Mathematical and Computational Finance Seminar

Please note that the list below only shows forthcoming events, which may not include regular events that have not yet been entered for the forthcoming term. Please see the past events page for a list of all seminar series that the department has on offer.

Past events in this series
Tomorrow
16:00
to
17:30
Prof Teemu Pennanen
Abstract

Welfare economics argues that competitive markets lead to efficient allocation of resources. The classical theorems are based on the Walrasian market model which assumes the existence of market clearing prices. The emergence of such prices remains debatable. We replace the Walrasian market model by double auctions and show that the conclusions of welfare economics remain largely the same. Double auctions are not only a more realistic description of real markets but they explain how equilibrium prices and efficient allocations emerge in practice. 

  • Mathematical and Computational Finance Seminar
1 November 2018
16:00
to
17:30
Jan Palczewski
Abstract

I will start from a short survey of numerical methods for stochastic control problems from a point of view of dimensionality of the state and control space. The main part of the talk will be devoted to the presentation of a Regression Monte Carlo algorithm for optimal stochastic control of discrete-time Markov processes, a joint work with my PhD student Alessandro Balata. I will talk about convergence (first such results in a general discrete time setting) and dual bounds. Performance will be explored on problems of minimising interbank systemic risk with partial observation and managing an energy system with storage facilities.

  • Mathematical and Computational Finance Seminar
4 December 2018
16:00
to
17:30
Anne Balter
Abstract

Authors:

Anne Balter and Antoon Pelsser

Models can be wrong and recognising their limitations is important in financial and economic decision making under uncertainty. Robust strategies, which are least sensitive to perturbations of the underlying model, take uncertainty into account. Interpreting

the explicit set of alternative models surrounding the baseline model has been difficult so far. We specify alternative models by a stochastic change of probability measure and derive a quantitative bound on the uncertainty set. We find an explicit ex ante relation

between the choice parameter k, which is the radius of the uncertainty set, and the Type I and II error probabilities on the statistical test that is hypothetically performed to investigate whether the model specification could be rejected at the future test horizon.

The hypothetical test is constructed to obtain all alternative models that cannot be distinguished from the baseline model with sufficient power. Moreover, we also link the ambiguity bound, which is now a function of interpretable variables, to numerical

values on several divergence measures. Finally, we illustrate the methodology on a robust investment problem and identify how the robustness multiplier can be numerically interpreted by ascribing meaning to the amount of ambiguity.

  • Mathematical and Computational Finance Seminar
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