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We propose a novel ranking model and a complementary predictive ability test statistic to investigate the forecasting performances of different Value at Risk (VaR) methods. The ranking model develops a unified framework which penalizes excessive capital allocation, autocorrelation of violations...
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We investigate capital requirements based on Value at Risk (V@R) and Average Value at Risk (AV@R) when the bank's econometric model only approximately describes the true, unknown return generating process, as is often the case in practice. We provide a simple formula for such capital...
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extreme value theory (EVT) to propose a multivariate estimation procedure for value-at-risk (VaR) and expected shortfall (ES … estimators of market risk. Despite advances in the theory and practice of evaluating risk, existing measures are notoriously poor … contrast them with the popular Gaussian GARCH estimator in an extensive Monte Carlo simulation. The method we propose generally …
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The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period Value-at-Risk (VaR) and Expected Shortfall (ES) across 20 stock indices worldwide. The dataset is comprised of daily data covering...
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linear estimation and propose a bootstrap procedure for conducting inference. By employing monthly data from the Dutch …
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We examine the (potentially nonlinear) relationship between inequality and growth using a method which does not require an a priori assumption on the underlying functional form. This approach reveals a plateau completely missed by commonly used (nonlinear) parametric approaches - the economy...
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