The paper introduces the structure of parsimonious Portfolio Single Index (PSI) multivariate conditional and stochastic constant correlation volatility models, and methods for estimation of the underlying parameters. These multivariate estimates of volatility can be used for more accurate portfolio and risk management, to enable efficient forecasting of Value-at-Risk (VaR) thresholds, and to de...

We analyze extreme movements of the main stocks market indexes in the European Union. We find that the Sweden and UK markets are the preferred ones for risk averse investors since they present the best risk-return performance. Moreover, the UK market is found to have a very low dependence with the rest of the European financial cycles, being the best one (in terms of risk-return) available for ...

This paper intends to explain Venezuela’s country spread behavior through the use of Neural Networks, incorporating as input data the IGAEM’s (monthly economic activity general index, an index of economic indicators constructed by the BCV) VaR (value at risk), a measure of the shocks affecting country risk (country spreads) of emerging markets (the EMBI +Global) and EEUU’s short term interest r...

We estimate Value-at-Risk for sums of dependent random variables. We model multivariate dependent random variables using archimedean copulas. This structure allows one to calculate the asymptotic behaviour of extremal events. An important application of such results are Value-at-Risk estimates for sums of dependent random variables.

its most compelling aspect is its vertical variation, that is, the sum of the vertical distances between its adjacent terms. Denoted by varw, the vertical variation of the sequence in (1.1) is varw = 2 + 1 + 0 + 2 + 1 = 6. Our purpose here is to compute the mean and variance of var on four classical sets of combinatorial sequences. To formalize matters and place our problem in the context of ot...

I show that the structure of the firm is not neutral in respect to regulatory capital budgeted under rules which are based on the Value-at-Risk.

The aim of this paper is to discuss the use of the Generalized Hyperbolic Distributions to fit Brazilian assets returns. Selected subclasses are compared regarding goodness of fit statistics and distances. Empirical results show that these distributions fit data well. Then we show how to use these distributions in value at risk estimation and derivative price computation.

Introduction Conclusions References