Study Of Asset Allocation For Open-end Funds Based On Liquidity Risk

Though the history of open-end funds is less than 10-year in China, though the history of open-end funds is less than 10-year in China, as a very important investment product, open-end funds attracted lots of attention. Compared with the closed-end funds which well-known by domestic investors, the open-end funds has liquidity risk problems while it has many advantages. Based on the formation mechanism, it fingers out that the system of free redemption of open-end funds results in the inevitable liquidity risk. Meanwhile, macroeconomic policy, habits of investors, maturity of financial markets will affect the liquidity risk of funds. From the fund manager's point of view, these aspects are beyond the control, the manager can only reduce the negative effects of liquidity risk by manage the asset allocation of open-end funds.Therefore, this paper attempts to identify the most appropriate asset allocation of the fund in order to reduce the liquidity risk of funds. First of all, through the analysis, this paper found that both insufficient and excessive liquid assets, will bring negative impact to the Fund's performance. so, this paper defines the cost of liquidity of open-end funds, then gives mobility methods to characterize the volatility of cost, and a suitable model for the ratio of liquid assets.In addition, this paper statistically used the distribution of the net redemption amount of the loss factor and mobility of open-end funds to give liquid formula for expected costs, fund managers can make mobility a way to find the minimum expected cost to the appropriate allocation ratio of liquid assets.Then, through do regression analysis to a set of data, this paper empirical analysis of the model and find a suitable proportion.To make the results more practical and convenient operation of fund managers, this paper took advantage of Miller-Orr Model to give fund liquid assets upper and lower limits which ensure the smallest loss of liquidity, and gives the empirical results. Then, established a range of improved model in different accuracies, the fund managers can choose their favorite one to build the upper and lower limits of security zone for the configuration of the Fund's assets.