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Market Risk


Enterprise risk management (ERM) has been the topic of increased media attention in recent years. Many organizations have implemented ERM programs, consulting firms have established specialized ERM units, and universities have developed ERM-related courses and research centers. Despite the heightened interest in ERM by academics and practitioners, there is an absence of empirical evidence regarding the impact of such programs on firm value. The objective of this study is to measure the extent to which specific firms have implemented ERM programs and, then, to assess the value implications of these programs. We focus our attention in this study on U.S. insurers in order to control for differences that might arise from regulatory and market differences across industries. We use a maximum-likelihood treatment effects framework to simultaneously model the determinants of ERM and the effect of ERM on firm value. In our ERM-choice equation we find ERM usage to be positively related to factors such as firm size and institutional ownership, and negatively related to reinsurance use, leverage, and asset opacity. By focusing on publicly-traded insurers we are able to estimate the effect of ERM on Tobin’s Q, a standard proxy for firm value. We find a positive relation between firm value and the use of ERM. The ERM premium of 16.5% is statistically and economically significant and is robust to a range of alternative specifications of both the ERM and value equations.
[Authors: Robert E. Hoyt, University of Georgia - C. Herman and Mary Virginia Terry College of Business / Andre P. Liebenberg, University of Mississippi - School of Business Administration]
Hoyt 2623 Downloads 07.01.2010
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Several generalisations of the Black–Scholes (BS) Model have been made in the literature to overcome the well–known empirical inadequacies of the BS–Model. In this work I perform an empirical comparison of stochastic volatility models established by Du?e et al. (2000) with jumps in the volatility and four deductive special cases. In addition I include the model of Schoebel/Zhu (1999) with volatility driven by an Ornstein–Uhlenbeck process instead of a Cox–Ingersoll–Ross process. As Zhu (2000) suggested the model can be easily combined with a jump component in the underlying. I examine the resulting model empirically and stress its good properties. This comparison embeds out–of–sample pricing performance as an important element in a model performance study based on model risk. The main result in terms of ?t performance is that the most complex models are not always the best ones. It is important to quantify model risk like e.g. Cont (2004) and to examine the sensitivity of exotic options in terms of moneyness, maturity and market condition. To achieve this comparison the model risk measure of Cont (2004) is extended and applied to various exotic options.
manuelaender 2663 Downloads 15.11.2009
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Index-linked notes/securities (ILS) are defined as debt instruments for which the amounts of the coupon payments (interest) and/or the principal outstanding are linked to the movements of a stock market or price index. ILS are securities whose values are aggregates of the cash flows of asset pools and depend generally on the performance of an underlying aggregated index. This means they are linked to a basket of stocks or to other composite securities representing a constant portfolio over a longer time period. Index-linked notes/securities are sometimes referred to equity index-linked notes or real yield securities (REALS), if they are linked to an equity index. The underlying assets are the values of individual equity securities.
[Source: Stefanie Kipp: Index-Linked Notes/Securities, in: Peter Moles (ed.): Encyclopaedia of Financial Engineering and Risk Management, New York & London: Routledge, 2005]
StefanieKipp 13923 Downloads 24.11.2008
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Bei einer Analyse der US-amerikanischen Subprime-Krise, die sich zu einer weltweiten Finanzkrise ausgeweitet hat, stellt man sich die Frage, ob diese unvorhersehbar und ein unglücklicher Zufall war. Und ist die aktuelle Krise tatsächlich, wie Josef Ackermann, Vorstandsvorsitzender der Deutschen Bank sagt, ein Zeichen von Marktversagen, das staatliche Eingriffe erfordert? Tatsächlich waren vielen Führungskräften von Kreditinstituten potenzielle Risiken aus den extrem gestiegenen Preisen amerikanischer Immobilien und der exzessiven Kreditvergabepolitik ebenso bewusst, wie die vergleichsweise geringe Transparenz vieler derivativer Finanzprodukte (etwa der Collateralized Debt Obligations). Wie konnte es dann zu einer derartigen Krise kommen? Im Folgenden werden Erklärungen zusammengefasst, die teilweise überraschend sein mögen – aber mit einem etwas tieferen Blick in die Finanz- und Bankenlandschaft an vielen Stellen verifiziert werden können.
[Quelle: Gleißner, W./Romeike, F.: Analyse Subprime-Krise: Risikoblindheit und Methodikschwächen, in: RISIKO MANAGER 21/2008, S. 1, 8-12.]
Romeike 7319 Downloads 30.10.2008
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The key to truly effective risk management lies in the behavior or markets during times of crisis, when investment value is most at risk. Observing markets under stress teaches important lessons about the role and dynamics of markets and the implications of risk management.
Bookstaber 10382 Downloads 10.10.2008
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The 1998 failure of Long-Term Capital Management (LTCM) is said to have nearly blown up the world's financial system. For such a near-catastrophic event, the finance profession has precious little information to draw from. By piecing together publicly available information, this paper draws lessions from risk management practices at LTCM.
Jorion 10281 Downloads 10.10.2008
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The focus of this article is on providing a methodology to implement the second alternative. We propose a tool to measure Value-at-Risk (VaR) associated economic and financial events, and demonstrate how it can be used to measure the financial risk associated with EMU-related stress scenarios. This tool, which we refer to as ESSA - Exploratory Stress Scenario Analysis - allows risk managers to automatically and consistently adjust VaR estimates to reflect their views on market price movements, market uncertainty and co-movements among markets.
Zangari 11052 Downloads 10.10.2008
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Aktuell vollzieht sich ein Paradigmenwechsel im Risikomanagement. Praktiker beginnen zu verstehen, dass extreme Ausschläge an Finanzmärkten möglichst realitätsgetreu abzubilden sind. Die Fokussierung auf das Phänomen starker Schwankungen ist aufgrund der steigenden Anforderungen an das Risikomanagement sowie wegen der höheren Komplexität vieler Finanzprodukte unbedingt erforderlich. Dadurch wird die Zukunftsfähigkeit herkömmlicher Ansätze grundsätzlich in Frage gestellt. Der vorliegende Artikel beleuchtet eine viel versprechende Klasse von Wahrscheinlichkeitsverteilungen, die diesen gewachsenen Ansprüchen gerecht wird: die α-stabile Verteilungsklasse.
mbuttler 7332 Downloads 22.05.2008
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We extend the class of GARCH models to comprise asymmetric and nonlinear effects on volatility. In particular, we do not only explain future volatility of a time series on its own past, but allow for external influences and spillovers between capital markets. For this generalized class of models, the asymptotic behavior of the Quasi-Maximum-Likelihood estimator of model parameters is derived. The models are applied to time series of fx-rates. It is found that in particular the simple asymmetric models lead to improved performance.
Wehrspohn 9552 Downloads 14.04.2008
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Do investors obtain their long term returns smoothly and steadily over time, or is their long term performance largely determined by the return of just a few outliers? How likely are investors to successfully predict the best days to be in and out of the market? The evidence from 15 international equity markets and over 160,000 daily returns indicates that a few outliers have a massive impact on long term performance. On average across all 15 markets, missing the best 10 days resulted in portfolios 50.8% less valuable than a passive investment; and avoiding the worst 10 days resulted in portfolios 150.4% more valuable than a passive investment. Given that 10 days represent less than 0.1% of the days considered in the average market, the odds against successful market timing are staggering.
Estrada 7481 Downloads 13.02.2008
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