Dr Patrick McSharry
Patrick McSharry heads the Smith School’s Catastrophe Risk Financing research area.
His expertise is in the area of time series modelling, machine learning, signal processing, and systems analysis. He has substantial experience in constructing quantitative models for forecasting, classification, decision-making, and risk management.
This is achieved using a range of modelling approaches and combines techniques from mathematics, statistics, physics and engineering. While working with practitioners in the private and public sector, he has developed techniques to account for uncertainty in the modelling process and provide decision-making based on probabilistic scenarios.
Dr McSharry is currently investigating the quantification and financing of risk associated with natural catastrophes. The challenge is to find appropriate mechanisms to share catastrophe risk equitably and sustainably across society.
Public-private partnerships such as the Turkish Catastrophe Insurance Pool could offer a means of providing insurance and sharing risk. Another important development is the Global Earthquake Model (GEM) which was initiated by the OECD.
Dr McSharry is working on the Socio-Econonic Impact module of GEM which aims to become the independent standard to calculate and communicate earthquake risk worldwide. He will investigate how to communicate the direct economic benefits gained from developing mechanisms to protect against natural catastrophes. This will also involve promoting a multi-disciplinary approach to catastrophe risk financing through the integration of science, finance, and insurance.
He is working on the use of quantitative models for decision-making and is particularly interested in the communication of forecasts to policy-makers and the advantages of probabilistic forecasts.
Dr McSharry is the Senior Academic participating in the Willis Research Network, which represents the largest partnership between re-insurance and academia. He will manage the Willis Research Fellow at Oxford University who will be appointed in Spring.
He is also the Oxford primary investigator on an EU-funded consortium of twenty international partners, entitled SafeWind, which is investigating probabilistic forecasting of wind power generation.
Dr. McSharry received a BA in Theoretical Physics and an MSc in Electronic and Electrical Engineering from Trinity College Dublin and received a DPhil in Mathematics from the University of Oxford. He joined the Smith School in November 2009, moving from the Said Business School.
Recent papers on forecasting and risk:
S. Arora, M.A. Little and P.E. McSharry (2012). Nonlinear and Nonparametric Modeling Approaches for Probabilistic Forecasting of the US Gross National Product.
P.E. McSharry (2012). Stream Analytics for Forecasting.
D. Orrell and P. E. McSharry (2009). System economics: overcoming the pitfalls of forecasting models via a multidisciplinary approach. International Journal of Forecasting, 25(4): 734-743.
D. Orrell and P. E. McSharry (2009). Systems forecasting: rethinking the ways that we forecast. Foresight: The International Journal of Applied Forecasting, 14: 24-30.
P. Pinson, P. E. McSharry, and H. Madsen (2010, accepted). Reliability diagrams for density forecasts of continuous variables: accounting for serial correlation. Quarterly Journal of the Royal Meteorological Society.
J. Rodda, M. A. Little, H. J. E. Rodda, and P. E. McSharry (2009, in press). A comparative study of the magnitude, frequency and distribution of intense rainfall in the United Kingdom. International Journal of Climatology.
H. J. E. Rodda, M. A. Little, R. G. Wood, N. MacDougall, and P. E. McSharry (2008). Extreme Rainfalls in the British Isles over the Period 1866-1968. Weather 64(3), 71-75.
M. A. Little, H. J. E. Rodda, and P. E. McSharry (2008). Bayesian objective classification of extreme UK daily rainfall for flood risk applications. Hydrol. Earth Syst. Sci. Discuss., 5, 3033-3060.
M. A. Little, P. E. McSharry, and J. W. Taylor (2008). Generalised Linear Models for Site-Specific Density Forecasting of UK Daily Rainfall. Monthly Weather Review, 137, 1031-1047.
J. W. Taylor, P. E. McSharry, and R. Buizza (2009). Wind power density forecasting using ensemble predictions and time series models. IEEE Trans Power Conversion, 24(3): 775-782.
P. E. McSharry, P. Pinson, and R. Gerard (2009). Methodology for the evaluation of probabilistic forecasts. SafeWind Report.