Constructing Mean Variance Efficient Frontiers Using Foreign Large Blend Mutual Funds

2016 ◽  
pp. 315-329
Author(s):  
Ganlin Xu ◽  
Harry Markowitz ◽  
Minyee Wang ◽  
John B. Guerard
Keyword(s):  
Mutual Funds ◽  
Efficient Frontiers ◽  
Mean Variance

Arithmetic and Continuous Return Mean-Variance Efficient Frontiers

2009 ◽  
Author(s):  
Robert Ferguson ◽  
Dean Leistikow ◽  
Susana Yu
Keyword(s):  
Efficient Frontiers ◽  
Mean Variance

Average Drawdown Risk and Capital Asset Pricing

2013 ◽  
Vol 16 (04) ◽  
pp. 1350028 ◽  
Author(s):  
Mohammad Reza Tavakoli Baghdadabad ◽  
Paskalis Glabadanidis
Keyword(s):  
Asset Pricing ◽  
Mutual Funds ◽  
Risk Measure ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Acceptable Risk ◽  
Asset Pricing Model ◽  
The Past ◽  
Mean Variance

Practitioners and academics have spent the past few decades debating the validity and relevance of the capital asset pricing model (CAPM). One of the attributes of the model is an estimate of risk by beta, which in equilibrium describe the behavior of mean-variance (MV) investors. In the MV framework, risk is measured by the variance of returns which is a questionable and restrictive risk measure. In contrast, the average drawdown risk is a more acceptable risk measure and can be applied to modeling an alternative behavioral hypothesis, namely mean-drawdown behavior with a replacement risk measure for diversified investors, the average drawdown beta leading to an alternative pricing model based on this beta. Our findings clearly support the average drawdown beta and the pricing model of average drawdown CAPM versus the conventional beta and CAPM in a sample of Malaysian mutual funds.

Dotted Representations of Mean-Variance Efficient Frontiers and their Computation

2009 ◽  
Vol 47 (1) ◽  
pp. 15-21 ◽  
Author(s):  
Yue Qi ◽  
Markus Hirschberger ◽  
Ralph E. Steuer
Keyword(s):  
Efficient Frontiers ◽  
Mean Variance

Arithmetic and Continuous Return Mean-Variance Efficient Frontiers

2009 ◽  
Vol 18 (3) ◽  
pp. 62-69 ◽  
Author(s):  
Robert A Ferguson ◽  
Dean Leistikow ◽  
Susana Yu
Keyword(s):  
Efficient Frontiers ◽  
Mean Variance

scholarly journals Application of Performance Ratios in Portfolio Optimization

2015 ◽  
Vol 63 (6) ◽  
pp. 1969-1977 ◽  
Author(s):  
Aleš Kresta
Keyword(s):  
Probability Distribution ◽  
Portfolio Optimization ◽  
Mutual Funds ◽  
Portfolio Theory ◽  
Sharpe Ratio ◽  
Higher Moments ◽  
Computational Power ◽  
Return Probability ◽  
Contemporary Development ◽  
Mean Variance

The cornerstone of modern portfolio theory was established by pioneer work of Harry Markowitz. Based on his mean-variance framework, Sharpe formulated his well-known Sharpe ratio aiming to measure the performance of mutual funds. The contemporary development in computer’s computational power allowed to apply more complex performance ratios, which take into account also higher moments of return probability distribution. Although these ratios were proposed to help the investors to improve the results of portfolio optimization, we empirically demonstrated in our paper that this may not necessarily be true. On the historical dataset of DJIA components we empirically showed that both Sharpe ratio and MAD ratio outperformed Rachev ratio. However, for Rachev ratio we assumed only one level of parameters value. Different set-ups of parameters may provide different results and thus further analysis is certainly required.

The Robustness of Portfolio Optimization Models

2018 ◽  
pp. 210-229
Author(s):  
Antonis Pavlou ◽  
Michalis Doumpos ◽  
Constantin Zopounidis
Keyword(s):  
Value At Risk ◽  
Conditional Value At Risk ◽  
Optimization Models ◽  
Absolute Deviation ◽  
Efficient Frontiers ◽  
Out Of Sample ◽  
Efficient Portfolios ◽  
Using Data ◽  
Investment Portfolios ◽  
Mean Variance

The optimization of investment portfolios is a topic of major importance in financial decision making, and many relevant models can be found in the literature. These models extend the traditional mean-variance framework using a variety of other risk-return measures. Existing comparative studies have adopted a rather restrictive approach, focusing solely on the minimum risk portfolio without considering the whole set of efficient portfolios, which are also relevant for investors. This chapter focuses on the performance of the whole efficient set. To this end, the authors examine the out-of-sample robustness of efficient portfolios derived by popular optimization models, namely the traditional mean-variance model, mean-absolute deviation, conditional value at risk, and a multi-objective model. Tests are conducted using data for S&P 500 stocks over the period 2005-2016. The results are analyzed through novel performance indicators representing the deviations between historical (estimated) efficient frontiers, actual out-of-sample efficient frontiers, and realized out-of-sample portfolio results.

Limits to mutual funds' ability to rely on mean/variance optimization

2016 ◽  
Vol 37 ◽  
pp. 282-292
Author(s):  
Iordanis Karagiannidis ◽  
Nadia Vozlyublennaia
Keyword(s):  
Mutual Funds ◽  
Mean Variance

scholarly journals The Determinants of the Performance of Precious Metal Mutual Funds

2020 ◽  
Vol 13 (11) ◽  
pp. 286
Author(s):  
Ioannis E. Tsolas
Keyword(s):  
Mutual Funds ◽  
Precious Metal ◽  
Performance Metrics ◽  
Additive Model ◽  
Fund Performance ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Beta Coefficient ◽  
First Time ◽  
Mean Variance

The aim of this paper is to assess the efficiency of a set of 62 precious metal mutual funds (PMMFs) and to explain performance differences between funds using weighted additive data envelopment analysis (DEA) and Tobit regression, respectively. The contribution of this paper is twofold: to provide for the first-time metrics of the relative performance of PMMFs using a particular weighted additive model, namely the range-adjusted measure (RAM), and to explain the performance of the funds by the use of a Tobit model. Results do not suggest positive linkages between RAM-based and standard fund performance metrics (Sharpe ratio and Jensen’s alpha). Moreover, for the sample inefficient funds the mean–variance performance hypothesis does not hold. In addition, fund performance based on RAM can be explained by the persistence of the fund and the beta coefficient.

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