scholarly journals Macro Asset Allocation with Social Impact Investments

2019 ◽  
Vol 11 (11) ◽  
pp. 3140 ◽  
Author(s):  
Massimo Biasin ◽  
Roy Cerqueti ◽  
Emanuela Giacomini ◽  
Nicoletta Marinelli ◽  
Anna Grazia Quaranta ◽  
...  
Keyword(s):  
Risk Aversion ◽  
Asset Allocation ◽  
Social Impact ◽  
Large Fraction ◽  
Stock Index ◽  
Portfolio Risk ◽  
Risk Return ◽  
Mean Variance ◽  
Different Levels ◽  
Unique Dataset

Using a unique dataset of 50 listed companies that meet the majority of the OECD requirements for social impact investments, we construct a social impact finance stock index and investigate how investing in social impact firms can contribute to portfolio risk-return performance. We build portfolios with three different methodologies (naïve, Markowitz mean-variance optimization, GARCH-copula model), and we study the performance in terms of returns, Sharpe ratio, utility, and forecast premium based on a constant relative risk aversion function for investors with different levels of risk aversion. Consistent with the idea that social impact investment can improve portfolio risk-return performance, the results of our macro asset allocation analysis show the importance of a large fraction of investor portfolios’ stake committed to social impact investments.

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.

Dynamic asset allocation for a bank under CRRA and HARA framework

2015 ◽  
Vol 02 (03) ◽  
pp. 1550031 ◽  
Author(s):  
Ryle S. Perera
Keyword(s):  
Interest Rate ◽  
Risk Aversion ◽  
Portfolio Choice ◽  
Asset Allocation ◽  
Absolute Risk ◽  
Optimal Investment ◽  
Stock Index ◽  
Investment Horizon ◽  
Power Utility ◽  
Dynamic Portfolio Optimization

This paper analyzes an optimal investment and management strategy for a bank under constant relative risk aversion (CRRA) and hyperbolic absolute risk aversion (HARA) utility functions. We assume that the bank can invest in treasuries, stock index fund and loans, in an environment subject to stochastic interest rate and inflation uncertainty. The interest rate and the expected rate of inflation follow a correlated Ornstein–Uhlenbeck processes and the risk premia are constants. Then we consider the portfolio choice under a power utility that the bank's shareholders can maximize expected utility of wealth at a given investment horizon. Closed form solutions are obtained in a dynamic portfolio optimization model. The results indicate that the optimal proportion invested in treasuries increases while the optimal proportion invested in the loans progressively decreases with respect to time.

ROBUST ASSET ALLOCATION FOR LONG-TERM TARGET-BASED INVESTING

2017 ◽  
Vol 20 (03) ◽  
pp. 1750017 ◽  
Author(s):  
P. A. FORSYTH ◽  
K. R. VETZAL
Keyword(s):  
Standard Deviation ◽  
Asset Allocation ◽  
Optimal Strategy ◽  
Stock Index ◽  
Quadratic Loss ◽  
Leverage Ratio ◽  
Terminal Wealth ◽  
Constant Proportion ◽  
Mean Variance

This paper explores dynamic mean-variance (MV) asset allocation over long horizons. This is equivalent to target-based investing with a quadratic loss penalty for deviations from the target level of terminal wealth. We provide a number of illustrative examples in a setting with a risky stock index and a risk-free asset. Our underlying model is very simple: the value of the risky index is assumed to follow a geometric Brownian motion diffusion process and the risk-free interest rate is specified to be constant. We impose realistic constraints on the leverage ratio and trading frequency. In many of our examples, the MV optimal strategy has a standard deviation of terminal wealth less than half that of a constant proportion strategy which has the same expected value of terminal wealth, while the probability of shortfall is reduced by a factor of two to three. We investigate the robustness of the model through resampling experiments using historical data dating back to 1926. These experiments also show much lower standard deviation and shortfall probability for the MV optimal strategy relative to a constant proportion strategy with approximately the same expected terminal wealth.

scholarly journals Strategic Asset Allocation Of Credit Guarantors

2015 ◽  
Vol 31 (5) ◽  
pp. 1823
Author(s):  
Dong-Woo Rhee ◽  
Hyoung-Goo Kang ◽  
Soo-Hyun Kim
Keyword(s):  
Public Policy ◽  
Asset Allocation ◽  
Minimum Variance ◽  
Risk Return ◽  
Out Of Sample ◽  
Sample Test ◽  
Prior Literature ◽  
The Mean ◽  
Risk Contribution ◽  
Mean Variance

<p>How to manage the portfolio of credit guarantors is important in practice and public policy, but has not been investigated well in the prior literature. We empirically compare four different approaches in managing credit guarantor portfolios. The four approaches are equal weighted, minimum variance, mean variance optimization and equal risk contribution methods. In terms of risk return ratio, the mean variance optimization model performs best in out-of-sample test. This result contrasts with previous findings against mean variance optimization. Our results are robust. The results do not change as the characteristics of guarantee portfolio vary.</p>

Can Asset Allocation Limits Determine Portfolio Risk-Return Profiles in DC Pension Schemes?

2018 ◽  
Author(s):  
Tomás Gutierrez ◽  
Bernardo Pagnoncelli ◽  
Davi Valladão ◽  
Arturo Cifuentes
Keyword(s):  
Asset Allocation ◽  
Portfolio Risk ◽  
Risk Return ◽  
Pension Schemes

The Impact of Financial Crises on the Asset Allocation: Classical Theory Versus Behavioral Theory

2019 ◽  
Vol 32 (2) ◽  
pp. 218-236
Author(s):  
Amen Aissi Harzallah ◽  
Mouna Boujelbene Abbes
Keyword(s):  
Financial Crisis ◽  
Risk Aversion ◽  
Asset Allocation ◽  
Global Financial Crisis ◽  
Optimal Portfolio ◽  
Optimal Portfolios ◽  
The Mean ◽  
Degree Of Risk ◽  
The Impact ◽  
Mean Variance

The aim of this article is to compare the portfolio optimization generated by the behavioral portfolio theory (BPT) and the mean variance theory (MVT) by investigating the impact of the global financial crisis on the asset allocation. We use data from the Canadian Stock Exchange over the 2002–2015 period. By comparing both approaches, we show that for any level of aspiration and admissible failure, the BPT optimal portfolio will always contain a part of the mean–variance frontier. Thus, in the case of higher degree of risk aversion induced by typical BPT investors, the security set is located on the upper right of the Markowitz frontier. However, even if the optimal portfolios of MVT and BPT may coincide, MVT investors associated with an extremely low degree of risk aversion will not systematically choose BPT optimal portfolios. Our results also indicate the period of financial crisis generate huge losses in MVT portfolio values that implies a lower expected return and a higher level of risk. Furthermore, we point out the absence of the BPT optimal portfolio when potential losses are higher during the 2008 global financial crisis. JEL: G11, G17, G40

OPTIMAL TIME-CONSISTENT PORTFOLIO AND CONTRIBUTION SELECTION FOR DEFINED BENEFIT PENSION SCHEMES UNDER MEAN–VARIANCE CRITERION

2014 ◽  
Vol 56 (1) ◽  
pp. 66-90 ◽  
Author(s):  
XIAOQING LIANG ◽  
LIHUA BAI ◽  
JUNYI GUO
Keyword(s):  
Risk Aversion ◽  
Asset Allocation ◽  
Optimization Problems ◽  
Realistic Model ◽  
Optimal Time ◽  
Pension Scheme ◽  
Defined Benefit ◽  
Special Cases ◽  
Hamilton Jacobi Bellman ◽  
Mean Variance

AbstractWe investigate two mean–variance optimization problems for a single cohort of workers in an accumulation phase of a defined benefit pension scheme. Since the mortality intensity evolves as a general Markov diffusion process, the liability is random. The fund manager aims to cover this uncertain liability via controlling the asset allocation strategy and the contribution rate. In order to have a more realistic model, we study the case when the risk aversion depends dynamically on current wealth. By solving an extended Hamilton–Jacobi–Bellman system, we obtain analytical solutions for the equilibrium strategies and value function which depend on both current wealth and mortality intensity. Moreover, results for the constant risk aversion are presented as special cases of our models.

Analysis on the Risk Return Profile of Alternative Assets Under Reference Portfolio Concept

2019 ◽  
Vol 27 (2) ◽  
pp. 193-209
Author(s):  
Su Jin Lee ◽  
Jin Wan Cho ◽  
Jae Hyun Lee
Keyword(s):  
Asset Allocation ◽  
Opportunity Costs ◽  
Strategic Asset Allocation ◽  
Alternative Investments ◽  
Risk Return ◽  
Benchmark Portfolio ◽  
The Mean ◽  
Asset Classes ◽  
Relationship Of ◽  
Mean Variance

This paper provides the methodology of estimating the risk-return relationship of alternative asset investments within the mean-variance framework. While conducting strategic asset allocation, most of the institutional investors do not take into account the risk-return relationship of alternative assets, or use arbitrary policy numbers that do not properly reflect the characteristics of alternative assets. This paper borrows the concept of reference portfolio in developing the methodology of estimating the risk-return relationship of alternative investments. The reference portfolio is the benchmark portfolio used in strategic asset allocation by pension funds. This can serve as the opportunity costs of alternative investments. We use the realized IRR’s from actual investments, and estimate the risk-return characteristics of alternative investments. We find that by properly estimating the mapping relationship between the reference portfolio and alternative asset classes, we can incorporate the risk-return profile of these non-market assets within the mean-variance framework together with the other traditional asset classes.

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