This paper proposes several parametric models to compute the portfolio VaR and CVaR in a given temporal horizon and for a given level of confidence. Firstly, we describe extension of the EWMA RiskMetrics model considering conditional elliptically distributed returns. Secondly, we examine several new models based on different stable Paretian distributional hypotheses of return portfolios. Finall...

Multistage portfolio optimization models are difficult to solve when market risk is measured by Value-at-Risk (VaR), this paper proposes a soft method for solving VaR-based portfolio optimization models based on a soft optimization approach. In order to demonstrate the validity of the proposed soft method, we perform portfolio management experiments with real data from the New York stock market...

This paper measured the value at risk (VaR) and expected shortfall (ES) of the US Treasury yield changes. The US Treasury yield data were tested and found to be not normally distributed. Consequently, the mixture normal model (MNM) was used to improve the delta normal VaR and ES measures. It performed extraordinarily well in all cases, based on bootstrapping and mean square error tests. In addi...

In this paper we put forward a new method to estimate value at risk (VaR), autoregressive conditional heteroskedastic (ARCH) factor, which combines multivariate analysis with ARCH models. Firstly, from a set of correlated portfolio risk factors, we derive a smaller uncorrelated risk factors set, by applying multivariate analysis. Secondly, we use ARCH schemes to model uncorrelated factors histo...

We review, under a historical perspective, the developement of the problem of nonfundamentalness of Moving Average (MA) representations of economic models, starting from the work by Hansen and Sargent [1980]. Nonfundamentalness typically arises when agents’ information space is larger than the econometrican’s one. Therefore it is impossible for the latter to use standard econometric techniques,...

This paper deals with model selection and forecasting in vector autoregressions (VARs) in situations where the set of available predictors is inconveniently large to accommodate with methods and diagnostics used in traditional small-scale models. Available information over this large dataset can be summarized into a considerably smaller set of variables through factors estimated by the dynamic ...

The value at risk (VaR) measure often relies on an assumption about the return (or price) distribution of the underlying risky assets. Different distributional assumptions may produce widely different computed VaR values. When estimating VaR using intra-daily equity returns, the question arises as to what assumption should be made about the return distribution. Because of the difficulty of deco...

We present a new method for forecasting systems of multiple interrelated time series. The method learns the forecast models together with discovering leading indicators from within the system that serve as good predictors improving the forecast accuracy and a cluster structure of the predictive tasks around these. The method is based on the classical linear vector autoregressive model (VAR) and...

Many economic applications call for simultaneous equations VAR modeling. We show that the existing importance sampler can be prohibitively inefficient for this type of models. We develop a Gibbs simulator that works for both simultaneous and recursive VAR models with a much broader range of linear restrictions than those in the existing literature. We show that the required computation is of an...