This paper defines a risk-stability index (RSI) that takes into account the extreme dependence structure and the conditional probability of joint failure (CPJF) among risk factors in a portfolio. In combination, both the RSI and CPJF provide a valuable tool for analyzing risk from complementary...

Factor models have large potential in the modeling of several natural and human phenomena. In this paper we consider a multivariate time series Yn, n≥1, rescaled through random factors Tn, n≥1, extending some scale mixture models in the literature. We analyze its extremal behavior by...

When correlations between assets turn positive, multi-asset portfolios can become riskier than single assets. This article presents the estimation of tail risk at very high quantiles using a semiparametric estimator which is particularly suitable for portfolios with a large number of assets. The...

The well-known M4 processes of Smith and Weissman are very flexible models for asymptotically dependent multivariate data. Extended M4 of Heffernan et al. allows to also account for asymptotic independence. In this paper we introduce a more general multivariate model comprising asymptotic...

In this work, we use a time varying copula model to investigate the impact of the global financial crisis on dependence between US and each of six major stock markets and on risk management strategies. The model is implemented with an AR-GARCH-t for the marginal distribution and the extreme...

We define a new multivariate time series model by generalizing the ARMAX process in a multivariate way. We give conditions on stationarity and analyze local dependence and domains of attraction. As a consequence of the obtained results, we derive new multivariate extreme value distributions. We...