Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.656635
Title: Modelling animal spirits in financial markets
Author: Andruszkiewicz, Grzegorz
ISNI:       0000 0004 5348 9065
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2014
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Abstract:
The term 'animal spirits' was introduced by Keynes to describe the entrepreneur's often irrational optimism and drive to act as opposed to basing decisions on formal analysis. This PhD thesis provides an analysis, both theoretical and empirical, of this phenomenon in the financial markets from several points of view. In the first chapter we show that the pricing kernel in the economy may be represented in a probabilistic form, as a solution to a stochastic filtering problem. The noise in the associated information process may contain drift term that is impossible to estimate from current market prices of assets. This drift can be associated with 'animal spirits' driving the market. The second chapter is explicitly devoted to 'animal spirits': it introduces a factor based risk-management model for an illiquid project. We show that behavioural factors together with the collateralization mechanism often employed by banks not only increase the risk for the banking system, but also introduce anomalies during high-volatility crisis periods. In the third chapter we apply Hidden Markov Models to estimate animal spirits from historic asset prices. We argue that an arbitrary addition of a stress scenario to the model can greatly improve risk estimation. The last chapter deals with optimal investment problem in a model with behavioural factors. This may be linked to the pricing kernel discussion from the first chapter by the marginal utility maximisation approach to pricing derivatives.
Supervisor: Davis, Mark H. A. Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.656635  DOI: Not available
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