Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486459
Title: Statistical modelling of financial crashes
Author: Fry, John Michael
ISNI:       0000 0001 0737 9971
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2008
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Abstract:
As the stock market came to the attention of increasing numbers of physicists, an idea that has recently emerged is that it might be possible to develop a mathematical theory of stock market crashes. This thesis is primarily concerned with statistical aspects of such a theory. Chapters 1-5 discuss simple models for bubbles. Chapter 1 is an introduction. Chapter 2 describes a skeleton exploratory analysis, before discussing some economic interpretations of crashes and a rational expectations model of financial crashes - a slightly simplified version of that in Johansen et aZ. (2000). This model assumes that economic variables undergo a phase transition prior to a crash, and we give some empirical support of this idea in Chapters 4 and 5. Chapter 3 discusses SDE models for bubbles. We describe maximum likelihood estimation of the Bornette and Andersen (2002) model and refine previous estimation of this model in Andersen and Bornette (2004). Further, we extend this model using a heavy-tailed hyperbolic process, Eberlein and Keller (1995), to provide a robust statistical test for bubbles. In Chapter 4 we examine a range of volatility and liquidity precursors. We have some evidence that crashes occur on volatile illiquid markets and economic interpretation of our results appears interesting. Chapter 5 synthesises Chapters 2-4. In Chapter 6 we develop calculations in Johansen and Bornette (2001), to derive a generalised Pareto distribution for drawdowns. In addition, we review the Bornette et aZ. (2004) method of using power-laws to distinguish between endogenous and exogenous origins of crises. Despite some evidence to support the original approach, it appears that a better model is a stochastic volatility model where the log volatility is fractional Gaussian noise. Arias (2003) makes a distinction between insurance crisis and illiquidity crisis models. In Chapter 7, focusing upon illiquidity crises, we apply the method of Malevergne and Barnette (2005) to evaluate contagion in economics. Chapter 8 summarises the main findings and gives suggestions for further work.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.486459  DOI: Not available
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