Dual Stochastic Dominance and Related Mean-Risk Models

By Wlodzimierz Ogryczak, Andrzej Ruszczynski. SIAM Journal on Optimization, 2002

545 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Soil Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3bf3fdf91ca737e88e9ce30e09edf83ceb39e7a1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

In Soil Science

In Statistics and Probability

Citations in the corpus

Lectures on Stochastic Programming: Modeling and Theory (2009, background): “We thank Stevens Institute of Technology and Rutgers University for granting sabbatical leaves to Darinka Dentcheva and Andrzej Ruszczyński, during which a large portion of this work was written.”
Worst-Case Conditional Value-at-Risk with Application to Robust Portfolio Management (2009, unknown): “x ∈ X , (24) max π∈Pπ α + 1 1− β T u ≤ θ, (25) uk ≥ f(x,y[k])− α, k = 1, · · · , S, (26) uk ≥ 0, k = 1, · · · , S.”
Stochastic Orders and Risk Measures: Consistency and Bounds (2006, unknown): “Thus, if we take λ > 0, then λX and λY are comonotone and ρ ( λX ) + ρ ( λY ) − ρ ( λX + λY ) = λε which tends to ∞ if λ tends to ∞ .”
Optimal reinsurance under risk and uncertainty (2015, unknown): “Furthermore, the CV aR is becoming more and more popular among researchers and practitioners due to its interesting properties (Ogryczak and Ruszczynski, 2002).”
Mean-risk models using two risk measures: a multi-objective approach (2007, background): “An important result, proved by Rockafellar and Uryasev (2000, 2002), and independently by Ogryczak and Ruszczynski (2002), is that the CVaR of a random variable Rx can be calculated by solving a convex optimization problem.”
An enhanced model for portfolio choice with SSD criteria: a constructive approach (2011, background): “The importance of SSD as a choice criterion in portfolio selection, as well as the difficulty in applying it in practice, have been widely recognized (Hadar and Russell 1969, Whitmore and Findlay 1978, Kroll and Levy 1980, Ogryczak and Ruszczyński 2001, 2002).”
Optimal reinsurance with general risk measures (2009, unknown): “…2002) nd the conditional value at risk is becoming more and more mportant in Finance and Insurance because it also respects the tochastic dominance (Ogryczak and Ruszczynski, 2002), provides nformation about the degree of risk in monetary terms (capital equirements, reserves etc.), shows suitable…”
Optimization of Convex Risk Functions (2006, methodology): “Most notable are here the works on the mean-variance model by Markowitz [13] [14], but many efforts have been made to use other deviation measures, like semideviation and deviations from quantiles (Ogryczak and Ruszczy´nski [16] [17] [18]).”
Conditional value at risk and related linear programming models for portfolio optimization (2007, background): “The tail GMD (Ogryczak and Ruszczy´nski (2002a, 2002b)) is defined for any β ∈ (0, 1] by averaging the vertical diameters h p (x) within the tail interval p ≤ β as: β (x) = 2 β 2 β 0 (μ(x)α − F (−2) x (α))dα.”
Stochastic dominance analysis of Asian hedge funds (2008, background): “However, Ogryczak and Ruszczynski (2002), Leitner (2005) and Ma and Wong (2006) noted that stochastic dominance (SD) approach is superior as whatever information obtained by applying VaR and CVaR could be obtained by the SD approach.”
Higher moment coherent risk measures (2007, background): “Replacing the variance by the lower standard semi-deviation as a measure of investment risk so as to take into account only ‘negative’ deviations from the expected level has been proposed as early as by Markowitz (1959); see also more recent works by Ogryczak and Ruszczyński (1999, 2001, 2002).”
Risk-averse two-stage stochastic programming with an application to disaster management (2012, methodology): “We also refer to Ruszczyński [32] and Kall and Mayer [33] for alternative decomposition methods in two-stage stochastic programming.”