New Development on the Third Order Stochastic Dominance for Risk-Averse and Risk-Seeking Investors with Application in Risk Management

2018 ◽  
Author(s):  
Raymond Honfu Chan ◽  
Ephraim Clark ◽  
Wing-Keung Wong
Keyword(s):  
Risk Management ◽  
Stochastic Dominance ◽  
Risk Averse ◽  
Third Order ◽  
The Third ◽  
Risk Seeking ◽  
Order Stochastic Dominance ◽  
New Development

scholarly journals STOCHASTIC DOMINANCE: CONVEXITY AND SOME EFFICIENCY TESTS

2012 ◽  
Vol 15 (05) ◽  
pp. 1250036 ◽  
Author(s):  
ANDREY LIZYAYEV
Keyword(s):  
Linear Programming ◽  
Relative Risk ◽  
Risk Aversion ◽  
Stochastic Dominance ◽  
Convex Sets ◽  
Relative Risk Aversion ◽  
Third Order ◽  
Efficient Sets ◽  
The Third ◽  
Order Stochastic Dominance

This paper points out the importance of Stochastic Dominance (SD) efficient sets being convex. We review classic convexity and efficient set characterization results on SD efficiency of a given portfolio relative to a diversified set of assets and generalize them in the following aspects. First, we propose a linear programming SSD test that is more efficient than that of Post (2003). Secondly, we expand the SSD efficiency criteria developed by Dybvig and Ross (1982) onto the Third Order Stochastic Dominance and further to Decreasing Absolute and Increasing Relative Risk Aversion Stochastic Dominance. The efficient sets for those are finite unions of convex sets.

scholarly journals The concept of stochastic dominance in ranking investment alternatives

2005 ◽  
Vol 50 (164) ◽  
pp. 135-149
Author(s):  
Dejan Trifunovic
Keyword(s):  
Stochastic Dominance ◽  
Distribution Functions ◽  
Third Order ◽  
Ranking Problem ◽  
First Order ◽  
Order Stochastic Dominance ◽  
Arbitrage Opportunities ◽  
Second Order Stochastic Dominance ◽  
Mean Variance ◽  
Almost Stochastic Dominance

In order to rank investments under uncertainty, the most widely used method is mean variance analysis. Stochastic dominance is an alternative concept which ranks investments by using the whole distribution function. There exist three models: first-order stochastic dominance is used when the distribution functions do not intersect, second-order stochastic dominance is applied to situations where the distribution functions intersect only once, while third-order stochastic dominance solves the ranking problem in the case of double intersection. Almost stochastic dominance is a special model. Finally we show that the existence of arbitrage opportunities implies the existence of stochastic dominance, while the reverse does not hold.

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.

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.

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.

The Effect of Ambiguity Aversion on Precautionary Effort

2018 ◽  
Vol 26 (3) ◽  
pp. 371-389
Author(s):  
Jimin Hong
Keyword(s):  
Stochastic Dominance ◽  
Ambiguity Aversion ◽  
Sufficient Conditions ◽  
Background Risk ◽  
Risk Averse ◽  
Income Risk ◽  
Order Stochastic Dominance ◽  
Second Order Stochastic Dominance ◽  
Period Model

This study analyzes the effect of ambiguity aversion on precautionary effort under a two period model when background risk like income risk is added to loss. Precautionary effort only affects the probability of loss occurrence. The sufficient conditions under which a risk averse and ambiguity averse individual makes more effort than a risk averse and ambiguity neutral one are as follows. First, the distribution of background risk changes in type of first order stochastic dominance. Second, the distribution of background risk changes in type of second order stochastic dominance and the utility function shows prudence. In both cases, AAA (absolute ambiguity aversion) should not increase. That is, AAA denotes DAAA (Decreasing Absolute Ambiguity Aversion) or CAAA (Constant Absolute Ambiguity Aversion). The effect of AAA is not observed in the existing literatures which assume a one-period model. In a one period model, the effect of AAA on precautionary effort of a long term may have ignored. Lastly, precautionary effort increases if and only if AAA is not increasing in cases when the background risk follows binary distribution or an individual is risk neutral and ambiguity averse.

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