A Proposed Model to Behaviourally Pricing Risk

Jacques Peeperkorn

doi:10.22610/jebs.v6i6.509

Abstract

To imagine that asset pricing is not dependant on behavioural heuristics and game theory, we are required to reduce the definition of the participants to that of utility maximising, risk-averse, uniform automata. This study examines this statement through an application of behavioural theory that speaks to the ability of investors to perceive risk, as well as the interactive effects of game theory to distort the perception of risk from exogenous variables to that of endogenous probability beliefs. We present a foundation for a state-space model, such as a Kalman filter, to be used in pricing risk.

Highlights

The treatment of risk as a probability belief dictated by behavioural heuristics is a significant divergence from traditional pricing theory
We argue that an asset pricing model should include a learning based component in its estimation; and provide a foundation for the use of a Kalman filter in this regard
How are risks identified, processed and priced by individuals? What risks are relevant to asset pricing? Are all non-systematic risks diversified? Are both homogenous and heterogeneous behavioural predispositions to investors appropriately considered?. The results of this investigation dictate that an appropriate model should follow from the single risk factor design of the traditional capital asset pricing model (CAPM), as seen in Equation 1 below, where the expected return of share i is a function of the risk free rate and the product of the exposure of the share to the market (β) and an equity risk premium(rm − rf )

Summary

Introduction

The results of this investigation dictate that an appropriate model should follow from the single (systematic) risk factor design of the traditional capital asset pricing model (CAPM), as seen in Equation 1 below, where the expected return of share i is a function of the risk free rate (rf) and the product of the exposure of the share to the market (β) and an equity risk premium(rm − rf ). The fundamental departure from the traditional CAPM comes in how, as a cross-sectional collective, the risk representative slope coefficient of the linear regression model is specified, as well as how it is described to evolve over time. This risk factor should be considerate of specific individual and interactive behavioural characteristics, which can be seen to represent the typical investor. We provide a qualitative outline of our argument for the inclusion of a learning augmented approach to pricing risk This outline includes discussion around probability beliefs, risk and utility.

A Qualitative Analysis

Establishing probability beliefs

Behavioural evolution

Into model design

Findings

Conclusion