Almost Stochastic Dominance for Risk-Averse and Risk-Seeking Investors

In this paper we first develop a theory of almost stochastic dominance for risk-seeking investors to the first three orders. Thereafter, we study the relationship between the preferences of almost stochastic dominance for risk-seekers with that for risk averters

Almost Stochastic Dominance for Risk-Averse and Risk-Seeking Investors

In this paper we first develop a theory of almost stochastic dominance for risk-seeking investors to the first three orders. Thereafter, we study the relationship between the preferences of almost stochastic dominance for risk-seekers with that for risk averters

A Note on Almost Stochastic Dominance

To satisfy the property of expected-utility maximization, Tzeng et al. (2012) modify the almost second-degree stochastic dominance proposed by Leshno and Levy (2002) and define almost higher-degree stochastic dominance. In this note, we further investigate the relevant properties. We define an almost third-degree stochastic dominance in the same way that Leshno and Levy (2002) define second-degree stochastic dominance and show that Leshno and Levy's (2002) almost stochastic dominance has the hierarchy property but not expected-utility maximization. In contrast, Tzeng et al.'s (2012) definition has the property of expected-utility maximization but not the hierarchy property. This phenomenon also holds for higher-degree stochastic dominance for these two concepts. Thus, the findings in this paper suggest that Leshno and Levy's (2002) definitions of ASSD and ATSD might be better than those defined by Tzeng et al. (2012) if the hierarchy property is considered to be an important issue

Almost Stochastic Dominance and Moments

This paper establishes some equivalent relationships for the first three orders of the almost stochastic dominance (ASD). Using these results, we first prove formally that the ASD definition modified by Tzeng et al. (2012) does not possess any hierarchy property. Thereafter, we conclude that when the first three orders of ASD are used in the prospects comparison, investors prefer the one with positive gain, smaller variance and positive skewness. This information, in turn, enables decision makers to determine the ASD relationship among prospects when they know the moments of the prospects

Almost Stochastic Dominance and Moments

This paper first extends the theory of almost stochastic dominance (ASD) to the first four orders. We then establish some equivalent relationships for the first four orders of the ASD. Using these results, we prove formally that the ASD definition modified by Tzeng et al.\ (2012) does not possess any hierarchy property. Thereafter, we conclude that when the first four orders of ASD are used in the prospects comparison, risk-averse investors prefer the one with positive gain, smaller variance, positive skewness, and smaller kurtosis. This information, in turn, enables decision makers to determine the ASD relationship among prospects when they know the moments of the prospects. At last, we discuss the necessary and sufficient conditions for different orders the ASD and the moments of the prospects

A Note on Almost Stochastic Dominance

To satisfy the property of expected-utility maximization, Tzeng et al. (2012) modify the almost second-degree stochastic dominance proposed by Leshno and Levy (2002) and define almost higher-degree stochastic dominance. In this note, we further investigate the relevant properties. We define an almost third-degree stochastic dominance in the same way that Leshno and Levy (2002) define second-degree stochastic dominance and show that Leshno and Levy's (2002) almost stochastic dominance has the hierarchy property but not expected-utility maximization. In contrast, Tzeng et al.'s (2012) definition has the property of expected-utility maximization but not the hierarchy property. This phenomenon also holds for higher-degree stochastic dominance for these two concepts. Thus, the findings in this paper suggest that Leshno and Levy's (2002) definitions of ASSD and ATSD might be better than those defined by Tzeng et al. (2012) if the hierarchy property is considered to be an important issue

Almost Stochastic Dominance and Moments

This paper first extends the theory of almost stochastic dominance (ASD) to the first four orders. We then establish some equivalent relationships for the first four orders of the ASD. Using these results, we prove formally that the ASD definition modified by Tzeng et al.\ (2012) does not possess any hierarchy property. Thereafter, we conclude that when the first four orders of ASD are used in the prospects comparison, risk-averse investors prefer the one with positive gain, smaller variance, positive skewness, and smaller kurtosis. This information, in turn, enables decision makers to determine the ASD relationship among prospects when they know the moments of the prospects. At last, we discuss the necessary and sufficient conditions for different orders the ASD and the moments of the prospects

Make Almost Stochastic Dominance really Almost

Leshno and Levy (2002) extend stochastic dominance (SD) theory to almost stochastic dominance (ASD) for {\it most} decision makers. When comparing any two prospects, Guo, et al.\ (2013) find that there will be ASD relationship even there is only very little difference in mean, variance, skewness, or kurtosis. Investors may prefer to conclude ASD only if the dominance is nearly almost. Levy, et al. (2010) have provided two approaches to solve the problem. In this paper, we extend their work by first recommending an existing stochastic dominance test to handle the issue and thereafter developing a new test for the ASD which could detect dominance for any pre-determined small value. We also provide two approaches to obtain the critical values for our proposed test

Make Almost Stochastic Dominance really Almost

Leshno and Levy (2002) extend stochastic dominance (SD) theory to almost stochastic dominance (ASD) for {\it most} decision makers. When comparing any two prospects, Guo, et al.\ (2013) find that there will be ASD relationship even there is only very little difference in mean, variance, skewness, or kurtosis. Investors may prefer to conclude ASD only if the dominance is nearly almost. Levy, et al. (2010) have provided two approaches to solve the problem. In this paper, we extend their work by first recommending an existing stochastic dominance test to handle the issue and thereafter developing a new test for the ASD which could detect dominance for any pre-determined small value. We also provide two approaches to obtain the critical values for our proposed test