In financial market risk measurement, Value-at-Risk (VaR) techniques have proven to be a very useful and popular tool. Unfortunately, most VaR estimation models suffer from major drawbacks: the lognormal (Gaussian) modeling of the returns does not take into account the observed fat tail distribution and the non-stationarity of the financial instruments severely limits the efficiency of the VaR ...

One of the most commonly used methods for modeling multivariate time series is the Vector Autoregressive Model (VAR). VAR is generally used to identify lead, lag and contemporaneous relationships describing Granger causality within and between time series. In this paper, we investigate VAR methodology for analyzing data consisting of multilayer time series which are spatially interdependent. Wh...

In this paper, we assess the magnitude of model uncertainty of credit risk portfolio models, i.e., what is the maximum and minimum Value-at-Risk (VaR) that can be justified given a certain amount of available information. Puccetti and Rüschendorf (2012b) and Embrechts et al. (2013) propose the rearrangement algorithm (RA) as a general method to approximate VaR bounds when the default probabilit...

This paper proposes the use of Bayesian approach to implement Value at Risk (VaR) model for both linear and non-linear portfolios. The Bayesian approach provides risk traders with the flexibility of adjusting their VaR models according to their subjective views. First, we deal with the case of linear portfolios. By imposing the conjugate-prior assumptions, a closed-form expression for the Bayes...

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...

Modeling and Estimation of High-dimensional Vector Autoregressions by Sumanta Basu Chair: George Michailidis Vector Autoregression (VAR) represents a popular class of time series models in applied macroeconomics and finance, widely used for structural analysis and simultaneous forecasting of a number of temporally observed variables. Over the years it has gained popularity in the fields of cont...

Background: In light of the latest global financial crisis and the ongoing sovereign debt crisis, accurate measuring of market losses has become a very current issue. One of the most popular risk measures is Value-at-Risk (VaR). Objectives: Our paper has two main purposes. The first is to test the relative performance of selected GARCH-type models in terms of their ability of delivering volatil...