In this paper, we construct a single composite financial stress indicator (FSI) which aims to predict developments in the real economy in the euro area. Our FSI contains financial variables that have a causal relationship with the real economy. Therefore, our FSI is able to serve as an early warning indicator for negative impacts of financial stress on the real economy.The causal relationship between our FSI and the real economy is tested and confirmed by the error-correction models. An empirical analysis reveals that our FSI has more predictive power than the bench-mark normally used for financial markets, especially stock markets, namely the Euro STOXX 50 volatility index for the recent banking crisis and the euro-area sovereign debt crisis. One of the main empirical results is that our FSI shows negative effects from financial markets on the real economy one to four months in advance. Nicht-technische Zusammenfassung AbstractIn this paper, we construct a single composite financial stress indicator (FSI) which aims to predict developments in the real economy in the euro area. Our FSI was shown to perform better than the Euro STOXX 50 volatility index for the recent banking crisis and the euro-area sovereign debt crisis and to be able to serve as an early warning indicator for negative impacts of financial stress on the real economy.

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Since the seminal work of Mandelbrot (1963),-stable distributions with in…nite variance have been regarded as a more realistic distributional assumption than the normal distribution for some economic variables, especially …nancial data. After providing a brief survey of theoretical results on estimation and hypothesis testing in regression models with in…nite-variance variables, we examine the statistical properties of the coe¢ cient of determination in models with-stable variables. If the regressor and error term share the same index of stability < 2, the coe¢ cient of determination has a nondegenerate asymptotic distribution on the entire [0; 1] interval, and the density of this distribution is unbounded at 0 and 1. We provide closed-form expressions for the cumulative distribution function and probability density function of this limit random variable. In contrast, if the indices of stability of the regressor and error term are unequal, the coe¢ cient of determination converges in probability to either 0 or 1, depending on which variable has the smaller index of stability. In an empirical application, we revisit the Fama-MacBeth two-stage regression and show that in the in…nite-variance case the coe¢ cient of determination of the second-stage regression converges to zero in probability even if the slope coe¢ cient is nonzero.

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Since the seminal work of Mandelbrot (1963),-stable distributions with in…nite variance have been regarded as a more realistic distributional assumption than the normal distribution for some economic variables, especially …nancial data. After providing a brief survey of theoretical results on estimation and hypothesis testing in regression models with in…nite-variance variables, we examine the statistical properties of the coe¢ cient of determination in models with-stable variables. If the regressor and error term share the same index of stability < 2, the coe¢ cient of determination has a nondegenerate asymptotic distribution on the entire [0; 1] interval, and the density of this distribution is unbounded at 0 and 1. We provide closed-form expressions for the cumulative distribution function and probability density function of this limit random variable. In contrast, if the indices of stability of the regressor and error term are unequal, the coe¢ cient of determination converges in probability to either 0 or 1, depending on which variable has the smaller index of stability. In an empirical application, we revisit the Fama-MacBeth two-stage regression and show that in the in…nite-variance case the coe¢ cient of determination of the second-stage regression converges to zero in probability even if the slope coe¢ cient is nonzero.