This paper compares and investigates the impact of different VaR models with conditional elliptical and stable distributed returns. In particular, we analyze some non-Gaussian VaR models and we discuss the applicability of some temporal aggregation rules. Thus, we propose and examine the performance of several VaR models: (i) an EWMA model with Student's t conditional distributions, (ii) a stab...

In this paper, Bayesian estimation and hypothesis testing are introduced for identified normalized Vector Autoregressive (VAR) models. A class of priors is proposed to take advantage of the structure of normalized VAR models. Efficient Markov Chain Monte Carlo algorithms are used for sampling from the posterior of the VAR parameters without using Metropolis algorithms. Marginal likelihoods are ...

abstract: with regard to the basel committee’s emphasis on the necessity of using 10-day value-at-risk (var) internal models in order to determine minimum market risk capital requirements, and downsides of the square-root-of-time rule, our purpose is to produce more accurate forecasts of the multi-period var using sixteen models for three stock indices, the tepix, nasdaq, and ftse. the results,...

In this article we investigate the theoretical behaviour of finite lag VAR(n) models fitted to time series that in truth come from an infinite order VAR(∞) data generating mechanism. We show that overall error can be broken down into two basic components, an estimation error that stems from the difference between the parameter estimates and their population ensemble VAR(n) counterparts, and an ...

Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a range of approaches will improve for...