DH 1st floor SR

We discuss sparse portfolio optimization in continuous time.

Optimization objective is to maximize an expected utility as in the

classical Merton problem but with regularizing sparsity constraints.

Such constraints aim for asset allocations that contain only few assets or

that deviate only in few coordinates from a reference benchmark allocation.

With a focus on growth optimization, we show empirical results for various

portfolio selection strategies with and without sparsity constraints,

investigating different portfolios of stock indicies, several performance

measures and adaptive methods to select the regularization parameter.

Sparse optimal portfolios are less sensitive to estimation

errors and performance is superior to portfolios without sparsity

constraints in reality, where estimation risk and model uncertainty must

not be ignored.

We study portfolio optimization and contingent claim valuation in markets where illiquidity may affect the transfer of wealth over time and between investment classes. In addition to classical frictionless markets and markets with transaction costs, our model covers nonlinear illiquidity effects that arise in limit order markets. We extend basic results on arbitrage bounds, attainable claims and optimal portfolios to illiquid markets and general swap contracts where both claims and premiums may have multiple payout dates. We establish the existence of optimal trading strategies and the lower semicontinuity of the optimal value of portfolio optimization under conditions that extend the no-arbitrage condition in the classical linear market model.

A multi-dimensional extension of the structural default model with firms' values driven by diffusion processes with Marshall-Olkin-inspired

correlation structure is presented. Semi-analytical methods for solving

the forward calibration problem and backward pricing problem in three

dimensions are developed. The model is used to analyze bilateral counter- party risk for credit default swaps and evaluate the corresponding credit and debt value adjustments.

The models of Brownian motion, Poisson processes, Levy processes and martingales are frequently used as basic formulations of prices in financial market. But probability and/or distribution uncertainties cause serious problems of robustness. Nonlinear expectations (G-Expectations) and the corresponding martingales are useful tools to solve them.

In vertical annular two-phase flow, large amplitude waves ("disturbance waves") are the most significant means by which the liquid is transported by the action of the gas phase. The presentation is of certain experimental results with the intention of defining a conceptual model suitable for possible mathematical interpretation.

These large waves have been studied for over 50 years but there has been little corresponding advance in the mathematical understanding of the phenomenon.

The aim of the workshop is to discuss what analysis might be possible and how this might contribute to the understanding of the phenomena involved.

We wish to discuss the role of Modelling in Health Care. While risk factor prevalences vary and change with time it is difficult to anticipate the change in disease incidence that will result without accurately modelling the epidemiology. When detailed study of the prevalence of obesity, tobacco and salt intake, for example, are studied clear patterns emerge that can be extrapolated into the future. These can give rise to estimated probability distributions of these risk factors across age, sex, ethnicity, social class groups etc into the future. Micro simulation of individuals from defined populations (eg England 2012) can then estimate disease incidence, prevalence, death, costs and quality of life. Thus future health and other needs can be estimated, and interventions on these risk factors can be simulated for their population effect. Health policy can be better determined by a realistic characterisation of public health. The Foresight microsimulation modelling of the National Heart Forum (UK Health Forum) will be described. We will emphasise some of the mathematical and statistical issues associated with so doing.

I will present the basics of mathematical finance, and what probabilists do there. More specifically, I will present the basic concepts of replication of a derivative contract by trading, market completeness, arbitrage, and the link with Backward Stochastic Differential Equations (BSDEs).

PLEASE NOTE EARLY START TIME TO AVOID CLASH WITH OCCAM GROUP MEETING

The human influenza A virus causes three to five million cases of severe illness and about 250 000 to 500 000 deaths each year. The 1918 Spanish Flu may have killed more than 40 million people. Yet, the underlying cause of the seasonality of the human influenza virus, its preferential transmission in winter in temperate climates, remains controversial. One of the major forms of the human influenza virus is a sphere made up of lipids selectively derived from the host cell along with specialized viral proteins. I have employed molecular dynamics simulations to study the biophysical properties of a single transmissible unit--an approximately spherical influenza A virion in water (i.e., to mimic the water droplets present in normal transmission of the virus). The surface area per lipid can't be calculated as a ratio of the surface area of the sphere to the number of lipids present as there are many different species of lipid for which different surface area values should be calculated. The 'mosaic' of lipid surface areas may be regarded quantitatively as a Voronoi diagram, but construction of a true spherical Voronoi tessellation is more challenging than the well-established methods for planar Voronoi diagrams. I describe my attempt to implement an approach to the spherical Voronoi problem (based on: Hyeon-Suk Na, Chung-Nim Lee, Otfried Cheong. Computational Geometry 23 (2002) 183–194) and the challenges that remain in the implementation of this algorithm.

Our starting point is a recent characterisation of one-dimensional, time-homogeneous diffusion in terms of its distribution at an exponential time. The structure of this characterisation leads naturally to the idea of measuring `how far' a diffusion is away from being a martingale diffusion in terms of expected local time at the starting point. This work in progress has a connection to finance and to

a Skorokhod embedding.

A number of pricing models for electricity and carbon credit pricing involve nonlinear dependencies between two, or more, of the processes involved; for example, the models developed by Schwarz and Howison. The consequences of these nonlinearities are not well understood.

In this talk I will discuss some much simpler models, namely options whose values are defined self-referentially, which have been looked at in order to better understand the effects of these non-linear dependencies.