Dynamic proportion portfolio insurance using genetic programming with principal component analysis

This paper proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change according to market conditions. This research identifies risk variables relating to market conditions. These risk variables are used to build the equation tree for the risk multiplier by genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. In addition, principal component analysis of the risk variables in equation trees indicates that among all the risk variables, risk-free interest rate influences the risk multiplier most.