Research in Probability in Finance and Insurance
Our research in probability in finance and insurance covers diverse aspects in quantitative modelling in finance, insurance, and risk management. Current areas include robust models on option pricing; model-uncertainty in decision making; valuation financial derivatives with exotic features; equilibrium with market constraints and informational asymmetry; optimal trading with micro-structure noise; insurance securitisation; contagion in financial and insurance markets; modelling energy and commodity markets.
The current members of the Probability in Finance and Insurance group are Beatrice Acciaio, Pauline Barrieu, Erik Baurdoux, Luciano Campi, Umut Cetin, Angelos Dassios, Kostas Kardaras and Hao Xing.
Research in Social Statistics
Research in social statistics is concerned with the development of statistical methods that can be used across the social sciences. Statisticians play an essential role in all aspects of social inquiry, including: study design; measurement; data linkage; development of statistical models that account for the complex structure of social data; model selection and assessment.
Members of the Social Statistics group have interest in statistical methods in each of these areas and regularly collaborate with social scientists whose questions motivate new lines of methodological research. We have experience in a range of social science disciplines, including demography, education, epidemiology, psychology and sociology, and psychology.
The current members of the Social Statistics group are Wicher Bergsma, Sara Geneletti, Kostas Kalogeropoulos, Jouni Kuha, Irini Moustaki, Chris Skinner and Fiona Steele.
Research in Time Series and Statistical Learning
The Department's research in time series and statistical learning encompasses many aspects of these disciplines. We are keenly involved in both theoretical developments and practical applications. Current areas of interest include time series (including high-dimensional and non-stationary time series), data science and machine learning, networks (including dynamical networks), high-dimensional inference and dimension reduction, statistical methods for ranking data, spatio-temporal processes, functional data analysis, shape-constrained estimation, multiscale modelling and estimation and change-point detection.
The current members of the Time Series and Statistical Learning group are Matteo Barigozzi, Yining Chen, Piotr Fryzlewicz, Kostas Kalogeropoulos, Clifford Lam, Xinghao Qiao, Leonard Smith and Qiwei Yao.
The Centre for the Analysis of Time Series (CATS)
The centre aims to ddress the question of data analysis using both physical insight and the latest statistical methods; focus on non-linear analysis in situations of economic and physical interest, such as weather forecasting; promote awareness of limitations of non-linear analysis and the danger of blindly transferring well-known physics to simulation modelling; focus on end-to-end forecasting, taking account of current uncertainty about the state of the system, model inadequacy and finite computational power.
CATS is led by Director Leonard Smith, with Chair Henry Wynn and Co-Directors Pauline Barrieu, Roman Frigg and David Stainforth.