Behavioural Financial Decision Making Under Uncertainty

Ever since von Neumann and Morgenstern published the axiomisation of Expected Utility Theory, there have been a considerable amount of observations appeared in the literature violating the expected utility theory. To make decisions under uncertainty, people generally separate possible outcomes into gains and losses. They are risk averse for gains but risk seeking for losses with very large probabilities; risk averse for losses but risk seeking for gains with very small probabilities. To accommodate these characteristics, Prospect Theory and its improvement Cumulative Prospect Theory were developed in order to formulate people’s behaviours under uncertainty in a descriptive and normative way. As such, values are assigned to gains and losses and probabilities are replaced by probability weighting functions. The CPT models built in this project are based on the power value function and the compound invariant form of probability weighting function. The models are calibrated with the data from Hong Kong Mark Six lottery market. The parameters in the models are estimated, hence to examine properties of the models and give an insights into how they fit the real life situation. In the first approach, the parameter in the value function is fixed, but the plots of the estimated probability weighting function do not give sensible explanations of lottery player’s behaviours. In the second approach, the parameters in value function and weighting function are both estimated from the data to give an optimal fitting of the model.