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• W1469156970 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." @default.
• W1469156970 created "2016-06-24" @default.
• W1469156970 creator A5058725794 @default.
• W1469156970 date "2014-04-01" @default.
• W1469156970 modified "2023-09-23" @default.
• W1469156970 title "Modelling animal spirits in financial markets" @default.
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• W1469156970 doi "https://doi.org/10.25560/24868" @default.
• W1469156970 hasPublicationYear "2014" @default.
• W1469156970 type Work @default.
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• W1469156970 crossrefType "dissertation" @default.
• W1469156970 hasAuthorship W1469156970A5058725794 @default.
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