We propose a new one-parameter family of risk functions defined on portfolio return sample -paths, which is called conditional drawdown-at-risk (CDaR). These risk functions depend on the portfolio drawdown (underwater) curve considered in active portfolio management. For some value of the tolerance parameter α , the CDaR is defined as the mean of the worst % 100 ) 1 ( ∗ − α drawdowns. The CDaR ...

A binary linear classification method, CGS method, was recently proposed by Gotoh and Takeda. The classification model was developed by introducing a risk measure known as the conditional value-at-risk (β-CVaR). CVaR minimization for the margin distribution leads to CGS problem, equivalent to ν-SVC of Schölkopf et al. in the convex case and Extended ν-SVC of Perez-Cruz et al. in the nonconvex c...

We study a linear-quadratic, optimal control problem on discrete, finite time horizon with distributional ambiguity, in which the cost is assessed via Conditional Value-at-Risk (CVaR). take steps toward deriving scalable dynamic programming approach to upper-bound value function for this problem. This program yields novel, tunable risk-averse policy, we compare existing state-of-the-art methods.

In today’s competitive business environment, the design and management of supply chainnetwork is one of the most important challenges that managers encounter. The supply chain network shouldbe designed for satisfying of customer demands as well as minizing the total system costs. This paper presentsa multi-period multi-stage supply chain network design problem under demand uncertainty. The prob...

This paper describes a hybrid stock trading system based on Genetic Network Programming (GNP) and Mean Conditional Value-at-Risk Model (GNP–CVaR). The proposed method, combining the advantages of evolutionary algorithms and statistical model, has provided useful tools to construct portfolios and generate effective stock trading strategies for investors with different risk-attitudes. Simulation ...

We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered SSP, which however oblivious to incurred risk. present an alternative view, instead optimizing conditional value-at-risk (CVaR), established risk measure. both chains as well MDP and introduce, through novel insights, two algorithms, based linear...

In this paper, we study risk-averse models for multicriteria optimization problems under uncertainty. We use a weighted sum-based scalarization and take a robust approach by considering a set of scalarization vectors to address the ambiguity and inconsistency in the relative weights of each criterion. We model the risk aversion of the decision makers via the concept of multivariate conditional ...

The purpose of this study is to develop portfolio optimization and assets allocation using our proposed models. The study is based on a non-parametric efficiency analysis tool, namely Data Envelopment Analysis (DEA). Conventional DEA models assume non-negative data for inputs and outputs. However, many of these data take the negative value, therefore we propose the MeanSharp-βRisk (MShβR) model...

We suggest a new methodology to overcome several well-known deeciencies of Value at Risk computations. Our approach mainly addresses two aspects of Value at Risk: rst, to avoid potentially disastrous clustering in predicted tail events we derive a new approach to accurately estimating the conditional distribution of asset returns using maximum entropy densities. Second, by the very nature of th...

We introduce a wholesale pricing strategy for an incumbent supplier facing with a competitive counterpart. We propose a profit function which considers both the present loss and future loss from a wholesale price and then study the optimal wholesale prices for different objectives about this profit function for the incumbent supplier. First, we achieve an optimal wholesale price for the incumbe...