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Vorgestellt wird eine empirische Studie, welche den Zusammenhang zwischen Rendite und Risiko für ein Sample deutscher Versicherungsaktien im Zeitraum 1975-1998 untersucht. Als Methode wurde ein Multifaktorenmodell mit makroökonomischen Faktoren verwendet. Je nach Untersuchungszeitraum beläuft sich der Anteil der erklärten Varianz auf 9,29% bis 13,62%. Es konnte eine signifikanter negativer Einfluß zwischen der Veränderung des allgemeinen Zinsniveaus und den Risikoprämien von Versicherungsaktien identifiziert werden. Weiterhin ist Wechselkurses der DM zum US-Dollar signifikant.
Ambivalence in the regulatory definition of capital adequacy for credit risk has recently stirred the financial services industry to collateral loan obligations (CLOs) as an important balance sheet management tool. CLOs represent a specialised form of Asset-Backed Securitisation (ABS), with investors acquiring a structured claim on the interest proceeds generated from a portfolio of bank loans in the form of tranches with different seniority. By way of modelling Merton-type risk-neutral asset returns of contingent claims on a multi-asset portfolio of corporate loans in a CLO transaction, we analyse the optimal design of loan securitisation from the perspective of credit risk in potential collateral default. We propose a pricing model that draws on a careful simulation of expected loan loss based on parametric bootstrapping through extreme value theory (EVT). The analysis illustrates the dichotomous effect of loss cascading, as the most junior tranche of CLO transactions exhibits a distinctly different default tolerance compared to the remaining tranches. By solving the puzzling question of properly pricing the risk premium for expected credit loss, we explain the rationale of first loss retention as credit risk cover on the basis of our simulation results for pricing purposes under the impact of asymmetric information. Klassifikation: C15, C22, D82, F34, G13, G18, G20
This paper shows that emerging market eurobond spreads after the Asian crisis can be almost completely explained by market expectations about macroeconomic fundamentals and international interest rates. Contrary to the claim that emerging market bond spreads are driven by market variables such as stock market volatility in the developed countries, it is found that this did not play a significant role after the Asian crisis. Using panel data techniques, it is shown that the determinants of bond spreads can be divided into long-term structural variables and medium-term variables which explain month-to-month changes in bond spreads. As relevant medium-term variables, ''consensus forecasts'' of real GDP growth and inflation, and international interest rates are identified. The long-term structural factors do not explicitly enter the model and show up as fixed or random country-specific effects. These intercepts are highly correlated with the countries' credit rating.
This paper provides an in-depth analysis of the properties of popular tests for the existence and the sign of the market price of volatility risk. These tests are frequently based on the fact that for some option pricing models under continuous hedging the sign of the market price of volatility risk coincides with the sign of the mean hedging error. Empirically, however, these tests suffer from both discretization error and model mis-specification. We show that these two problems may cause the test to be either no longer able to detect additional priced risk factors or to be unable to identify the sign of their market prices of risk correctly. Our analysis is performed for the model of Black and Scholes (1973) (BS) and the stochastic volatility (SV) model of Heston (1993). In the model of BS, the expected hedging error for a discrete hedge is positive, leading to the wrong conclusion that the stock is not the only priced risk factor. In the model of Heston, the expected hedging error for a hedge in discrete time is positive when the true market price of volatility risk is zero, leading to the wrong conclusion that the market price of volatility risk is positive. If we further introduce model mis-specification by using the BS delta in a Heston world we find that the mean hedging error also depends on the slope of the implied volatility curve and on the equity risk premium. Under parameter scenarios which are similar to those reported in many empirical studies the test statistics tend to be biased upwards. The test often does not detect negative volatility risk premia, or it signals a positive risk premium when it is truly zero. The properties of this test furthermore strongly depend on the location of current volatility relative to its long-term mean, and on the degree of moneyness of the option. As a consequence tests reported in the literature may suffer from the problem that in a time-series framework the researcher cannot draw the hedging errors from the same distribution repeatedly. This implies that there is no guarantee that the empirically computed t-statistic has the assumed distribution. JEL: G12, G13 Keywords: Stochastic Volatility, Volatility Risk Premium, Discretization Error, Model Error
Traditional tests of the CAPM following the Fama / MacBeth (1973) procedure are tests of the joint hypotheses that there is a relationship between beta and realized return and that the market risk premium is positive. The conditional test procedure developed by Pettengill / Sundaram / Mathur (1995) allows to independently test the hypothesis of a relation between beta and realized returns. Monte Carlo simulations show that the conditional test reliably identifies this relation. In an empirical examination for the German stock market we find a significant relation between beta and return. Previous studies failed to identify this relationship probably because the average market risk premium in the sample period was close to zero. Our results provide a justification for the use of betas estimated from historical return data by portfolio managers.