Everyone is welcome to attend. Refreshments will be served in the Math Lounge before the exam.
Thursday, June 26, 2014
11:10 a.m.
BA6183, 40 St George St.
PhD Candidate: Ryan Donnelly
Supervisor: Sebastian Jaimungal
Thesis title: Ambiguity Aversion in Algorithmic and High Frequency
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Abstract:
The concept of model uncertainty is one of increasing importance in the field of Mathematical Finance. The main goal of this work is to explore model uncertainty in the specifi c area of algorithmic and high frequency trading. From a behavioural perspective, model uncertainty naturally leads to the notion of ambiguity aversion – a person’s tendency
to avoid situations in which randomness plays a role, but the type of randomness itself is uncertain.
Electronic trading algorithms rely heavily on stochastic models of relevant variables, and the act of postulating a specifi c model creates vulnerabilities and risks due to model misspeci fication. Within the setting of a commonly used model for limit order and market order dynamics, the e ffects of protecting oneself against such misspecifi cation in both high frequency market making and liquidation scenarios are investigated. In this case, diff erent types of ambiguity aversion are shown to have diff erent e ffects on optimal behaviour.
Further in this work, a new reference model is introduced in order to alleviate some practical issues with the original model. This model results in a highly simplifi ed set of allowable trading behaviours, but introduces powerful predictive elements. This work concludes with another investigation of the e ffects of ambiguity aversion in the context of this new model.
The thesis can be found in this link: http://individual.utoronto.ca/donnelly/donnelly_thesis.pdf
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