Approximating persistence times of endemic infections

The spread of infectious disease through a population may be modeled as a stochastic process (typically a continuous-time Markov chain). For infections which are able to persist in the long term (i.e. become endemic in the population), a random variable of interest is the time until eventual extinction of infection.  Programmes exist aimed at global […]

Adaptive Monte Carlo Methods for Stochastic Differential Equations

Capturing rare events is crucial for accurate risk assessment and its successful management. An example in finance is computing the probability of a large, but rare, loss from a financial portfolio. Approximating expectations involving such rare events is difficult because, when using Monte Carlo, many of the generated samples do not contribute to the final […]

Predictive performance modelling for financial investment strategies

The development of multi-asset quantitative strategies is crucial in the investment industry and requires the analysis of vast amounts of data to gain insight about performance. This project will investigate the use of statistical predictive models and performance measures for risk evaluation in financial investment strategies, aiming to provide increased levels of predictive robustness in […]

Predictive models in health and morbidity trends related to insurance

Over recent decades, medical interventions and advances have become important drivers of health and morbidity trends. In this project we will consider statistical predictive models for such trends, relating to particular major conditions such as heart disease and cancer. The principal aim will be to address the timely need to develop robust predictive models for […]

Quantitative Methods and Models with Applications in Finance and Actuarial Science: Valuation Techniques, Investment Strategies and Dependence Modeling

Various stakeholders in finance and insurance—such as regulators, investors and managers—rely on quantitative analysis in their decision-making processes. This research project employs quantitative models and methods from probability theory and statistics to tackle problems that are of practical relevance in these fields. Three topics are mainly concerned.  The first topic studies numerical techniques that are […]

Portfolio Design and Feature Extraction Methods in Fiat and Crypto Currency Portfolios

In this project you will explore development of multi-period optimal portfolio design and allocation methods for a range of currency markets both fiat and crypto currency markets. This will involve study of equal risk parity methods as compared to classical global minimum variance and mean-variance methods. It will require analysis of volatility dynamics for returns […]

Numerical Methods for Financial Market Models

Many existing models for the evolution of financial and economic variables such as interest rates, inflation and so forth have no known closed-form solution. In order to deal with such models, e.g. for pricing and risk management of financial derivatives, it is therefore of fundamental importance to design numerical methods that are highly accurate, fast […]

Machine learning methods for Risk and Insurance

In this project you will explore a series of machine learning methodological developments to facilitate new approaches to insurance product design and pricing. It will be focused on methodological developments that can be applied to a variety of insurance domains. The work will be primarily exploring aspects of kernel machines, boosting and ensemble methods, random […]

High Frequency Financial Trading and Limit Order Book Modelling

In this project you will explore development of marked Hawkes process models for intra day high frequency finance. It will involve calibration of such models to Limit order book data for equities and futures contracts. This will involve exploring different aspects of estimation and simulation of these self exciting marked point process models. Once such […]