Alexander Schied (Mannheim)
Mathematical aspects of market impact modeling
Abstract: In this talk, we discuss the problem of executing large orders in illiquid markets so as to optimize the resulting liquidity costs. There are several reasons why this problem is relevant. On the mathematical side, it leads to interesting  nonlinearity effects that arise from the price feedback of  strategies. On the economical side, it helps  understanding which market impact models are viable, because the analysis of order execution provides a test for the existence of undesirable properties of a model. In the first part of the talk, we present market impact models with transient price impact, modeling the resilience of electronic limit order books. In the second part of the talk, we consider the Almgren-Chriss market impact model and analyze the effects of risk aversion on optimal strategies by using stochastic control methods. In the final part, we discuss effects that occur in a multi-player equilibrium.

Theo Kypraios (Nottingham)
A novel class of semi-parametric time series models: Construction and Bayesian Inference
Abstract
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In this talk a novel class of semi-parametric time series models will be presented, for which we can specify in advance the marginal distribution of the observations and then build the dependence structure of the observations around them by introducing an underlying stochastic process termed as 'latent branching tree'. It will be demonstrated how can we draw Bayesian inference for the model parameters using Markov Chain Monte Carlo methods as well as Approximate Bayesian Computation methodology. Finally a real dataset on genome scheme data will be fitted to these models and we will also discuss how this kind of models can be used in modelling Internet traffic.

Vincent Macaulay, Dept of Statistics, University of Glasgow
Inference of migration episodes from modern DNA sequence variation
One view of human prehistory is of a set of punctuated migration events across space and time, associated with settlement, resettlement and discrete phases of immigration. It is pertinent to ask whether the variability that exists in the DNA sequences of samples of people living now, something which can be relatively easily measured, can be used to fit and test such models. Population genetics theory already makes predictions of patterns of genetic variation under certain very simple models of prehistoric demography. In this presentation I will describe an alternative, but still quite simple, model designed to capture more aspects of human prehistory of interest to the archaeologist, show how it can be rephrased as a mixture model, and illustrate the kinds of inferences that can be made on a real data set, taking a Bayesian approach.