An asset pricing model with loss aversion and its stylized facts

TY - CHAP

T1 - An asset pricing model with loss aversion and its stylized facts

AU - Pruna, Radu

AU - Polukarov, Maria

AU - Jennings, Nicholas

PY - 2017/2/13

Y1 - 2017/2/13

N2 - A well-defined agent-based model able to match the widely observed properties of financial assets is valuable for testing the implications of various empirically observed heuristics associated with investors behaviour. In this paper, we extend one of the most successful models in capturing the observed behaviour of traders, and present a new behavioural asset pricing model with heterogeneous agents. Specifically, we introduce a new behavioural bias in the model, loss aversion, and show that it causes a major difference in the agents interactions. As we demonstrate, the resulting dynamics achieve one of the major objectives of the field, replicating a rich set of the stylized facts of financial data. In particular, for the first time our model enables us to match the following empirically observed properties: conditional heavy tails of returns, gains/loss asymmetry, volume power-law and long memory and volume-volatility relations.

AB - A well-defined agent-based model able to match the widely observed properties of financial assets is valuable for testing the implications of various empirically observed heuristics associated with investors behaviour. In this paper, we extend one of the most successful models in capturing the observed behaviour of traders, and present a new behavioural asset pricing model with heterogeneous agents. Specifically, we introduce a new behavioural bias in the model, loss aversion, and show that it causes a major difference in the agents interactions. As we demonstrate, the resulting dynamics achieve one of the major objectives of the field, replicating a rich set of the stylized facts of financial data. In particular, for the first time our model enables us to match the following empirically observed properties: conditional heavy tails of returns, gains/loss asymmetry, volume power-law and long memory and volume-volatility relations.

U2 - 10.1109/SSCI.2016.7850003

DO - 10.1109/SSCI.2016.7850003

M3 - Conference paper

BT - 2016 IEEE Symposium Series on Computational Intelligence (SSCI)

PB - IEEE

ER -