REF2021 Impact Case Studies
Improving election polling methodologies
Professor Jouni Kuha
Social Statistics research group
Email: j.kuha@lse.ac.uk
Summary of the impact:
High-quality political polling is a significant element in the good conduct of democratic politics. In the UK, public confidence in polling was badly damaged by the failure of the polls to correctly predict the outcome of the 2015 General Election. Professor Jouni Kuha was the only statistician appointed by the British Polling Council (BPC) and the Market Research Society (MRS) to a panel of Inquiry to investigate this poor polling performance. The panel’s findings led to changes in BPC and MRS rules and in the methodological procedures used by commercial polling companies. Its research also influenced the conclusions and recommendations of the House of Lords Select Committee on Political Polling and Digital Media. As a result, it has influenced regulation of UK political polling, polling methodology, media reporting of polls, and the reputation and commercial prospects of the polling industry. By providing a more robust tool to use in generating poll results, it has contributed to the provision to parties and voters of more accurate information, and thereby to better democratic governance.
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Improved Monitoring of the NHS Drugs Bill
Professor Chris Skinner
Social Statistics research group
Summary of the impact:
Research by Professor Chris Skinner on design and estimation methods for cross-classified sampling has led to reductions in sampling error for a UK Department of Health and Social Care survey of medicines pricing. The results of this survey are used to determine how much community pharmacies in England are reimbursed for medicines dispensed via NHS prescriptions. The reduction in sampling error resulting from Skinner’s work has helped to ensure that reimbursement price adjustments are smoother and based on more accurate evidence. In turn, this helps ensure both efficiency in NHS spending on prescription medicines and a more stable and predictable NHS funding stream for community pharmacies. Ultimately, the research has contributed to increasing NHS finance in areas supporting improved health and individual wellbeing.
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Counterparty credit risk management at Barclays: estimating extreme quantiles
Professor Qiwei Yao
Time Series and Statistical Learning group
Email: q.yao@lse.ac.uk
Summary of the impact:
An innovative method for estimating extreme quantiles of multiple random variables was developed at the ÐÓ°ÉÂÛ̳ in collaboration with investment bank Barclays. Its application to the management of the tens of billions of US-dollars’ worth of counterparty risk-weighted assets (RWA) held by Barclay’s has helped the bank meet the requirements of Basel III. Specifically, a new methodology for future counterparty RWA evaluation, in which the extreme quantile estimation method plays a key role, has enabled the bank to calculate an appropriate capital reserve to protect customers’ interests as well as its own business in an effective and efficient manner, avoiding holding excessively large additional capital. This reduces the cost of borrowing and contributes positively to investment and economic growth. Barclays’ new methodology for future counterpart RWA evaluation has withstood backtesting under the Basel III framework since its inception in November 2013.
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Other case studies
The Department of Mathematics also submitted an impact case study by Professor Luitgard Veraart - Improving Financial Stress Testing at the Bank of England.
REF2014 Impact Case Studies
Helping Barclays meet the new Basel III regulation rules
Professor Qiwei Yao
Time Series and Statistical Learning group
Email: q.yao@lse.ac.uk
Summary of the impact:
In response to the deficiencies in bank risk management revealed following the 2008 financial crisis, one of the mandated requirements under the Basel III regulatory framework is for banks to backtest the internal models they use to price their assets and to calculate how much capital they require should a counterparty default. Qiwei Yao worked with the Quantitative Analyst - Exposure team at Barclays Bank, which is responsible for constructing the Barclays Counterpart Credit Risk (CCR) backtesting methodology. They made use of several statistical methods from Yao’s research to construct the newly developed backtesting methodology which is now in operation at Barclays Bank. This puts the CCR assessment and management at Barclays in line with the Basel III regulatory capital framework.
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Improving weather forecasts to avoid disruption, damage and disaster
Professor Leonard Smith
Centre for the Analysis of Time Series (CATS)
Summary of the impact:
Research by Professor Leonard Smith and the ÐÓ°ÉÂÛ̳ Centre for the Analysis of Time Series (CATS) on forecasting in non-linear and often chaotic systems, with particular attention to weather, has led to advances in three areas: 1) national and international weather industry products and services that are built upon state-of-the-art research and knowledge, 2) dissemination of state-of-the-art practice in forecast production and verification to national, regional and local weather centres around the world, and 3) the introduction of, and new applications in, state-of-the-art forecasting methods in industries facing high uncertainty and risk, e.g. insurance and energy.
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Ensuring the best science-based predictions of climate change
Professor Leonard Smith
Centre for the Analysis of Time Series (CATS)
Summary of the impact:
As the realities of climate change have become more widely accepted over the last decade, decision makers have requested projections of future changes and impacts. Founded in 2002, the Centre for Analysis of Time Series (CATS) conducted research revealing how the limited fidelity of climate models reduces the relevance of cost-benefit style management in this context: actions based on ill-founded projections (including probabilistic projections) can lead to maladaptation and poor policy choice. CATS’ conclusions were noted in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) report and led in turn to the toning down of the UK Climate Projections 2009 and the 2012 UK Climate Change Risk Assessment. Members of the insurance sector, energy sector, national security agencies, scientific bodies and governments have modified their approaches to climate risk management as a direct result of understanding CATS’ research. Attempts to reinterpret climate model output and design computer experiments for more effective decision support have also resulted.
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