Asset allocation for a DC pension fund with stochastic income and mortality risk: A multi-period mean–variance framework

2014 ◽  
Vol 54 ◽  
pp. 84-92 ◽  
Author(s):  
Haixiang Yao ◽  
Yongzeng Lai ◽  
Qinghua Ma ◽  
Minjie Jian
Keyword(s):  
Mortality Risk ◽  
Asset Allocation ◽  
Pension Fund ◽  
Mean Variance

scholarly journals Artificial Intelligence Approach to Secure Pension Fund

2021 ◽  
Vol 23 (07) ◽  
pp. 110-120
Author(s):  
Safwat Saadeldin ◽  
◽  
Hegazy Zaher ◽  
Naglaa Ragaa ◽  
Heba Sayed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Asset Allocation ◽  
Pension Fund ◽  
Planning Horizon ◽  
Return Level ◽  
Insolvency Risk ◽  
Finance Management ◽  
Portfolio Optimization Model ◽  
Mean Variance

Pension fund needs to produce a high-income return to face actuarial expectations of different kinds of benefits. An asset allocation management model of a pension fund must consider a large planning horizon because of its long-term obligations. Asset allocation controls the solvency of the fund by suitable investments and contribution policies to secure the pensioner’s future liabilities. Artificial intelligence approaches given by experts and accepted by decision-makers, provide a powerful tool for describing uncertainty. A portfolio optimization model is introduced based on variance minimization at a required return level that secures the fund against insolvency risk. This method uses an artificial Bee ColonyOptimizationApproach to the mean-variance defined by Markowitz so that future returns of the stocks are predicted where the ability of AI to improve predictive and prescriptive financial forecasting processes will change the world of finance management.

scholarly journals Pension Fund Asset Allocation: A Mean-Variance Model with CVaR Constraints

2017 ◽  
Vol 108 ◽  
pp. 1302-1307 ◽  
Author(s):  
Yibing Chen ◽  
Xiaolei Sun ◽  
Jianping Li
Keyword(s):  
Asset Allocation ◽  
Pension Fund ◽  
Variance Model ◽  
Mean Variance

A Global Perspective on Pension Fund Asset Allocation

CFA Digest ◽  
1998 ◽  
Vol 28 (4) ◽  
pp. 45-46
Author(s):  
Charles F. Peake
Keyword(s):  
Asset Allocation ◽  
Pension Fund ◽  
Global Perspective

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.

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.

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