Risk-Averse Portfolio Optimization via Stochastic Dominance Constraints

2014 ◽  
pp. 2281-2302 ◽  
Author(s):  
Darinka Dentcheva ◽  
Andrzej Ruszczynski
Keyword(s):  
Portfolio Optimization ◽  
Stochastic Dominance ◽  
Risk Averse

Portfolio Optimization using Rank Correlation

2014 ◽  
pp. 1866-1879
Author(s):  
Chanaka Edirisinghe ◽  
Wenjun Zhou
Keyword(s):  
Portfolio Optimization ◽  
Rank Correlation ◽  
Financial Assets ◽  
Risk Averse ◽  
Out Of Sample ◽  
Return Distributions ◽  
Sample Testing ◽  
Correlation Measures ◽  
Computational Properties ◽  
Critical Challenge

A critical challenge in managing quantitative funds is the computation of volatilities and correlations of the underlying financial assets. We present a study of Kendall's t coefficient, one of the best-known rank-based correlation measures, for computing the portfolio risk. Incorporating within risk-averse portfolio optimization, we show empirically that this correlation measure outperforms that of Pearson's in our out-of-sample testing with real-world financial data. This phenomenon is mainly due to the fat-tailed nature of stock return distributions. We also discuss computational properties of Kendall's t, and describe efficient procedures for incremental and one-time computation of Kendall's rank correlation.

Evaluating the Efficiency of Portfolio-Hedging Strategies by Incorporating Third Degree Stochastic Dominance Criteria and Data Envelopment Analysis

2021 ◽  
pp. 22-46
Author(s):  
Margareta Gardijan Kedžo
Keyword(s):  
Data Envelopment Analysis ◽  
Stochastic Dominance ◽  
Data Envelopment ◽  
Risk Averse ◽  
Super Efficiency ◽  
Risk Hedging ◽  
The Third ◽  
Positive Skewness ◽  
Hedging Strategies ◽  
Efficient Hedging

The chapter investigates chosen hedging strategies with options as useful risk hedging instruments. Assuming that average investor prefers greater return, is risk-averse, and prefers greater positive skewness, the performance of different hedged and unhedged portfolios is evaluated using stochastic dominance (SD) criteria and data envelopment analysis (DEA). The SD is examined up to the third degree (TSD) using Davidson-Duclos (DD) test. In the DEA, a super efficiency BCC model is used. It is investigated how these two methodologies can be combined and how the TSD criteria can be integrated into DEA in order to simplify the analysis of determining efficient hedging strategies with options.

scholarly journals Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Q. H. Zhai ◽  
T. Ye ◽  
M. X. Huang ◽  
S. L. Feng ◽  
H. Li
Keyword(s):  
Portfolio Optimization ◽  
Optimization Algorithm ◽  
Stochastic Dominance ◽  
Optimization Model ◽  
Second Order ◽  
Whale Optimization Algorithm ◽  
Order Stochastic Dominance ◽  
Whale Optimization ◽  
Portfolio Optimization Model ◽  
Second Order Stochastic Dominance

In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maximizing its returns. Furthermore, the whale optimization algorithm based on FTSE100 index data is used to optimize the above multiconstraint portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. Furthermore, extensive experiments validate the superiority of the whale optimization algorithm over the other four swarm intelligence optimization algorithms (gray wolf optimizer, fruit fly optimization algorithm, particle swarm optimization, and firefly algorithm) through various indicators of the results, especially under harsh constraints.

Nitrogen-Fixing Winter Cover Crops and Production Risk: A Case Study for No-Tillage Corn

1998 ◽  
Vol 30 (1) ◽  
pp. 163-174 ◽  
Author(s):  
James A. Larson ◽  
Roland K. Roberts ◽  
Donald D. Tyler ◽  
Bob N. Duck ◽  
Stephen P. Slinsky
Keyword(s):  
Cover Crops ◽  
Stochastic Dominance ◽  
Nitrogen Fertilization ◽  
Risk Averse ◽  
Production Risk ◽  
Risk Seeking ◽  
Wide Range ◽  
Seeking Behavior ◽  
Net Revenue ◽  
Applied Nitrogen

AbstractWinter legumes can substitute for applied nitrogen fertilization of corn. Stochastic dominance was used to order net revenues from legume and applied nitrogen alternatives. Stochastic dominance orderings indicate that systems combining vetch with low applied nitrogen fertilization (50 and 100 pounds/acre, respectively) were risk inefficient. By contrast, vetch and 150 pounds/acre applied nitrogen maximized expected net revenue and was risk efficient for a wide range of risk-averse and risk-seeking behavior. Farmers with these risk attitudes may not reduce applied nitrogen if they switch to a vetch cover. Extremely risk-averse or risk-seeking farmers would not prefer winter legumes.

Fractional Degree Stochastic Dominance

2020 ◽  
Vol 66 (10) ◽  
pp. 4630-4647 ◽  
Author(s):  
Rachel J. Huang ◽  
Larry Y. Tzeng ◽  
Lin Zhao
Keyword(s):  
Lower Bound ◽  
Risk Aversion ◽  
Stochastic Dominance ◽  
Absolute Risk ◽  
Comparative Statics ◽  
Risk Averse ◽  
Absolute Risk Aversion ◽  
Risk Apportionment ◽  
Lottery Choices ◽  
Increase In Risk

We develop a continuum of stochastic dominance rules for expected utility maximizers. The new rules encompass the traditional integer-degree stochastic dominance; between adjacent integer degrees, they formulate the consensus of individuals whose absolute risk aversion at the corresponding integer degree has a negative lower bound. By extending the concept of “uniform risk aversion” previously proposed in the literature to high-order risk preferences, we interpret the fractionalized degree parameter as a benchmark individual relative to whom all considered individuals are uniformly no less risk averse in the lottery choices. The equivalent distribution conditions for the new rules are provided, and the fractional degree “increase in risk” is defined. We generalize the previously defined notion of “risk apportionment” and demonstrate its usefulness in characterizing comparative statics of risk changes in fractional degrees. This paper was accepted by David Simchi-Levi, decision analysis.

STOCHASTIC DOMINANCE AND BEHAVIOR TOWARDS RISK: THE MARKET FOR ISHARES

2012 ◽  
Vol 07 (01) ◽  
pp. 1250005 ◽  
Author(s):  
DOMINIC GASBARRO ◽  
WING-KEUNG WONG ◽  
J. KENTON ZUMWALT
Keyword(s):  
Stochastic Dominance ◽  
Utility Functions ◽  
Investor Behavior ◽  
Risk Averse ◽  
Risk Seeking ◽  
Bull Markets ◽  
Seeking Behavior ◽  
Return Distributions ◽  
And Behavior ◽  
Positive Return

Prospect theory suggests that risk seeking can occur when investors face losses and thus an S-shaped utility function can be useful in explaining investor behavior. Using stochastic dominance procedures, Post and Levy (2015) find evidence of reverse S-shaped utility functions. This is consistent with investors exhibiting risk-seeking tendencies in bull markets and risk aversion in bear markets. We use both ascending and descending stochastic dominance procedures to test for risk-averse and risk-seeking behavior. By partitioning iShares' return distributions into negative and positive return regions, we find evidence of all four utility functions: concave, convex, S-shaped and reverse S-shaped.

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