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.