In this paper, we derive a portfolio optimization model by minimizing upper and lower bounds of loss probability. These bounds are obtained under a nonparametric assumption of underlying return distribution by modifying the so-called generalization error bounds for the support vector machine, which has been developed in the field of statistical learning. Based on the bounds, two fractional prog...

<p style='text-indent:20px;'>This paper studies a single-period inventory problem with quantity-oriented reference point, where the newsvendor has loss-averse preferences and conditional value-at-risk (CVaR) measure is introduced to hedge against his risk. It shown there exists unique optimal order quantity maximizing CVaR of utility. Moreover, it decreasing in loss aversion level, confid...

Abstract In this paper the concept of quantile-based optimal portfolio selection is introduced and a specific connected to it, conditional value-of-return (CVoR) portfolio, proposed. The CVoR defined as mean excess return or value-at-risk (CVaR) distribution. consists solely risk measures. Financial institutions that work in context Basel 4 use CVaR measure. regulatory framework sufficient nece...

We study stochastic optimization problems with chance and risk constraints, where in the latter, is quantified terms of conditional value-at-risk (CVaR). consider distributionally robust versions these problems, constraints are required to hold for a family distributions constructed from observed realizations uncertainty via Wasserstein distance. Our main results establish that if samples drawn...

We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints. begin by formulating in a Lagrangian framework. Under assumption that constraints can be represented transition mapping, we propose an optimization-based method to synthesize Markovian lower-bound constrained risk-averse problem. demonstrate formulated optimi...

In this paper, we propose a stochastic programming model for the well-known single-period newsvendor problem by adopting the conditional Value-at-Risk (CVaR) as the risk metric in the objective function. The demand uncertainty is modeled in terms of discrete scenarios that reflect the empirical distributions implied by market demand data. Our numerical results demonstrate that the higher order ...

A large conglomerate such as a property/casualty insurance firm in this case, can be divided along business boundaries. This division might be along commercial lines, homeowner lines and perhaps across countries. An insurance firm’s capital can be interpreted as a buffer that protects the company from insolvency and its inability to pay policyholder losses. Rare events have been simulated over ...

Die Portfoliooptimierung zur integrativen Bewertung von Bewirtschaftungsoptionen ist ein geeignetes Werkzeug, um Anpassungsstrategien an den Klimawandel in der landwirtschaftlichen Produktion zu entwickeln. Risiken für Landwirte können diverse Quellen haben. In unserer Analyse wurde das durch unsichere Wetterbedingungen entstehende Risiko untersucht und durch stochastische Klimaszenarien abgebi...

This paper will examine some commonly adopted approaches to the measurement of risk in finance and the various shortcomings implicit in the underpinnings of these approaches: early views on the nature of risk and uncertainty (Hume, Bernoulli, Knight, Keynes and Ramsey); the adoption of a mean variance decision choice criteria as a central foundation in financial economics and its accompanying l...

We present a new algorithm, Iterative Estimation Maximization (IEM), for stochastic linear and convex programs with Conditional-Value-at-Risk (CVaR) constraints. IEM iteratively constructs a sequence of compact-sized linear, or convex, optimization problems, and solves them sequentially to find the optimal solution. The problem size IEM solves in each iteration is unaffected by the size of rand...