design of a logistics network in proper way provides a proper platform for efficient and effective supply chain management. this paper studies a multi-period, multi echelon and multi-product integrated forward-reverse logistics network under uncertainty. first, an efficient complex mixed-integer linear programming (milp) model by considering some real-world assumptions is developed for the inte...

This paper presents an optimal portfolio selection approach based on value at risk (VaR), conditional value at risk (CVaR), worst-case value at risk (WVaR) and partitioned value at risk (PVaR) measures as well as calculating these risk measures. Mathematical solution methods for solving these optimization problems are inadequate and very complex for a portfolio with high number of assets. For t...

This paper develops a bi-objective portfolio selection problem that maximizes returns and minimizes a risk measure called conditional Drawdown (CDD). The drawdown measures include the maximal Drawdown and Average Drawdown as its limiting case. The CDD family of risk functional is similar to conditional value at Risk (CVaR). In this paper, the fuzzy method has been used to solve the bi-objec...

VALUE-AT-RISK IN RANKING CROP INSURANCE ALTERNATIVAES Abstract This study examines the use of conditional value-at-risk (CVaR) as a measure for evaluating risky alternatives. CVaR has been shown to have a number of advantages compared to the more traditionally applied value-at-risk in decision settings that involve choices among truncated revenue distributions. A conceptual presentation illustr...

investors use different approaches to select optimal portfolio. so, optimal investment choices according to return can be interpreted in different models. the traditional approach to allocate portfolio selection called a mean - variance explains. another approach is markov chain. markov chain is a random process without memory. this means that the conditional probability distribution of the nex...

Conditional Value at Risk (CVaR) is a prominent risk measure that is being used extensively in various domains. We develop a new formula for the gradient of the CVaR in the form of a conditional expectation. Based on this formula, we propose a novel sampling-based estimator for the gradient of the CVaR, in the spirit of the likelihood-ratio method. We analyze the bias of the estimator, and prov...

We consider the sensitivity of conditional value-at-risk (CVaR) with respect to the tail index assuming regularly varying tails and exponential and faster-than-exponential tail decay for the return distribution. We compare it to the CVaR sensitivity with respect to the scale parameter for stable Paretian, the Student’s t, and generalized Gaussian laws and discuss implications for the modeling o...

We evaluate conditional value-at-risk (CVaR) as a risk measure in data-driven portfolio optimization. We show that portfolios obtained by solving mean-CVaR and global minimum CVaR problems are unreliable due to estimation errors of CVaR and/or the mean, which are aggravated by optimization. This problem is exacerbated when the tail of the return distribution is made heavier. We conclude that CV...

Financial returns exhibit stylized facts such as leptokurtosis, skewness and heavy-tailness. Regarding this behavior, in this paper, we apply multivariate generalized hyperbolic (mGH) distribution for portfolio modeling and performance evaluation, using conditional value at risk (CVaR) as a risk measure and allocating best weights for portfolio selection. Moreover, a robust portfolio optimizati...

Conditional Value at Risk (CVaR) is a prominent risk measure that is being used extensively in various domains such as finance. In this work we present a new formula for the gradient of the CVaR in the form of a conditional expectation. Our result is similar to policy gradients in the reinforcement learning literature. Based on this formula, we propose novel sampling-based estimators for the CV...