2016-17 Financial Mathematics - Seminars

If you are interested in giving a talk at our seminar please contact Foivos Xanthos (foivos@ryerson.ca).

Upcoming Seminars

Thursday, April 6, 2017 at 3:00pm, Location: ENG-210
Dr. Ruodu Wang, University of Waterloo
Title: A theory for measures of tail risk
Abstract: The notion of “tail risk” has been a crucial consideration in modern risk management. To achieve a comprehensive understanding of the tail risk, we carry out an axiomatic study for risk measures which quantify the tail risk, that is, the behavior of a risk beyond a certain quantile. Such risk measures are referred to as tail risk measures in this talk. The two popular classes of regulatory risk measures in banking and insurance, the Value-at-Risk (VaR) and the Expected Shortfall (ES), are prominent, yet elementary, examples of tail risk measures. We establish a connection between a tail risk measure and a corresponding law-invariant risk measure, called its generator, and investigate their joint properties. A tail risk measure inherits many properties from its generator, but not subadditivity or convexity; nevertheless, a tail risk measure is coherent if and only if its generator is coherent. We explore further relevant issues on tail risk measures, such as bounds, distortion risk measures, risk aggregation, elicitability, and dual representations. In particular, there is no elicitable tail convex risk measure other than the essential supremum, and under a continuity condition, the only elicitable and positively homogeneous monetary tail risk measures are the VaRs. The study on tail risk measures brings in new tools and insights for prudent risk management as highlighted in the recent Basel documents on financial regulation. This talk focuses on mathematical developments of the theory.

Recent Seminars

Thursday, November 17, 2016 at 2:00pm, Location: ENG-210
Dr. Sebastian Ferrando, Ryerson University
Title: Trajectorial Martingales, Null Sets, Integration and Convergence.
Abstract: Starting with a trajectory space, providing a non-stochastic analogue of a martingale process, we use the notion of super replication to introduce a definition for a null function and the associated notion of a property holding almost everywhere (a.e.). The latter providing what can be seen as the worst case analogue of sets of measure zero in a stochastic setting. The a.e. notion is used to prove the pointwise convergence, on a full set of the original trajectory space, of the limit of a trajectorial martingale transform sequence. The setting also allows to construct a natural integration operator.

Thursday, October 27, 2016 at 2:00pm, Location: ENG-210
Dr. Foivos Xanthos, Ryerson University
Title: Robust representations of risk measures on Orlicz spaces
Abstract: In 2002, Delbaen proved a robust representation theorem for risk measures on L via L1. It has since been an intriguing problem to extend this result to a more general class of underlying spaces. In this talk, we present a solution to this problem for Orlicz spaces.

Thursday, September 29, 2016 at 2:30pm, Location: ENG-210
Dr. Pablo Olivares, Ryerson University
Title: Pricing and Hedging some Crack and Spark Contracts in Energy Markets under Levy Processes
Abstract: We discuss the pricing of some derivative contracts in the Energy market whose payoff depends on the value of multiple assets. In particular, we focus on European and Barrier options having electricity, gasoline, natural gas and uranium as underlying assets. In order to capture their particular dynamic, we consider models with discontinuous jumps and mean reverting properties. Several ad-hoc approximations and numerical issues are analysed.

Thursday, January 28, 2016 from 12:00pm-1:00pm, Location: ENG288
Speaker: Michael Hasler, Rotman School of Management, University of Toronto
Dynamic Attention Behaviour Under Return Predictability

Thursday, January 21, 2016, 1-2pm, Location: VIC736
Speaker: Dr. Oleksandr Romanko, Research Analyst,
Risk Analytics - Business Analytics, IBM Canada
Adjunct Professor, University of Toronto
Scenario-Based Financial Value-at-Risk Optimization

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