| On September 11,2016,the China Securities Regulatory Commission promulgated the "Guidelines for the Operation of Publicly Raised Securities Investment Funds No.2-Guidelines for Funds in Funds",which opened the prelude to the issuance of publicly-launched FOF products in China.After more than three years of development,many publicly-launched FOF products have appeared in the market,but the revenue and scale of these FOF products are not ideal.Under this background,how fund companies can find a suitable allocation method for the characteristics of funds in order to obtain stable returns and at the same time contribute to the development of the capital market has become an important research topic.This article compares the development of the FOF industry in China and the US FOF,and finds the advantages and trends of FOF allocation of ETF assets.In the planning of FOF allocation of ETF assets,the Black-Litterman model was selected to construct a model of FOF funds to invest in ETF assets.First,using ARMA-GARCH to predict the returns of ETF assets,as a subjective point of view of investors in the market,tries to make the investment structure of FOF funds more scientific and reasonable.Second,the predicted rate of return is regarded as the subjective return of the Black-Litterman model.By constructing a Black-Litterman,minimum variance,equal weight,and market allocation of FOF funds from November 2018 to November 2019,a comparative analysis is obtained: First,the BL model innovatively introduces investors in the mark-maintained variance model of Mark From the perspective of income,this has improved the efficiency of ETF asset allocation relative to market allocation.After introducing the subjective point of view,the BL model adjusted the allocation weight according to the market value weight.Due to the expected effect,the allocation of the correspondingETF assets was increased and reduced.Compared with market allocation,the strategy improves the return of FOF portfolio and reduces the risk of FOF portfolio.Second,in the first period of strategy execution,the Sharpe ratio of the BL model strategy is not as good as the performance of the minimum variance strategy and the equal weight model strategy.There are certain advantages in balancing risks.Finally,in view of the BL model using the historical covariance matrix estimation method,in the process of practice,it is easy for the model to be unstable and the configuration weights to appear extreme values.Therefore,in order to overcome the problem of extreme values,this paper uses the method of combining the compressed estimation method and the BL model to optimize the B-L strategy.And compare with the previous strategy.The empirical results show that: First,the BL model based on the compressed estimation method is more powerful than other models in improving the return of FOF funds.Second,the BL strategy portfolio based on the compressed estimation method has higher portfolio income and portfolio risk.From the perspective of the regulatory layer's constraints on FOF capital allocation assets,the regulatory level has a tendency to diversify various types of underlying assets of the FOF.Based on the compressed estimate The covariance BL model can solve the problems of unstable model and extreme value of weight in BL strategy during the solution process.The advantage of the compressed estimation method in solving the covariance matrix is ??verified.Third,when the multi-market shows a decline,the retraction of the BL strategy based on the compression estimation method is smaller than other strategies.In the initial period of market recovery,the BL strategy based on the compressed estimation method is relatively weaker than the BL strategy in terms of net worth performance.In the mid-to-late period of market recovery,the BL strategy based on the compressed estimation method can have better net worth performance.From the performance of net value,it can be seen that BL strategy based oncompression estimation is more practical. |
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