Behavioural Financial Decision Making Under Uncertainty

Abstract

Ever since von Neumann and Morgenstern published the axiomisation of\nExpected Utility Theory, there have been a considerable amount of ob-\nservations appeared in the literature violating the expected utility theory.\nTo make decisions under uncertainty, people generally separate possible\noutcomes into gains and losses. They are risk averse for gains but risk\nseeking for losses with very large probabilities; risk averse for losses but\nrisk seeking for gains with very small probabilities. To accommodate\nthese characteristics, Prospect Theory and its improvement Cumulative\nProspect Theory were developed in order to formulate people's behaviours\nunder uncertainty in a descriptive and normative way. As such, values are\nassigned to gains and losses and probabilities are replaced by probability\nweighting functions. The CPT models built in this project are based on\nthe power value function and the compound invariant form of probability\nweighting function. The models are calibrated with the data from Hong\nKong Mark Six lottery market. The parameters in the models are esti-\nmated, hence to examine properties of the models and give an insights into\nhow they fit the real life situation. In the first approach, the parameter\nin the value function is fixed, but the plots of the estimated probability\nweighting function do not give sensible explanations of lottery player's\nbehaviours. In the second approach, the parameters in value function and\nweighting function are both estimated from the data to give an optimal\nfitting of the model.

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