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...

Starting with a time-0 coherent risk measure defined for “value processes”, we also define risk measurement processes. Two other constructions of measurement processes are given in terms of sets of test probabilities. These latter constructions are identical and are related to the former construction when the sets fulfill a stability condition also met in multiperiod treatment of ambiguity as i...

Evaluating risk measures, premiums, and capital allocation based on dependent multi-losses is a notoriously difficult task. In this paper, we demonstrate how this can be successfully accomplished when losses follow the multivariate Pareto distribution of the second kind, which is an attractive model for multi-losses whose dependence and tail heaviness are influenced by a heavy-tailed background...

prostate cancer is a major cause of morbidity and mortality in iran, yet there are few studies examining risk factors specific to the iranian context. we conducted a case-control study to explore risk factors for prostate cancer in mazandaran, iran from 2005 to 2008. the cases were 137 men with clinicopathologically confirmed prostate cancer. controls were 137 neighborhood and age match men wit...

This article considers the problem of risk-optimal allocation security measures when actuators an uncertain control system are under attack. We consider adversary injecting false data into actuator channels. The attack impact is characterized by maximum performance loss caused a stealthy with bounded energy. Since random variable, due to uncertainty, we use Conditional Value-at-Risk (CVaR) char...

 Ambiguity in the inputs of the models is typical especially in portfolio selection problem where the true distribution of random variables is usually unknown. Here we use robust optimization approach to address the ambiguity in conditional-value-at-risk minimization model. We obtain explicit models of the robust conditional-value-at-risk minimization for polyhedral and correlated polyhedral am...

This paper examines a new approach for credit risk optimization. The model is based on the Conditional Value-at-Risk (CVaR) risk measure, the expected loss exceeding Value-at-Risk. CVaR is also known as Mean Excess, Mean Shortfall, or Tail VaR. This model can simultaneously adjust all positions in a portfolio of financial instruments in order to minimize CVaR subject to trading and return const...

This paper introduces a framework for robustifying convex, law invariant risk measures, to deal with ambiguity of the distribution of random asset losses in portfolio selection problems. The robustified risk measures are defined as the worst-case portfolio risk over the ambiguity set of loss distributions, where an ambiguity set is defined as a neighborhood around a reference probability measur...

Tail conditional expectations refer to the expected values of random variables conditioning on some tail events and are closely related to various coherent risk measures. In the univariate case, the tail conditional expectation is asymptotically proportional to the value-at-risk, a popular risk measure. The focus of this paper is on asymptotic relations between the multivariate tail conditional...

W study optimization problems with value-at-risk (VaR) constraints. Because it lacks subadditivity, VaR is not a coherent risk measure and does not necessarily preserve the convexity. Thus, the problems we consider are typically not provably convex. As such, the conditional value-at-risk (CVaR) approximation is often used to handle such problems. Even though the CVaR approximation is known as t...