earticle

영어

The essence of strategic asset allocation (SAA) is to derive the optimal asset allocation taking into account the diversification effect of each asset class under the same risk and return profile structure. The mean variance optimization method is useful when the structure of the risk and return of assets is homogeneous. However, due to the emergence of various alternative investments, methods for determining optimal asset allocation are the subject of controversy. We review the problems of the traditional SAA method that is used by pension funds and propose a new SAA method that considers multiple factors. We examine how to apply the new model to domestic pension funds. We provide a new asset allocation paradigm that provides a reasonable weighting for alternative investments and a basis for determining foreign currency hedge ratios for foreign investments. A review of domestic pension funds’ portfolios, assuming a similar target rate of returns and risk tolerance limits, identifies big differences in the weights given to alternative investments. The variation occurs because most pension funds predetermine the portion of the portfolio that will be alternative investments and then perform an SAA only for the remaining asset classes. As decisions on the weights of alternative investment consider the entire portfolio, it is necessary to improve the current asset-based SAA method that performs the allocation after excluding the alternative investment. Factor-based SAA can be a useful method for incorporating alternative investments into the asset allocation framework and can identify methodologies for integrating risk with existing assets such as stocks and bonds. There are four steps in factor-based SAA: selecting an asset mapping the asset class to each corresponding factor determining the target factor weight and re-mapping it to the asset class. In the first step, an appropriate factor must be selected to explain the payoff of each asset class. We use a principal component analysis to extract the common elements in all of the asset classes and then conduct a correlation analysis with the factor candidates to determine growth, inflation, real interest rates, credit, and FX. It is interesting to note that the inflation factor has a higher negative correlation with the other factors during an economic crisis, confirming that investing in the inflation factor during a crisis can effectively hedge a portfolio’s returns. Second, to estimate the factor coefficients for each asset using a multifactor model it is necessary to map the asset class to each corresponding factor. The estimated coefficients are then used as a link in determining the target factor exposure and to determine the optimal asset allocation. The current six asset classes are re-defined and divided into 17 asset classes based on the cash flow and risk characteristics of each asset class. In particular, we classify the alternative investments into equity and debt types. In this way, we can make the risk and profit structure of each asset class as homogeneous as possible. Third, the target factor weight is determined by selecting an appropriate factor exposure for the risk profile of the pension fund. The target factor weight should be consistent with the pension fund’s investment objectives and risk tolerance limits and is also a key element in determining the optimal allocation of investment assets. To determine the target factor exposure, we first derive a factor-based efficient frontier. Once the factor-based efficient frontier has been acquired, the optimal factor exposure with the highest Sharpe ratio is selected from the alternatives that meet the target returns and risk tolerances of the pension fund. In this case, the target factor exposure is based on the implied factor exposure obtained from the current portfolio’s starting point. Fourth, to remap to the asset class, we determine the asset class that meets the target factor weight. This process usually uses an optimization method. We set up a hypothetical portfolio of pension funds and analyze these portfolios. The implications of the case analysis are summarized as follows. First, to increase the Sharpe ratio, it is desirable to reduce the domestic equity investments and increase the portion of overseas investments. Second, the foreign exchange hedge policy of the pension fund is too conservative and should be expanded. Third, the impact of alternative investments on the portfolio’s risk-return profile varies by its composition, such that the weights should be distributed based on the correlation of factors and factor exposures. In addition, factor-based SAA allows monitoring of the exposure of investment assets to each of the corresponding risk factors at all of the times when effective risk monitoring for alternative investments is expected.

한국어

다양한 대체투자 자산들의 출현으로 인해 기존의 방법으로 최적 자산배분(안)을 도출하는 과정에 대한 논란이 제기되고 있다. 본 연구는 기존 연기금들의 SAA 방식의 문제점을 살펴보고, 그 대안으로 팩터(factors)에 기반한 새로운 SAA 방식을 제시하고 이를 국내 연기금들에 적용하는 방안을 검토하였다. 본 연구는 대체투자에 대한 적정비중, 해외 투자의 환 헤지 비중 결정 등 연기금들의 고민에 논리적 근거를 제시할 수 있는 새로운 자산배분의 패러다임을 제공한다. 본 연구에서는 가상 연금을 가정하여 사례분석을 행하였는데 사례분석의 결과에 따른 시사점을 정리하면 다음과 같다. 샤프 비율을 개선시키기 위해서는 우선, 국내 주식의 투자 비중을 줄이고 해외 투자의 비중을 늘리는 것이 바람직하다. 둘째, 현재 연기금의 환위험 헤지 정책은 지나치게 보수적이므로 환에 대한 익스포저를 늘리는 것을 검토할 필요가 있다. 셋째, 대체투자는 구성내역에 따라 포트폴리오 전체의 리스크와 수익 구조에 미치는 영향이 크게 다르므로 팩터들에 대한 익스포저와 그들 간의 상관관계를 고려하여 배분 비중을 결정해야 한다는 것이다. 아울러 이 방법론을 활용하면 실제 운용되는 투자자산이 각 리스크 팩터에 노출되는 상황을 상시 모니터링 할 수 있기에, 대체투자에 대한 리스크 모니터링을 효율적으로 수행할 수 있는 부수효과도 기대된다.