; forecasting volatility; South-Eastern Europe; GARCH models; non-Gaussian error distribution; Macedonia |
| JEL: | G12 C52 C22 |
| Date: | 2007–10–24 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:5319&r=rmg |
| By: | Dorn, Daniel; Huberman, Gur |
| Abstract: | The preferred risk habitat hypothesis, introduced here, is that individual investors select stocks with volatilities commensurate with their risk aversion; more risk-averse individuals pick lower-volatility stocks. The investors' portfolio perspective overlooks return correlations. The data, 1995-2000 holdings of over 20,000 customers of a German broker, are consistent with the predictions of the hypothesis: the portfolios contain highly similar stocks in terms of volatility, when stocks are sold they are replaced by stocks of similar volatilities, and the more risk averse customers indeed hold less volatile stocks. Cross-sectionally, the more risk averse investors also have a stronger tendency to invest in mutual funds. Major improvements in diversification are concentrated during periods when investors add money to their account. |
| Keywords: | preferred risk habitat; risk; risk aversion; stock portfolio; volatility |
| JEL: | G10 |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:6532&r=rmg |