Confidence regions for the mean-variance efficient set: An alternative approach to estimation risk

1991 ◽  
Vol 1 (3) ◽  
pp. 235-257 ◽  
Author(s):  
J. D. Jobson
Keyword(s):  
Confidence Regions ◽  
Efficient Set ◽  
Estimation Risk ◽  
Alternative Approach ◽  
The Mean ◽  
Mean Variance

scholarly journals Stochastic dominance theory for location-scale family

2006 ◽  
Vol 2006 ◽  
pp. 1-10 ◽  
Author(s):  
Wing-Keung Wong
Keyword(s):  
Stochastic Dominance ◽  
Indifference Curve ◽  
Efficient Set ◽  
Alternative Proof ◽  
Efficient Sets ◽  
Scale Parameters ◽  
General Utility ◽  
The Mean ◽  
Mean Variance ◽  
Variance Criterion

Meyer (1987) extended the theory of mean-variance criterion to include the comparison among distributions that differ only by location and scale parameters and to include general utility functions with only convexity or concavity restrictions. In this paper, we make some comments on Meyer's paper and extend the results from Tobin (1958) that the indifference curve is convex upwards for risk averters, concave downwards for risk lovers, and horizontal for risk neutral investors to include the general conditions stated by Meyer (1987). We also provide an alternative proof for the theorem. Levy (1989) extended Meyer's results by introducing some inequality relationships between the stochastic-dominance and the mean-variance efficient sets. In this paper, we comment on Levy's findings and show that these relationships do not hold in certain situations. We further develop some properties among the first- and second-degree stochastic dominance efficient sets and the mean-variance efficient set.

scholarly journals SCALED AND STABLE MEAN-VARIANCE-EVAR PORTFOLIO SELECTION STRATEGY WITH PROPORTIONAL TRANSACTION COSTS

2017 ◽  
Vol 18 (4) ◽  
pp. 561-584 ◽  
Author(s):  
Ebenezer Fiifi Emire ATTA MILLS ◽  
Bo YU ◽  
Jie YU
Keyword(s):  
Transaction Costs ◽  
Portfolio Selection ◽  
Downside Risk ◽  
Estimation Risk ◽  
Expected Return ◽  
Proportional Transaction Costs ◽  
Portfolio Returns ◽  
L2 Norm ◽  
The Mean ◽  
Mean Variance

This paper studies a portfolio optimization problem with variance and Entropic Value-at-Risk (evar) as risk measures. As the variance measures the deviation around the expected return, the introduction of evar in the mean-variance framework helps to control the downside risk of portfolio returns. This study utilized the squared l2-norm to alleviate estimation risk problems arising from the mean estimate of random returns. To adequately represent the variance-evar risk measure of the resulting portfolio, this study pursues rescaling by the capital accessible after payment of transaction costs. The results of this paper extend the classical Markowitz model to the case of proportional transaction costs and enhance the efficiency of portfolio selection by alleviating estimation risk and controlling the downside risk of portfolio returns. The model seeks to meet the requirements of regulators and fund managers as it represents a balance between short tails and variance. The practical implications of the findings of this study are that the model when applied, will increase the amount of capital for investment, lower transaction cost and minimize risk associated with the deviation around the expected return at the expense of a small additional risk in short tails.

scholarly journals MEAN-DRAWDOWN RISK BEHAVIOR: DRAWDOWN RISK AND CAPITAL ASSET PRICING

2013 ◽  
Vol 14 (Supplement_1) ◽  
pp. S447-S469 ◽  
Author(s):  
Mohammad Reza Tavakoli Baghdadabad ◽  
Fauzias Mat Nor ◽  
Izani Ibrahim
Keyword(s):  
Mutual Funds ◽  
Risk Behavior ◽  
Risk Measures ◽  
Capital Asset Pricing ◽  
Capital Asset ◽  
Pricing Models ◽  
Alternative Approach ◽  
The Mean ◽  
Maximum Drawdown ◽  
Mean Variance

We develop an alternative approach based on mean-drawdown risk behavior versus the mean-variance behavior. We develop two risk measures as the maximum draw down risk and average drawdown risk to estimate two new betas and then propose two CAPM-like models. The data includes a comprehensive universe of more than 11,000 US equity-based mutual funds from first month of 2000 to third month of 2011. The evidence clearly shows superiority of the maximum and average drawdown betas and their pricing models, the maximum drawdown CAPM and the average drawdown CAPM, over the traditional beta and CAPM, respectively.

scholarly journals Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hanji He ◽  
Guangming Deng
Keyword(s):  
Empirical Likelihood ◽  
Missing At Random ◽  
Confidence Regions ◽  
Likelihood Inference ◽  
High Dimensional ◽  
Finite Sample ◽  
The Mean ◽  
Data Missing ◽  
Consistency And Asymptotic Normality ◽  
The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

Sign in / Sign up

Export Citation Format

Share Document