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Dynamic Proportion Portfolio Insurance with Genetic Programming and Market Volatility Factors Analysis

. National Central University, Jungli, Taiwan, (30 June 2005)

Abstract

This thesis 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. It helps investor easily to understand how to allocate the capital among risky and risk-free assets and straightforward to implement. The risk multiplier in CPPI is predetermined by the investor's view-point and fixed to the end of investment duration. However, since the market changes constantly, we think that the risk multiplier should change accordingly. When the market becomes volatile, the predetermined large risk multiplier will lead to loss of insurance and DPPI may solve this kind of problem. This research identifies factors relating to market volatility. These factors are built into equation trees by genetic programming. We collected five stocks of American companies' financial data and the market information of New York Stock Exchange as input data feeding genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy. Because the equation trees are all different, there is no method to analyse the factor contributions to the results of the risk multiplier. We use principal component analysis to see the effect of factors, and the experimental results show that among the market volatility factors, risk-free rate influences the variances of risk multiplier most.

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