Dynamic Mean–Variance Asset Allocation

CFA Digest ◽  
2010 ◽  
Vol 40 (4) ◽  
pp. 47-49
Author(s):  
Johann U. de Villiers
Keyword(s):  
Asset Allocation ◽  
Mean Variance

Scaling and Adaptive Asset Allocation

2019 ◽  
Keyword(s):  
Asset Allocation ◽  
Investment Decision ◽  
Investment Risk ◽  
Speculative Bubbles ◽  
Time Horizons ◽  
Investment Decision Making ◽  
Time Diversification ◽  
Mean And Variance ◽  
Time Specific ◽  
Mean Variance

In this article, the author reminds us again that return mean and variance are not enough. Appropriate investment risk-bearing scales with surplus over future withdrawal commitments, as well as with investment return characteristics. This framework provides for the integration of financial planning and investment decision-making. Its time-varying risk aversion with the ratio of investments to surplus also provides an opportunity for use of dynamic strategies, though speculative bubbles require compensating inputs to avoid excessive allocation extremes. Appropriate risk-bearing can also scale with functions of shortfall probability to deal with time-specific funding requirements. The probability of avoiding shortfall from an initial surplus over longer time horizons may scale close to the square root of time, creating an illusion of time diversification. In contrast, from an initial surplus deficit, minimizing shortfall probability is akin to playing Russian roulette. Allocations based on minimized shortfall probability can be usefully blended with mean–variance allocations, especially for 5- to 15-year time horizons.

Horses for Courses: Mean-Variance for Asset Allocation and 1/N for Stock Selection

2019 ◽  
Author(s):  
Emmanouil Platanakis ◽  
Charles M. Sutcliffe ◽  
Xiaoxia Ye
Keyword(s):  
Asset Allocation ◽  
Stock Selection ◽  
Mean Variance

Characteristics analysis of behavioural portfolio theory in the Markowitz portfolio theory framework

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saksham Mittal ◽  
Sujoy Bhattacharya ◽  
Satrajit Mandal
Keyword(s):  
Emerging Markets ◽  
Asset Allocation ◽  
Portfolio Theory ◽  
Optimal Portfolio ◽  
Content Type ◽  
Major Index ◽  
Brics Countries ◽  
Trade Offs ◽  
Characteristics Analysis ◽  
Mean Variance

PurposeIn recent times, behavioural models for asset allocation have been getting more attention due to their probabilistic modelling for scenario consideration. Many investors are thinking about the trade-offs and benefits of using behavioural models over conventional mean-variance models. In this study, the authors compare asset allocations generated by the behavioural portfolio theory (BPT) developed by Shefrin and Statman (2000) against the Markowitz (1952) mean-variance theory (MVT).Design/methodology/approachThe data used have been culled from BRICS countries' major index constituents from 2009 to 2019. The authors consider a single period economy and generate future probable outcomes based on historical data in order to determine BPT optimal portfolios.FindingsThis study shows that a fair number of portfolios satisfy the first entry constraint of the BPT model. BPT optimal portfolio exhibits high risk and higher returns as compared to typical Markowitz optimal portfolio.Originality/valueThe BRICS countries' data were used because the dynamics of the emerging markets are significantly different from the developed markets, and many investors have been considering emerging markets as their new investment avenues.

Alternative to the Mean-Variance Asset Allocation Analysis

2009 ◽  
pp. 231-253
Author(s):  
Georges H√ºbner ◽  
Michael Schyns ◽  
yves Crama
Keyword(s):  
Asset Allocation ◽  
The Mean ◽  
Mean Variance

scholarly journals Abnormal Portfolio Asset Allocation Model: Review

2020 ◽  
Vol 1 (1) ◽  
pp. 46-54
Author(s):  
Nurfadhlina Bt Abdul Halima ◽  
Dwi Susanti ◽  
Alit Kartiwa ◽  
Endang Soeryana Hasbullah
Keyword(s):  
Asset Allocation ◽  
Asset Returns ◽  
Higher Moments ◽  
Investment Portfolio ◽  
Allocation Model ◽  
Model Review ◽  
The Mean ◽  
Mean Variance ◽  
Skewness And Kurtosis ◽  
Normally Distributed

It has been widely studied how investors will allocate their assets to an investment when the return of assets is normally distributed. In this context usually, the problem of portfolio optimization is analyzed using mean-variance. When asset returns are not normally distributed, the mean-variance analysis may not be appropriate for selecting the optimum portfolio. This paper will examine the consequences of abnormalities in the process of allocating investment portfolio assets. Here will be shown how to adjust the mean-variance standard as a basic framework for asset allocation in cases where asset returns are not normally distributed. We will also discuss the application of the optimum strategies for this problem. Based on the results of literature studies, it can be concluded that the expected utility approximation involves averages, variances, skewness, and kurtosis, and can be extended to even higher moments.

Mean–Variance Optimization for Asset Allocation

2021 ◽  
Vol 47 (5) ◽  
pp. 24-40
Author(s):  
Jang Ho Kim ◽  
Yongjae Lee ◽  
Woo Chang Kim ◽  
Frank J. Fabozzi
Keyword(s):  
Asset Allocation ◽  
Mean Variance

scholarly journals Mean-Variance Optimization Is a Good Choice, But for Other Reasons than You Might Think

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Andrea Rigamonti
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Risk Measures ◽  
Traditional Approach ◽  
Real Data ◽  
Downside Risk ◽  
Good Choice ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Allocation Decisions

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize it for asset allocation decisions. We then apply this approach to a variety of simulated and real data and show that the traditional approach based on the variance generally outperforms it. The results hold even if the CVaR is used, because all downside risk measures are difficult to estimate. The popularity of variance as a measure of risk appears therefore to be rationally justified.

Horses for courses: Mean-variance for asset allocation and 1/N for stock selection

2021 ◽  
Vol 288 (1) ◽  
pp. 302-317 ◽  
Author(s):  
Emmanouil Platanakis ◽  
Charles Sutcliffe ◽  
Xiaoxia Ye
Keyword(s):  
Asset Allocation ◽  
Stock Selection ◽  
Mean Variance

Dynamic mean variance asset allocation: Tests for robustness

2017 ◽  
Vol 04 (02n03) ◽  
pp. 1750021 ◽  
Author(s):  
Peter A. Forsyth ◽  
Kenneth R. Vetzal
Keyword(s):  
Asset Allocation ◽  
Stock Index ◽  
Dynamic Asset Allocation ◽  
Constant Weight ◽  
Terminal Wealth ◽  
Optimal Dynamic ◽  
Yearly Maximum ◽  
Hamilton Jacobi Bellman ◽  
Equity Index ◽  
Mean Variance

We consider a portfolio consisting of a risk-free bond and an equity index which follows a jump diffusion process. Parameters for the inflation-adjusted return of the stock index and the risk-free bond are determined by examining 89 years of data. The optimal dynamic asset allocation strategy for a long-term pre-commitment mean variance (MV) investor is determined by numerically solving a Hamilton–Jacobi–Bellman partial integro-differential equation. The MV strategy is mathematically equivalent to minimizing the quadratic shortfall of the target terminal wealth. We incorporate realistic constraints on the strategy: discrete rebalancing (yearly), maximum leverage, and no trading if insolvent. Extensive synthetic market tests and resampled backtests of historical data indicate that the multi-period MV strategy achieves approximately the same expected terminal wealth as a constant weight strategy, but with much smaller variance and probability of shortfall.

Sign in / Sign up

Export Citation Format

Share Document