Abstract
This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.
Highlights
In any financial institution, there might be no dissenting opinion regarding that asset allocation policies are crucial. Brinson et al (1986) analyze the performance of pension funds in the US between 1974 and 1983 and report that the effect of asset allocation on management can explain 93.6% of the fund’s return volatility. Ibbotson and Kaplan (2000) and Hensel et al (1991) report similar results
Instead of relying on such a statistical and mathematical model, this study aims to use a more practical model by conducting an in-sample empirical analysis using tactical asset allocation (TAA) based on the mean-variance optimization (MVO) model
It is preferable to use the same MVO methodology; the annual TAA based on the MVO method is dynamic asset allocation and the long-term strategic asset allocation (SAA) based on the MVO model is static asset allocation rather than heterogeneous analysis methods, such as stochastic programming, to maintain the consistency of the analysis methodology
Summary
There might be no dissenting opinion regarding that asset allocation policies are crucial. Brinson et al (1986) analyze the performance of pension funds in the US between 1974 and 1983 and report that the effect of asset allocation on management can explain 93.6% of the fund’s return volatility. Ibbotson and Kaplan (2000) and Hensel et al (1991) report similar results. Brinson et al (1986) analyze the performance of pension funds in the US between 1974 and 1983 and report that the effect of asset allocation on management can explain 93.6% of the fund’s return volatility. Won et al (2013), as shown, estimate that strategic asset allocation (SAA) contributes 6.04% toward a total annual average return of 6.01%, while active investment performance amounts to 3 bps. The contribution will vary depending on which evaluation period is examined, as in overseas cases, asset allocation largely explains investment performance in domestic asset management. Asset allocation, both at home and abroad, is a very important decision-making task that determines most of the portfolio’s returns and risks
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