Adjustment Method Of Risk Multiplier For Portfolio Insurance Strategy Based On Momentum

Portfolio insurance strategies can resist systemic risk in the stock market.Portfolio insurance can control the risk of the portfolio to a certain extent.It can limit the losses of the portfolio when the market is down and raise the profit properly when the market is up.The fixed risk multiplier during the whole insurance period can not effectively increase the profit in the market up and can not play a better insurance role in the market down.Therefore,this paper proposes a fixed-proportion portfolio insurance(M-CPPI)based on the momentum-adjusted risk multiplier for the entire insurance period,combining the momentum effects of the Chinese market and the Hong Kong market.Making the risk multiplier increase when the market price momentum is large and decreases when the market price momentum is low.Furthermore,EMD-M-CPPI is proposed based on Empirical Mode Decomposition(EMD)and momentum-adjusted risk multiplier(EMD-M-CPPI),which is based on Empirical Mode Decomposition(EMD).In order to test the actual effect of the two improved CPPI strategies,this paper conducts an empirical study on the CSI 300 Index Futures and Hang Seng Index,and compares them with the traditional CPPI strategy and the buying and holding strategy.The empirical results show that: 1 CPPI strategy is better than buy-and-hold strategy in Chinese market,but it is worse than buy-and-hold strategy in Hong Kong market.2 The M-CPPI in the two markets on the full sample can significantly increase the accumulated value of the initial CPPI portfolio's end-of-period value.3 The EMD-M-CPPI in the two markets on the full sample can significantly increase the accumulated value of the initial CPPI portfolio's end-of-period value.4 The EMD-M-CPPI strategy with empirical mode decomposition(EMD-M-CPPI)is proposed to improve the accumulated value of the M-CPPI strategy in two markets,and reduce the cumulative transaction cost of the M-CPPI strategy.5 The sub-sample performance test shows that the selection of the optimal parameter region is robust,and there is no high parameter over-fitting risk.