We analyzed the volatility dynamics of three developed markets (U.K., U.S. and Japan), during the period 2003-2011, by comparing the performance of several multivariate volatility models, namely Constant Conditional Correlation (CCC), Dynamic Conditional Correlation (DCC) and consistent DCC (cDCC) models. To evaluate the performance of models we used four statistical loss functions on the daily...

in this paper, we investigate the performance of parametric arch class models to forecast out-of-sample var for two portfolios of tehran stock exchange (tse) companies (market portfolio and a portfolio of 50 liquid companies), using a number of distributional assumptions and sample sizes at low and high confidence levels. we find, first, that leptokurtic distributions are able to produce better...

this paper studies the effect of considering time varying skewness and kurtosis on the estimation of value at risk (var) for both long and short positions using the hyaparch model and daily data for tehran stock exchange price index (tepix). our results show that applying conditional distributions with time varying or constant skewness and degrees of freedom is able to capture the asymmetry app...

In this paper we present a semi-automated search procedure to deal with the problem of the identification of the contemporaneous causal structure connected to a large class of multivariate time series models. We refer in particular to multivariate models, such as vector autoregressive (VAR) and dynamic factor (DF) model, in which the background or theoretical knowledge is not sufficient or enou...

Four dimensional variational data assimilation, called 4D-Var in the atmospheric sciences literature, is a method for combining forecast, dynamical systems equations, prior information about properties of the atmosphere, and heterogeneous observations, to get an estimate of the evolving state of the atmosphere. Summary: We (abstractly) generalize thètoy' weak 4D-Var model in Gong, Wahba, Johnso...

A fundamental yet poorly understood aspect of gene regulation in eukaryotic organisms is the mechanisms that control allelic exclusion and mutually exclusive gene expression. In the malaria parasite Plasmodium falciparum, this process regulates expression of the var gene family--a large, hypervariable repertoire of genes that are responsible for the ability of the parasite to evade the host imm...

Is it possible to obtain an objective and quantifiable measure of risk backed up by choices made by some specific groups of rational investors? To answer this question, in this paper we establish some behavior foundations for various types of VaR models, including VaR and conditional-VaR, as measures of downside risk. Though supported to some extent with unanimous choices by some specific group...

The purpose of this paper is to analyze in bivariate vector autoregression the relationship between feedback in stochastic systems, Granger causality and a measure of dissimilarity between ARMA models. In particular, we consider a bivariate vector autoregressive processes of order p (a bivariate VAR(p) process) and we prove if the distance between the univariate ARMA models implied by the VAR r...

Portfolio optimization problems involving Value-at-Risk (VaR) are often computationally intractable and require complete information about the return distribution of the portfolio constituents, which is rarely available in practice. These difficulties are compounded when the portfolio contains derivatives. We develop two tractable conservative approximations for the VaR of a derivative portfoli...

In this paper we discuss identification of codependent VAR and VEC models. Codependence of order q is given if a linear combination of autocorrelated variables eliminates the serial correlation after q lags. Importantly, maximum likelihood estimation and corresponding likelihood ratio testing are only possible if the codependence restrictions can be uniquely imposed. However, our study reveals ...