A variety of methods is available to estimate a portfolio’s Value-at-Risk. Aside from the overall VaR there is an apparent need for information about marginal VaR, component VaR and incremental VaR. Expressions for these VaR metrics have been derived under the restrictive normality assumption. In this paper we investigate these VaR concepts in an elliptical world and in a general distribution-f...

Antigenic variation in the human malaria parasite Plasmodium falciparum depends on the transcriptional regulation of the var gene family. In each individual parasite, mRNA is expressed exclusively from 1 var gene out of ∼60, while the rest of the genes are transcriptionally silenced. Both modifications to chromatin structure and DNA regulatory elements associated with each var gene have been im...

I quantify the importance of financial structure, labor market rigidities and industry mix for cross-country asymmetries in monetary transmission. To do so, I determine how closely the impulse responses to a monetary policy shock obtained from country-specific vectorautoregressive (VAR) models and a non-standard panel VAR model match. In the country-specific VAR models, the impulse responses va...

This paper intends to critically evaluate state-of-the-art methodologies for calculating the VaR of non-linear portfolios from the point of view of computational accuracy and efficiency. We focus on the quadratic portfolio model, also known as “DeltaGamma,” and, as a working assumption, we model risk factor returns as multi-normal random variables. We present the main approaches to Delta-Gamma ...

In this paper we propose a method to estimate the value-at-risk (VaR) of a portfolio based on a combination of time series, extreme value theory and copula fitting. Given multivariate financial data, we use a univariate ARMA-GARCH model for each return series. We then fit a generalized Pareto distribution to the tails of the residuals to model the distributions of marginal residuals, followed b...

Several authors, including Andersen and Bollerslev (1998), stress the importance of long-term volatility dependence for value-at-risk (VaR) prediction. The present paper addresses multiple-period market risk forecasts under long memory persistence in market volatility. To this aim, we propose volatility forecasts based on a combination of the GARCH(1,1)-model with potentially fat-tailed and ske...

Over the last decade, researchers, practitioners, and regulators have had intense debates about how to treat the data collection threshold in operational risk modeling. Several approaches have been employed to fit the loss severity distribution: the empirical approach, the “naive” approach, the shifted approach, and the truncated approach. Since each approach is based on a different set of assu...

[1] Increasing attention is dedicated to the implementation of suitable marine forecast systems for the estimate of the state of the ocean. Within the framework of the European MyOcean infrastructure, the pre-existing short-term Mediterranean Sea biogeochemistry operational forecast system has been upgraded by assimilating remotely sensed ocean color data in the coupled transport-biogeochemical...

Multivariate time-series modeling and forecasting is an important problem with numerous applications. Traditional approaches such as VAR (vector auto-regressive) models and more recent approaches such as RNNs (recurrent neural networks) are indispensable tools in modeling time-series data. In many multivariate time series modeling problems, there is usually a significant linear dependency compo...

This paper aims to investigate a Bayesian sampling approach to parameter estimation in the semiparametric GARCH model with an unknown conditional error density, which we approximate by a mixture of Gaussian densities centered at individual errors and scaled by a common standard deviation. This mixture density has the form of a kernel density estimator of the errors with its bandwidth being the ...