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Research by Oxford econometricians provided the basis for innovative new methods for predicting periods of potential financial stress and providing protection for investors against extreme events. During periods of financial stress, equity funds tend to sharply lose value while volatility tends to increase. Adding some long volatility exposure to a standard equity portfolio can significantly improve the tail behaviour of a portfolio. However, it is expensive to continually hold volatility contracts due to the volatility risk premium. Researchers at Man Group have applied the Oxford research to create new strategies to protect against tail risk and these are incorporated in their Tail Protect fund launched in October 2009.
The focus of this impact is the effect of the Global Vector Autoregressive (GVAR) project on international organisations such as the International Monetary Fund (IMF) and the European Central Bank (ECB) as well as its use by commercial organisations such as the Economist Intelligence Unit and the Asian Development Bank. The impact has enhanced the tools used by the ECB for communicating its policies. It has also allowed the IMF to demonstrate the effects of oil prices. The Economist Intelligence Unit found it an effective framework for assessing a wide-range of scenarios. The Asian Development Bank uses the GVAR model for forecasting in Asia.
The European Central Bank (ECB) uses forecasting tools to make predictions about the Eurozone economy as a whole. The University of Leicester has worked with the ECB to modify its main forecasting tool to improve its ability to take effective and timely action to keep inflation low and minimise deflation.
Similarly, the United Nations prepares a submission to each G8 Summit which includes forecasting and policy analysis based on the economies of the 193 member states. The forecasting tool used to create this submission has also been created in collaboration with the University of Leicester. These modelling tools help the world's most powerful leaders - both political and financial - to make informed and timely decisions about issues affecting economic stability, global food security and international safety.
The Computational Optimization Group (COG) in the Department of Computing produced new models, algorithms, and approximations for supporting confident decision-making under uncertainty — when computational alternatives are scarce or unavailable. The impact of this research is exemplified by the following:
The School of Mathematics at Cardiff University has developed important statistical and mathematical models for forecasting consumer buying behaviour. Enhancements to classical models, inspired by extensively studying their statistical properties, have allowed us to exploit their vast potential to benefit the sales and marketing strategies of manufacturing and retail organisations. The research has been endorsed and applied by Nielsen, the #1 global market research organisation that provides services to clients in 100 countries. Nielsen has utilised the models to augment profits and retain their globally leading corporate position. This has led to a US$30 million investment and been used to benefit major consumer goods manufacturers such as Pepsi, Kraft, Unilever, Nestlé and Procter & Gamble. Therefore the impact claimed is financial. Moreover, impact is also measurable in terms of public engagement since the work has been disseminated at a wide range of national and international corporate events and conferences. Beneficiaries include Tesco, Sainsbury's, GlaxoSmithKline and Mindshare WW.
A generalized additive model (GAM) explores the extent to which a single output variable of a complex system in a noisy environment can be described by a sum of smooth functions of several input variables.
Bath research has substantially improved the estimation and formulation of GAMs and hence
This improved statistical infrastructure has resulted in improved data analysis by practitioners in fields such as natural resource management, energy load prediction, environmental impact assessment, climate policy, epidemiology, finance and economics. In REF impact terms, such changes in practice by practitioners leads ultimately to direct economic and societal benefits, health benefits and policy changes. Below, these impacts are illustrated via two specific examples: (1) use of the methods by the energy company EDF for electricity load forecasting and (2) their use in environmental management. The statistical methods are implemented in R via the software package mgcv, largely written at Bath. As a `recommended' R package mgcv has also contributed to the global growth of R, which currently has an estimated 1.2M business users worldwide [A].
Ocean circulation accounts for much of the energy that drives weather and climate systems; errors in the representation of the ocean circulation in computational models affect the validity of forecasts of the dynamics of the ocean and atmosphere on daily, seasonal and decadal time scales. Research undertaken by the University of Reading investigated systematic model errors that resulted from data assimilation schemes embedded in the key processes used to predict ocean circulation. The researchers developed a new bias correction technique for use in ocean data assimilation that alleviates these errors. This has led to significant improvements in the accuracy of the forecasts of ocean dynamics. The technique has been implemented by the Met Office and by the European Centre for Medium Range Weather Forecasting (ECMWF) in their forecasting systems, resulting in major improvements to the prediction of the weather and climate from oceanic and atmospheric models. The assimilation technique is also leading to better use of expensively acquired satellite and in-situ data and improving ocean and atmosphere forecasts used by shipping and civil aviation, energy providers, insurance companies, the agriculture and fishing communities, food suppliers and the general public. The impact of the correction procedure is also important for anticipating and mitigating hazardous weather conditions and the effects of long-term climate change.
A two-dimensional flood inundation model called LISFLOOD-FP, which was created by a team led by Professor Paul Bates at the University of Bristol, has served as a blueprint for the flood risk management industry in the UK and many other countries. The documentation and published research for the original model, developed in 1999, and the subsequent improvements made in over a decade of research, have been integrated into clones of LISFLOOD-FP that have been produced by numerous risk management consultancies. This has not only saved commercial code developers' time but also improved the predictive capability of models used in a multimillion pound global industry that affects tens of millions of people annually. Between 2008 and 2013, clones of LISFLOOD-FP have been used to: i) develop national flood risk products for countries around the world; ii) facilitate the pricing of flood re-insurance contracts in a number of territories worldwide; and iii) undertake numerous individual flood inundation mapping studies in the UK and overseas. In the UK alone, risk assessments from LISFLOOD-FP clones are used in the Environment Agency's Flood Map (accessed on average 300,000 times a month by 50,000 unique browsers), in every property legal search, in every planning application assessment and in the pricing of the majority of flood re-insurance contracts. This has led to more informed and, hence, better flood risk management. A shareware version of the code has been available on the University of Bristol website since December 2010. As of September 2013, the shareware had received over 312 unique downloads from 54 different countries.
Data assimilation is playing an ever increasing role in weather forecasting. Implementing four- dimensional variational data assimilation (4DVAR) is part of the long term strategy of the UK Met Office.
In this case study, an idealised 4DVAR scheme, developed by a team from the Universities of Surrey and Reading working with the UK Met Office, based on the integration of Hamiltonian dynamics and nonlinearity into data assimilation, has now been taken up by the Met Office. It is being used to evaluate options for improving operational 4DVAR. The simplicity of the scheme developed by this team has facilitated careful analyses of some generic problems with the operational model. The outcome includes direct impact on the environment and indirect impact on the economy, both through improvements in weather forecasting.
Garratt's research on methods for quantifying the uncertainty surrounding macroeconomic forecasts, uncertainty which arises from not knowing the true model of the economy and from having to use inaccurate data, has been applied by Central Banks and national statistical agencies in their forecasting exercises and their analysis of policy interventions. Notably, Norges Bank (the central bank of Norway) has developed a system called the System for Averaging Models, which they use when they make macroeconomic forecasts and when they predict the effects of possible monetary policy actions, which incorporates Garratt's results.
Garratt's research provides new methods to allow for uncertainty about the 'true' model by using combinations of different possible models, when making forecasts. His research provides new procedures to take `data uncertainty' into account, when forecasts have to be based on real-time data (that is, inaccurate data which is available to the policymaker when a forecast is produced but which is revised later on). Garratt's research quantifies the effect of this uncertainty on forecasts by constructing probability density functions. Central banks and statistical agencies have applied his findings when making forecasts and undertaking policy analysis. Garratt's research has been disseminated through refereed journal articles, conference presentations, consultancy work with policy makers, and presentations to policy makers, including an invited presentation to HM Treasury.