Does Household Finance Matter? Small Financial Errors with Large Social Costs

2019 ◽  
Vol 109 (3) ◽  
pp. 1116-1154 ◽  
Author(s):  
Harjoat S. Bhamra ◽  
Raman Uppal
Keyword(s):  
Asset Allocation ◽  
Welfare Loss ◽  
Real Economy ◽  
Risky Assets ◽  
Financial Decisions ◽  
Intertemporal Consumption ◽  
Aggregate Growth ◽  
Mean Variance ◽  
Portfolio Return ◽  
Modest Reduction

Households with familiarity biases tilt their portfolios toward a few risky assets. The resulting mean-variance loss from portfolio underdiversification is equivalent to only a modest reduction of about 1  percent per year in a household’s portfolio return. However, once we consider also the effect of familiarity biases on the asset- allocation and intertemporal consumption-savings decisions, the welfare loss is multiplied by a factor of four. In  general equilibrium, the suboptimal decisions of households distort also aggregate growth, amplifying further the overall social welfare loss. Our findings demonstrate that financial markets are not a mere sideshow to the real economy and that improving the financial decisions of households can lead to large benefits, not just for individual households, but also for society. (JEL D14, D91, E21, E44, G11, G41)

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.

DYNAMIC ASSET ALLOCATION FOR TARGET DATE FUNDS UNDER THE BENCHMARK APPROACH

2021 ◽  
pp. 1-26
Author(s):  
Jin Sun ◽  
Dan Zhu ◽  
Eckhard Platen
Keyword(s):  
Asset Allocation ◽  
Dynamic Asset Allocation ◽  
Growth Optimal Portfolio ◽  
Risky Assets ◽  
Target Date ◽  
Target Date Funds ◽  
Asset Allocations ◽  
Investment Choices ◽  
Glide Paths

ABSTRACT Target date funds (TDFs) are becoming increasingly popular investment choices among investors with long-term prospects. Examples include members of superannuation funds seeking to save for retirement at a given age. TDFs provide efficient risk exposures to a diversified range of asset classes that dynamically match the risk profile of the investment payoff as the investors age. This is often achieved by making increasingly conservative asset allocations over time as the retirement date approaches. Such dynamically evolving allocation strategies for TDFs are often referred to as glide paths. We propose a systematic approach to the design of optimal TDF glide paths implied by retirement dates and risk preferences and construct the corresponding dynamic asset allocation strategy that delivers the optimal payoffs at minimal costs. The TDF strategies we propose are dynamic portfolios consisting of units of the growth-optimal portfolio (GP) and the risk-free asset. Here, the GP is often approximated by a well-diversified index of multiple risky assets. We backtest the TDF strategies with the historical returns of the S&P500 total return index serving as the GP approximation.

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.

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

Mean-variance equilibrium asset-liability management strategy with cointegrated assets

2021 ◽  
Vol 62 ◽  
pp. 209-234
Author(s):  
Mei Choi Chiu
Keyword(s):  
Financial Market ◽  
Time Consistency ◽  
Time Inconsistency ◽  
Asset Liability Management ◽  
Risky Assets ◽  
Liability Management ◽  
Equilibrium Control ◽  
Management Problems ◽  
Hamilton Jacobi Bellman ◽  
Mean Variance

This paper investigates asset-liability management problems in a continuous-time economy. When the financial market consists of cointegrated risky assets, institutional investors attempt to make profit from the cointegration feature on the one hand, while on the other hand they need to maintain a stable surplus level, that is, the company’s wealth less its liability. Challenges occur when the liability is random and cannot be fully financed or hedged through the financial market. For mean–variance investors, an additional concern is the rational time-consistency issue, which ensures that a decision made in the future will not be restricted by the current surplus level. By putting all these factors together, this paper derives a closed-form feedback equilibrium control for time-consistent mean–variance asset-liability management problems with cointegrated risky assets. The solution is built upon the Hamilton–Jacobi–Bellman framework addressing time inconsistency. doi: 10.1017/S1446181120000164

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