This paper proposes a new nonparametric test for conditional independence, which is based on the comparison of Bernstein copula densities using the Hellinger distance. The test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in general settings since there is no restriction on the dimension of the data. In fact, to apply the test, only a bandwidth is needed for the nonparametric copula. We prove that the test statistic is asymptotically pivotal under the null hypothesis, establish local power properties, and motivate the validity of the bootstrap technique that we use in finite sample settings. A simulation study illustrates the good size and power properties of the test. We illustrate the empirical relevance of our test by focusing on Granger causality using financial time series data to test for nonlinear leverage versus volatility feedback effects and to test for causality between stock returns and trading volume. In a third application, we investigate Granger causality between macroeconomic variables. JEL Classification: C12; C14; C15; C19; G1; G12; E3; E4; E52.
Recent research identifies several industry-related patterns that standard asset pricing models cannot explain effectively. This paper investigates what explains the crosssection of returns of firms in the oil industry and, in particular, how well an oil factor performs in comparison with the common systematic factors identified in the literature. We conduct a time series analysis and demonstrate that the oil factor has substantial explanatory power over traditional factors. A cross-sectional regression shows that the size, momentum and oil factors are associated with a positive risk premium and are able to explain the cross-sectional variation in stock returns in the
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AbstractThe reaction of EU bond and equity market volatilities to sovereign rating announcements (Standard & Poor's, Moody's, and Fitch) is investigated using a panel of daily stock market and sovereign bond returns. The parametric volatilities are filtered using EGARCH specifications. The estimation results show that upgrades do not have significant effects on volatility, but downgrades increase stock and bond market volatility. Contagion is present, with sovereign rating announcements creating interdependence among European financial markets with upgrades (downgrades) in one country leading to a decrease (increase) in volatility in other countries. The empirical results show also a financial gain and risk (value-at-risk) reduction for portfolio returns when taking into account sovereign credit ratings' information for volatility modelling, with financial gains decreasing with higher risk aversion.JEL: C22; C23; E44; G11; G15; H30.
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