Impact case study database

The Basel III framework is an internationally agreed set of measures intended to strengthen the regulation, supervision, and risk management of banks, in response to weaknesses exposed by the financial crisis of 2007-2009. It requires banks and other financial institutions to apply a backtesting procedure to various market risk factors, trade, and portfolio prices.

The challenges of managing counterparty credit risk: one of the requirements of Basel III is enhanced management of counterparty credit risk (CCR) [A]. This makes CCR backtesting a mandatory requirement for all banks with advanced model approval for CCR, including Barclays. Basel III also requires investment banks to hold adequate capital and liquidity to cover the CCR, which is the potential loss in derivative positions due to the default of trading counterparties. The reason for this strong emphasis on banks’ proper management of CCR is that it is important to the stability not only of individual banks but also, in light of the interconnections between them, to the financial system as a whole. The failures of Lehman Brothers and MF Global, and their impacts on the global financial system, illustrate the disruptive potential of instability in any part of the system.

However, CCR is a complex risk to assess; as a hybrid of credit and market risk, it is contingent both on changes in the counterparty’s creditworthiness and on movements in underlying market risk factors. The first step in computing CCR capital is typically to jointly simulate various future market risk factors such as interest rates, equities, and foreign exchange rates. Next, all the derivative positions of the bank are computed at each time horizon of each of these simulated market scenarios, to determine the bank’s potential future exposure (PFE) to counterparty default. The amount of holding capital required to cover CCR is then calculated, based on PFE and according to the relevant regulation.

The role of backtesting: backtesting is a critical component of the Basel III regulation, particularly as a means of assessing the extreme PFE quantiles used by banks and financial institutions to validate price models and to calculate their credit holding. The ad hoc approaches typically taken to calculating these extreme quantiles are inaccurate. Underestimating the extreme quantiles leads to the exposure of both banks and their customers to potential uncovered financial losses. Conservative estimation leads to additional unnecessary overheads and, consequently, to increases in the costs of borrowing and decreases in investment.

Implementing the new backtesting method at Barclays: Barclays work with Yao on the development of the new method for estimating extreme quantiles published in [1] has changed and improved important aspects of their approach to backtesting. Specifically, the new method has helped Barclays to:

“overcome the difficult technical challenges associated with backtesting uncollateralised portfolio…This allows Barclays to backtest the potential future exposure to counterparty default based on a theoretically sound method for the very first time.” [B]

That new method “ has been used as the official production method to backtest Barclays’ CCR exposure RWA [risk weighted assets] since November 2013” [B, further confirmed in C]. As a result, it is now “ one of the key components for backtesting our large CCR RWA for uncollateralised portfolio day-to-day” [B, further confirmed in C].

Reducing the cost of borrowing by more accurately calculating an appropriate capital buffer: Barclays holds CCR RWA worth tens of billions of US dollars (commercial sensitivity prevents Barclays from releasing the exact figure). The new methodology is applied across that portfolio, allowing the bank to calculate an adequate capital buffer to protect both its own and its customers’ interests by avoiding the need to hold excessively large additional capital. The direct saving from this new scientifically calculated buffer is substantial. This, in turn, also reduces the cost of borrowing, and potentially increases investment and economic growth, with substantial indirect benefits to society.

Helping Barclays to meet regulatory requirements and improve its CCR management: since its first use by Barclays in late 2013, the new method has withstood rigorous backtesting under the Basel III framework. The Managing Director and Head of Cross Product Modelling of Barclays explains:

“The new methodology has helped Barclays to meet [the] regulator’s requirements and better manage and control our counterparty risk…[because it] has helped Barclays to demonstrate quantitatively that we hold an adequate amount of capital to manage our counterparty credit risk.” [B]

This has helped to ensure that both Barclays and its customers have avoided exposure to highly risky positions. Had its backtesting failed, the bank would have been required to re-adjust its CCR RWA evaluation by increasing its estimates of CCR exposure. That increase would, in turn, demand that they hold additional capital add-ons, a requirement that would be extremely costly given the large size of the bank’s counterparty RWA. Commercial confidentiality also prevents Barclays from releasing its backtesting methodology documentation or related quantitative backtesting results. However, the bank’s Head of Cross Product Modelling writes:

“I am happy to provide this letter to recognise Professor Yao’s contribution to our CCR backtesting methodology, which has helped Barclays to improve our Counterparty Credit Risk management.” [B].

Wider benefits of improved CCR management: the proper management of exposure to CCR based on the new method underpinned by [1] delivers several important benefits.

1. First, it improves the overall stability of Barclays in the sense that both its customers’ and the bank’s own interests are protected by alleviating exposure to uncovered high risky positions within a small probability (such as 0.05% or 0.01%).

2. Secondly, it contributes to the stability of the global financial system as a whole by mitigating the potential impact of the failure of one bank on others. The collapse of Lehman Brothers in 2008 underscored the need for better protections of this sort within a highly interconnected global financial system.

3. Thirdly, it increases the bank’s confidence in lending via a reduction of the cost of borrowing. This results from the fact that calculating the extreme PFE based on the new method helps the bank more accurately estimate an adequate capital buffer. In other words, the method allows banks to cover a given degree of risk with a smaller capital requirement. Given that Barclays holds CCR RWA worth tens of billions of US dollars, the direct saving from this new and scientifically calculated buffer is substantial. This substantial saving improves the bank’s business and operations and encourages more investment.

Yao’s innovative contribution in estimating extreme PFE quantiles is an indispensable part of the new backtesting method directly supporting these benefits. As such, the research has contributed indirectly to assuring greater financial system security at lower cost, with wider economic benefits.

The Pharmaceutical Services Negotiating Committee (PSNC) promotes and supports the interests of NHS community pharmacies in England. The results of the Margin Survey have major financial implications for such pharmacies. Accordingly, the PSNC takes a keen interest in its methodology and the DHSC seeks to ensure agreement with the PSNC regarding any changes to this. Skinner met with DHSC and PSNC members roughly twice a year to share and discuss reports of the LSE research. These inputs guided both the agenda for the LSE research and the implementation in the Margin Survey of methods developed in that research.

Impacts on the methodology of the Margin Survey

The main impacts of the research on the methodology of the Margin Survey have been: (a) more reliable estimators of accuracy (standard errors); and (b) improved accuracy. More specifically:

1. New estimators of standard errors were introduced in 2016. These include methods for sub-annual estimates as well as for annual estimates, addressing the National Audit Office (NAO) recommendation about providing more timely results.

2. Research on how to make estimation more robust to outlying observations (in line with the NAO recommendations) led to new standard error-based rules for identifying “influential observations”; these were implemented in 2019. New Winsorization methods were also proposed and are currently under consideration.

3. Research into the dependence of the standard error on stratum sample sizes for both the drug and pharmacy samples supported a decision to increase the sample sizes for branded and generic drugs. This decision reflected research evidence showing that a greater effect could be achieved on reducing the standard error of the margin estimator by increasing the drug sample than by increasing the pharmacy sample.

4. Research into the dependence of the standard error on rotation of the drug sample led to the rotation period being shortened from six to three months in 2018/19. This led to an estimated reduction of about 15% in the standard error of the margin estimator.

Skinner’s role in this was the provision of expert - and, crucially, impartial - advice to both the DHSC and the PSNC as they negotiated an agreement on the optimal margin size. The changes outlined here had resourcing implications for both parties, who used the research insights provided by Skinner to support their productive collaboration on improvements to the Margin Survey. As a Public Health Analyst at the DHSC explains:

“The Margin Survey allows the DHSC to adjust reimbursements to pharmacies as necessary to make sure that they remain properly incentivised and fairly reimbursed, while protecting the NHS from overspending on the reimbursement of particular medicines…We sought to improve the accuracy of the model wherever possible and [Skinner] was able to provide impartial advice to both the DHSC and the PSNC on ways to achieve this. Changes to the sample size and its frequency, supported by [Skinner’s] research, helped improve the survey’s accuracy and confidence in its results.” [A]

Subsequent impacts on adjustments of reimbursement prices in the Drug Tariff

The changes in the Margin Survey methodology have, in turn, supported adjustments of the reimbursement prices in the Drug Tariff. These adjustments have been achieved because the new methods allow: (a) reduced deviations between actual and target margins; and (b) reduced variability in adjustments over time.

The scale of adjustments has significantly reduced from the NAO’s estimated deviations of hundreds of millions of pounds per annum in 2005-2009 (for the scale of this previous deviation, see **[B]**). Deviations between the actual and target margins reflect unpredictable market changes, as well as Margin Survey estimation error, and it is difficult to quantify the relative contributions of these two factors. However, based on consideration of the impacts on Margin Survey standard errors, the order of improved accuracy relative to the target margin, as well as of reductions in variation in adjustments, is in the tens of millions of GBP per annum.

Benefits to the NHS

In recent years the NHS has faced considerable cost pressures, including in relation to expenditure on medicines. By reducing errors in the Margin Survey estimation, Skinner’s research has helped ensure that the NHS is better able to recoup overspends on prescription drugs, supporting significant savings by reducing these sorts of losses. It is able to make this contribution because the adjustment of reimbursement prices for medicines is made only when evidence can be provided that the target margin has been missed. If that cannot be proven, the Drug Tariff cannot be adjusted and it is impossible for any overspend to be recouped. Inaccuracies in the estimated margin can therefore lead to a situation in which the NHS is unable to recoup overspends, simply because it cannot prove that the target margin has been missed.

For illustration, if the true annual margin is GBP860 million, the estimated margin is GBP850 million, and the standard error is GBP30 million, an approximate 95% confidence interval is GBP790-910 million. It may then be concluded that there is no statistical evidence that the target of GBP800 million has not been met and so the Drug Tariff does not need changing to recoup overspend. However, with a standard error of GBP20 million and a confidence interval of GBP810-890 million, it may be decided that there is an overspend of around GBP50 million (= GBP800-850 million) to be recouped.

These sorts of savings are desirable, not primarily for their own sake, but because greater efficiency in the use of NHS resources increases the amount of healthcare that can be financed from a given total NHS budget.

Benefits to community pharmacies and primary care networks

Community pharmacies have also faced severe cash flow challenges, with smaller independent pharmacies being especially vulnerable to “income variation and unpredictability” (see, for example, **[D]**). In October 2018, the PSNC noted that pharmacy reimbursement prices:

“will reduce by GBP10 million per month from November 2018 for the next five months (until March 2019). This is to repay excess margin earned by pharmacies in previous years, in particular 2015/16 for which the results of the Margin Survey show that there was a significant over-delivery of margin.” [E]

Against this backdrop, the PSNC expressed to HM Government its “ deep concerns about the financial pressures facing community pharmacy contractors and the fact that they would increasingly be unable to reinvest, given pressures from rising staff costs and business rates” [E]. The impact of such cost pressures can be a reduction of services or, ultimately, closure - there were 134 net closures of “bricks and mortar” pharmacies between November 2016 and April 2018 [F]. In the broader context of plans to cut their NHS funding by GBP170 million, the then-Health Minister Alistair Burt estimated in January 2016 that between 1,000 and 3,000 pharmacies could face closure ( BBC, 27 January 2016).

The research has helped ensure that community pharmacies receive a more stable and predictable funding stream from dispensing medicines. This results from the use of research findings to ensure that reimbursement price adjustments are smoother and made with accurate evidence from the Margin Survey [A]. In July 2019, the PSNC, NHS England and NHS Improvement, and the DHSC agreed a five-year deal for community pharmacies, guaranteeing funding levels until 2023/24. The deal secures pharmacy funding and sets out a clear vision for the expansion of clinical service delivery over the next five years, in line with the NHS Long Term Plan. Part of the agreement includes a commitment to:

“*working on a range of reforms to reimbursement arrangements to deliver smoother cash flow and fairer distribution of medicines margin and better value for money for the NHS.*” ( [G], p. 5)

The LSE research contributes to this commitment.

Benefits to patients

In May 2018, the CEO of the PSNC expressed concern about the effects of cost pressures on community pharmacies. In addition to closures, he noted that cuts meant that:

“[r]ather than investing in developments that could improve patient care and allow them to offer more services, contractors are having to consider reducing staff, opening hours and unpaid services, such as home delivery.” [F]

These sorts of changes all have potentially negative implications for patients, especially the vulnerable and those in areas of high deprivation where pharmacies have traditionally been able to help reduce health inequalities [F]. By helping to ensure that pharmacies have a more stable and predictable stream of income from prescription medicines, the ultimate impact of the research is therefore a contribution to increased NHS finance in areas supporting improved health and individual wellbeing.

Political polling plays an important role in shaping party strategies, media presentation of election campaigns, and public perceptions of the political landscape. The information it provides therefore has the potential to influence democratic processes. Because election polls are highly visible, their quality and accuracy can also have a major effect on the reputation of, and public confidence in, surveys and public opinion research more widely. The work outlined above has delivered direct impacts in several areas relevant to this.

Changing the rules and regulations of political polling in the UK: members of the British Polling Council (BPC) include all the major polling companies who carry out regular election polls in the UK. The work of the Inquiry panel and the resulting report [1] led directly to changes in BPC rules about the ways in which political polling is conducted and reported. In June 2016, the Council confirmed its agreement to immediately implement rule changes corresponding to the reporting and transparency Recommendations 7-9 of the report [1] (but postponed a response to Recommendation 10, on pre-registration of polls) [A]. In May 2018, it also responded to Recommendation 11 by introducing a new requirement for its members to publish a statement about the level of uncertainty in poll estimates of parties’ vote shares [B]. Responding to the methodological Recommendations (1-5), the BPC further advised that it was “ for individual member companies to decide how best to take these forward”. Some of the ways in which member companies did this are discussed below (see “Impacts on industry practice”).

The work has also had impacts on the regulation of polling in the UK via its use in public policy debate on the subject. Impacts here derive primarily from its influence on the conclusions and recommendations of the House of Lords (HoL) Select Committee on Political Polling and Digital Media, appointed on 29 June 2017 to consider the effects of political polling and digital media on politics. The Committee’s final report was published on 17 April 2018 [D]; the government’s response to it was received on 15 June 2018 [E]. Sturgis served as Specialist Advisor to the Committee and five other members of the Inquiry panel - including Kuha - gave evidence or provided briefings to it. The Committee’s report [D] refers extensively to [1], which helped set the parameters for its own inquiry. Thus, for example, the Committee explains that, since it had been “ comprehensively covered” in [1], “ We have not...attempted to replicate this work by delving in detail into the methodological causes of polling errors” [D*, para 13, p. 11 ] . The Chair of the Committee, Lord Lipsey, reported in a 2019 interview with the University of Southampton that: “ the BPC inquiry was one of the key cornerstones of our work constructing the Lords inquiry and in informing our view of the present polling outlook” [G, p.30 ].

The Select Committee’s recommendations on the regulation of the polling industry included a substantially expanded oversight and advisory role for the BPC. It did not, however, propose government regulation of political polling or banning polls close to elections, both of which it had considered. The spirit of these recommendations was echoed in the government response: “ Polling standards should remain self-regulated by the industry [… who…] already have a strong incentive to update and improve their techniques, especially if flaws are uncovered” [E, p. 1 ].

Impacts on industry practice: a second major area of impact - resulting largely from these effects on the rules and recommendations governing polling in the UK - has been changes in methodology and procedures within the polling industry . These have had direct effects on decisions and operations within, and outcomes for, individual polling companies. According to an associate director at Opinium, the principal value of the BPC Inquiry after the 2015 election was that it: “… was able to point quite definitively to a system-wide problem, larger than just an issue which might have only been affecting our Consumer Panel or other things which would have been unique to one company” [G, p. 37 ]. This more rigorous, system-wide understanding underpinned by [1] helped public opinion research companies to identify and address problems in their methods. In a BPC report published in 2017 [C], nine BPC-member companies summarised changes to their polling procedures in response to [1]. These particularly included changes to sampling, weighting, and turnout adjustment procedures.

The influence of the panel’s work on industry practice is also apparent in evidence provided to the HoL Select Committee. Joint evidence submitted by eight of the companies explains:

“The Joint Inquiry led by Professor Patrick Sturgis into political polling…after the 2015 General Election identified that the main cause of polling error was unrepresentative samples. As such, one of the most pressing issues for the industry to tackle has been to improve the quality of their sample, an undertaking pursued across the membership of the British Polling Council.” [F, PPD0014, p. 154 ]

Similarly, YouGov made explicit reference to its own work to address the key findings of the Inquiry, including a substantial investment in efforts to improve recruitment to its panel of respondents [F, PPD0016, p. 549 ]. Its Director of Political and Social Research confirmed that “ the [changes] that came out directly from the report findings were…the introduction of quota and weighting by political interest and the introduction of education weights” [G, p. 12 ].

The polling Inquiry has also had impact beyond the UK. A comparable inquiry was recently conducted along similar lines in Australia to examine the performance of the polls before the federal election there in 2019 [H]. According to the Chair of that inquiry panel: “ Not only was the Sturgis et al. review heavily cited, it was also tremendously influential in helping to frame the Australian inquiry and guide some our analysis” [I].

Benefits to the polling industry

Improving polling accuracy: the polling industry’s efforts appeared to bear fruit in the polls for the General Election in December 2019, for which the companies further refined their methodologies within the framework outlined in the Inquiry report [J]. The polls correctly predicted the general result of the election, and the BPC described their predicted vote shares as “ more accurate … than in any contest since 2005” [K].

Commercial benefits: the changes in industry practice have had important knock-on effects on the success of individual companies - and, thereby, on the industry’s contribution to the UK economy. Market research and public opinion research are big business in the UK. According to MRS, the UK’s research market is “ second only to the United States”, employing up to 73,000 people and generating GBP4.8 billion in annual gross value added. Election polling represents only a small part of this business, but one which is important because of its unique visibility and accountability. This makes it “ a ‘shop front’ for polling organisations - an activity aimed at increasing their public profiles and advertising their accuracy” [D, Appendix 5, p. 99 ]. The reputation of that “shop front” was badly damaged in 2015. As well as improving the reliability of polling methods, evidence submitted to the Select Committee acknowledged the impacts of [1] on transparency in the industry. Providing evidence for the BPC, its President Professor Sir John Curtice credits [1] with influencing the fact that: “ …if any company changes in any way the way in which it has collected or estimated its voting intention data during an election or referendum campaign, it has to make that public” [J, QQ 139–147, p. 89 ]. These changes have already begun to improve the polling industry’s reputation among the media and other stakeholders. According to Opinium, [1] itself was “ extremely valuable in that it showed that we were being transparent and were committed to resolving the various issues”. Public engagement work by the Inquiry panel was also “ extremely helpful” in “rehabilitating a bit of the industry's image after 2015” [G, pp. 37-38 ] . By providing actionable (and now actioned) recommendations to improve polling, the Inquiry has contributed to restoring confidence in this highly visible facet of the UK’s “business of evidence” industry.

Benefits to users of polling data: beyond the commercial benefits for pollsters and their industry, changes in polling practices have also delivered important benefits to those who use polling data. Key users and consumers of political polls include press and online media covering elections, political parties running in those elections, and voters themselves.

It is generally agreed that pre-election polling helps shape media coverage and therefore the “narrative” of elections; in turn, this may be seen to affect public perceptions of an election and decision-making by political parties. This view was echoed in evidence given to the HoL Select Committee by the Director of Editorial Policy and Standards at the BBC: “ Our concern about the 2015 and 2017 general elections and the Scottish and EU referendums was the capacity of the polls to influence the journalistic narrative of those election campaigns” [D, para. 82, p. 27 ]. Certainly, it is well known that political parties take an interest in the results of more prominent newspaper polls, as well as using private polling to inform decision-making, and the HoL Select Committee heard evidence that this can influence the strategic approach and decision-making of those parties [D, para. 83-91, pp. 27-29 ].

The report [1] has improved understanding among journalists about the reporting of polls. A representative of the BPC explained that it provided “ a very useful tool for the media to help understand exactly what had gone wrong [in 2015]…It made it easier for them to in turn explain to their readers [and] viewers what had happened” [G, p. 26 ]. The report also underpinned the development of new media recommendations about the use of polling data, now published by both BPC and MRS [L]. According to the Director of Deltapoll:

“…following the Inquiry the British Polling Council hardened its recommendation to journalists on how they should use and treat polling. I think lots of people now don’t look at individual polls to the extent that they did, but view them as part of a series and a trend and look at the general patterns rather than individuals, which obviously implies outliers probably get less recognition than they used to.” [G, p. 35 ]

Finally, political polls are one of the most prominent forms of information available to the voting public. One cannot make any very strong claims about whether and how polling may affect voting, as the empirical evidence on this question is limited. Whatever such effects there may be, however, it is clearly preferable that decision-making is based on accurate information. As the HoL Select Committee concluded:

“…voting intention polls play a hugely significant role in shaping the narrative around political events such as elections and referendums. Given the impact that they can have on political discourse, they will inevitably influence public behaviour and opinions, even if only indirectly. It is therefore vital that work continues in order to try to improve polling accuracy and that this is done as transparently as possible.” [D, para. 92, p. 29 ].

The work of the polling Inquiry has contributed to the achievement of this goal. By helping to improve the quality of polling, it has improved the accuracy of the information available to those who use voting data, helping them to develop better-informed campaign strategies and voting choices, and to present and receive fairer and more accurate election coverage.

Veraart has worked regularly with the Bank of England (BoE) since 2015. From October to December 2016, she served as a BoE George Fellow, in which capacity she was based full-time in the Stress Testing Strategy Division of the Financial Stability Strategy and Risk directorate [A]. The primary impact of the research outlined above is its use by the BoE to conduct stress testing in 2016 and 2017. Its use in this context means that the research has contributed to the fulfilment of one of the BoE’s key objectives: to protect and enhance the stability of the UK financial system.

Helping meet statutory objectives

The main aim of the BoE’s stress testing framework is to help the Financial Policy Committee (FPC) and the Prudential Regulation Authority (PRA) to meet their statutory objectives. The FPC’s primary objective is to contribute to the BoE’s financial stability objective to protect and enhance the stability of the UK financial system. It is also a general objective of the PRA to promote the safety and soundness of the banks it regulates. Stress testing, which is used to analyse the resilience of an object or system under extreme (adverse) conditions, supports both of these objectives. Stress tests are used both to measure risks and to manage risk by setting prudential policy. Their use in finance has significantly increased since the 2007-2009 Global Financial Crisis, which showed the fault lines of existing risk management practice. Following recommendations made by the Basel Committee on Banking Supervision (the primary global standard-setter for the prudential regulation of banks), regulators around the world have developed and implemented new stress testing frameworks to both measure and manage risk in financial markets. The Basel Committee notes that: “Stress testing is now a critical element of risk management for banks and a core tool for banking supervisors and macroprudential authorities” [B].

New tools for UK stress testing

In the United Kingdom, the BoE has conducted an annual stress test since 2014. The main purpose of this is to “provide a quantitative, forward-looking assessment of the capital adequacy of the UK banking system and individual institutions within it” [C, p. 7 ]. The first part of a stress test is to design a stress scenario. Each year since 2016, the BoE has considered a scenario whose severity reflects policymakers’ assessment of the current risk environment. This so-called “annual cyclical scenario” (ACS) is used to test the resilience of the UK banking system to factors such as deep simultaneous recessions in the UK and global economies and financial market stresses. The ACS is counter-cyclical, meaning it will be more severe during market conditions in which a large amount of risk has built up and less severe when these risks have realised or decreased. Additional scenarios are considered biannually.

The second part of the stress testing exercise is concerned with evaluating the impact of the stress scenario on banks’ balance sheets and, in particular, their capital positions. It is important that suitable models and methods are used to conduct this analysis, because the results of the stress testing exercise are used to set regulatory capital buffers and to determine whether banks need to improve their capital positions [D]. The LSE research described here has had impacts on this aspect of the BoE stress test.

In 2015, the BoE identified the modelling of system-wide dynamics and feedback mechanisms as a key priority for its stress testing framework. As a result, it particularly sought to develop tools facilitating the exploration of system-wide dynamics. The 2007-2009 crisis had demonstrated the vital importance of spillovers and feedback channels - both between financial institutions and between the financial sector and the real economy - to quantifying the likely impacts of financial stresses. The need to understand these channels made analysis of them, such as that of the interbank lending channel analysed in [1], an important element of stress tests.

Testing solvency contagion via interbank lending

In 2016, the BoE stress test included, for the first time, testing of solvency contagion via interbank lending [E, p.34 ]. The BoE’s solvency contagion model examines how deteriorating capital positions lead to revaluation of interbank debt claims, which can in turn further affect banks’ capital positions. The model used to conduct the solvency contagion test introduced in 2016 builds on the modelling framework set out in [1]. This is particularly apparent in its newly explicit inclusion of exogenous bankruptcy costs within the modelling framework. A subsequent BoE Staff Working Paper describing the development of the new solvency contagion model cites both [1] and [3] and acknowledges Veraart’s input [F]. The Executive Director of Financial Stability Strategy and Risk has subsequently further confirmed the importance of the underpinning research to the development of the BoE's stress testing:

“[Veraart’s] research has informed the Bank’s modelling and analysis, in particular on incorporating feedback and amplification mechanisms in the Bank of England annual cyclical scenario (ACS) stress test. Solvency contagion was the first amplification mechanism included in the Bank of England’s stress test in 2016. The methodology […] builds on research by Dr Veraart, in particular her work on financial networks (Rogers and Veraart (2013) **[1]**).” [A]

Understanding and incorporating feedback loops and amplification effects is paramount. In a 2017 speech, the BoE Executive Director for Financial Stability Strategy and Risk stated that it was these feedback loops that “helped to turn around USD300 billion of subprime mortgage-related losses into well over” USD2.5 trillion of potential write-downs in the global banking sector within one year [G, p.6 ].

The new model, which was also used as part of the 2017 stress test [H], helped to address two of the BoE’s key priorities: “ developing a genuinely macroprudential approach to identifying risks in the banking sector; and enhancing the Bank’s modelling capabilities as part of the concurrent stress tests of the banking system” [E].

Supporting stability during the Covid-19 pandemic

Veraart’s work on financial stability has continued to deliver further benefits for the BoE beyond these direct impacts on the 2016 and 2017 stress tests. In 2020, for example, she worked with economists at the BoE to explore liquidity stress in the repo market, looking at the wider financial system beyond the banking sector. As the BoE Executive Director of Financial Stability Strategy and Risk explains, this more recent work " has informed policymakers in the context of analysing the stress in financial markets observed during the Covid-19 pandemic" [A]. Veraart’s contribution to the BoE’s work on financial stability is further realised through her membership of the Academic Advisory Group to the One Bank Research Steering Committee, which oversees the direction of the BoE’s research.

Adams Prize

The development of new tools supporting financial stability was recognised in the aforementioned award to Veraart of the 2019 University of Cambridge Adams Prize. Professor Mihalis Dafermos, Chair of the Adams Prize Adjudicators, noted that:

“Dr Veraart has developed new tools and concepts relevant for the representation and analysis of financial stability and systemic risk in banking networks. Her work has had considerable visibility and impact, both within academia and outside” [I].