A directional multivariate value at risk Articles uri icon

publication date

  • November 2015

start page

  • 111

end page

  • 123

volume

  • 65

international standard serial number (ISSN)

  • 0167-6687

electronic international standard serial number (EISSN)

  • 1873-5959

abstract

  • In economics, insurance and finance, value at risk (VaR) is a widely used measure of the risk of loss on a specific portfolio of financial assets. For a given portfolio, time horizon, and probability alpha, the 100 alpha% VaR is defined as a threshold loss value, such that the probability that the loss on the portfolio over the given time horizon exceeds this value is alpha. That is to say, it is a quantile of the distribution of the losses, which has both good analytic properties and easy interpretation as a risk measure. However, its extension to the multivariate framework is not unique because a unique definition of multivariate quantile does not exist. In the current literature, the multivariate quantiles are related to a specific partial order considered in R-n, or to a property of the univariate quantile that is desirable to be extended to R-n. In this work, we introduce a multivariate value at risk as a vector-valued directional risk measure, based on a directional multivariate quantile, which has recently been introduced in the literature. The directional approach allows the manager to consider external information or risk preferences in her/his analysis. We derive some properties of the risk measure and we compare the univariate VaR over the marginals with the components of the directional multivariate VaR. We also analyze the relationship between some families of copulas, for which it is possible to obtain closed forms of the multivariate VaR that we propose. Finally, comparisons with other alternative multivariate VaR given in the literature, are provided in terms of robustness. (C) 2015 Elsevier B.V. All rights reserved.

keywords

  • multivariate risks; value at risk; directional approach; multivariate quantile; copula; quantiles; depth; distributions