Suchergebnisse

We develop a misspecification test for the multiplicative two-component GARCH-MIDAS model suggested in Engle et al. (2013). In the GARCH-MIDAS model a short-term unit variance GARCH component fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange Multiplier statistic for testing the null hypothesis that the variable has no explanatory power. Hence, under the null hypothesis the long-term component is constant and the GARCH-MIDAS reduces to the simple GARCH model. We derive the asymptotic theory for our test statistic and investigate its finite sample properties by Monte-Carlo simulation. The usefulness of our procedure is illustrated by an empirical application to S&P 500 return data.

We introduce a method for measuring default risk connectedness of euro zone sovereign states using credit default swap (CDS) and bond data. The connectedness measure is based on an out-of-sample variance decomposition of model forecast errors. Due to its predictive nature, it can respond more quickly to crisis occurrences than common in-sample techniques. We determine sovereign default risk connectedness with both CDS and bond data for a more comprehensive picture of the system. We find evidence that several observable factors drive the difference of CDS and bonds, but both data sources still contain specific information for connectedness spill-overs. Generally, we can identify countries that impose risk on the system and the respective spill-over channels. In our empirical analysis we cover the years 2009-2014, such that recovery paths of countries exiting EU and IMF financial assistance schemes and responses to the ECB's unconventional policy measures can be analyzed.

We introduce a method for measuring default risk connectedness of euro zone sovereign states using credit default swap (CDS) and bond data. The connectedness measure is based on an out-of-sample variance decomposition of model forecast errors. Due to its predictive nature, it can respond more quickly to crisis occurrences than common in-sample techniques. We determine sovereign default risk connectedness with both CDS and bond data for a more comprehensive picture of the system. We find evidence that several observable factors drive the difference of CDS and bonds, but both data sources still contain specific information for connectedness spill-overs. Generally, we can identify countries that impose risk on the system and the respective spill-over channels. In our empirical analysis we cover the years 2009-2014, such that recovery paths of countries exiting EU and IMF financial assistance schemes and responses to the ECB's unconventional policy measures can be ...

In practice, multivariate dependencies between extreme risks are often only assessed in a pairwise way. We propose a test to detect when tail dependence is truly high{dimensional and bivariate simpli cations would produce misleading results. This occurs when a signi cant portion of the multivariate dependence structure in the tails is of higher dimension than two. Our test statistic is based on a decomposition of the stable tail dependence function, which is standard in extreme value theory for describing multivariate tail dependence. The asymptotic properties of the test are provided and a bootstrap based nite sample version of the test is suggested. A simulation study documents the good performance of the test for standard sample sizes. In an application to international government bonds, we detect a high tail{risk and low return situation during the last decade which can essentially be attributed to increased higher-order tail risk. We also illustrate the empirical consequences from ignoring higher-dimensional tail risk.

In practice, multivariate dependencies between extreme risks are often only assessed in a pairwise way. We propose a test to detect when tail dependence is truly high-dimensional and bivariate simplifications would produce misleading results. This occurs when a significant portion of the multivariate dependence structure in the tails is of higher dimension than two. Our test statistic is based on a decomposition of the stable tail dependence function, which is standard in extreme value theory for describing multivariate tail dependence. The asymptotic properties of the test are provided and a bootstrap based finite sample version of the test is suggested. A simulation study documents the good performance of the test for standard sample sizes. In an application to international government bonds, we detect a high tail{risk and low return situation during the last decade which can essentially be attributed to increased higher{order tail risk. We also illustrate the empirical consequences from ignoring higher-dimensional tail risk.

In practice, multivariate dependencies between extreme risks are often only assessed in a pairwise way. We propose a test to detect when tail dependence is truly high{dimensional and bivariate simplifications would produce misleading results. This occurs when a significant portion of the multivariate dependence structure in the tails is of higher dimension than two. Our test statistic is based on a decomposition of the stable tail dependence function, which is standard in extreme value theory for describing multivariate tail dependence. The asymptotic properties of the test are provided and a bootstrap based finite sample version of the test is suggested. A simulation study documents the good performance of the test for standard sample sizes. In an application to international government bonds, we detect a high tail-risk and low return situation during the last decade which can essentially be attributed to increased higher-order tail risk. We also illustrate the empirical consequences from ignoring higher-dimensional tail risk.