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This paper aims to analyse the recent changes to the Pillar 2 regulatory-prescribed methodologies to classify and calculate credit concentration risk. Focussing on the Prudential Regulation Authority’s (PRA) methodologies, the paper tests the susceptibility to bias of the Herfindahl–Hirscham Index (HHI). The empirical tests serve to assess the assumption that the regulatory classification of exposures within the geographical concentration is subject to potential misuse that would undermine the PRA’s objective of obtaining risk sensitivity and improved banking competition.

Using the credit exposure data from three global banks, the HHI methodology is applied to the portfolio of geographically classified exposures, replicating the regulatory exercise of reporting credit concentration risk under Pillar 2. In doing so, the validity of the aforementioned assumption is tested by simulating the PRA’s Pillar 2 regulatory submission exercise with different scenarios, under which the credit exposures are assigned to different geographical regions.

The paper empirically shows that changing the geographical mapping of the Eastern European EU member states can result in a substantial reduction of the Pillar 2 credit concentration risk capital add-on. These empirical findings hold only for the banks with large exposures to Eastern Europe and Central Asia. The paper reports no material impact for the well-diversified credit portfolios of global banks.

This paper reviews the PRA-prescribed methodologies and the Pillar 2 regulatory guidance for calculating the capital add-on for the single name, sector and geographical credit concentration risk. In doing so, this paper becomes the first to test the assumptions that the regulatory guidance around the geographical breakdown of credit exposures is subject to potential abuse because of the ambiguity of the regulations.

The use of risk proxies in internal models remains a popular modelling solution. However, there is some risk that a proxy may not constitute an adequate representation of the underlying asset in terms of capturing tail risk. Therefore, using empirical examples for the financial collateral haircut model, this paper aims to critically review available statistical tools for measuring the adequacy of capturing tail risk by proxies used in the internal risk models of banks. In doing so, this paper advises on the most appropriate solutions for validating risk proxies.

This paper reviews statistical tools used to validate if the equity index/fund benchmark are proxies that adequately represent tail risk in the returns on an individual asset (equity/fund). The following statistical tools for comparing return distributions of the proxies and the portfolio items are discussed: the two-sample Kolmogorov–Smirnov test, the spillover test and the Harrell’s C test.

Upon the empirical review of the available statistical tools, this paper suggests using the two-sample Kolmogorov–Smirnov test to validate the adequacy of capturing tail risk by the assigned proxy and the Harrell’s C test to capture the discriminatory power of the proxy-based collateral haircuts models. This paper also suggests a tool that compares the reactions of risk proxies to tail events to verify possible underestimation of risk in times of significant stress.

The current regulations require banks to prove that the modelled proxies are representative of the real price observations without underestimation of tail risk and asset price volatility. This paper shows how to validate proxy-based financial collateral haircuts models.

Focusing on delivering practical implications, the purpose of this paper is to show optimal ways of calculating risk weights for public sector entities (PSEs) under the standardised approach in credit risk. Focusing on the changing regulatory background, this paper aims to explain the proposed revisions to the standardised approach for credit risk. Where necessary, upon the review of the forthcoming standards, this paper attempts to indicate room for improvement for policymakers and flag areas of potential ambiguity for practitioners.

This paper discusses and analyses the revised standards for the standardised approach in credit risk with respect to the treatment of PSEs. This paper, analysing the current regulatory proposals, tests the hypothesis stating that the affected banks may experience higher or lower capital charges for credit risk depending on the following factors: Choosing the optimal risk weight calculation methodology; and choosing the optimal composition of the credit risk portfolio.

The paper advises on using sovereign ratings as a base of risk weight calculations and categorising eligible entities as sovereign exposures. Individual entity ratings are not readily available and the majority of PSEs remain unrated by the external agencies. The simplistic approach of using sovereign ratings results in a lower risk weighted capital than the approach of using individual entity ratings. The sovereign rating approach decreases the value of the original exposure by 77 per cent. Reliance on sovereign ratings outperforms the optimal solution proposed in this paper. Categorisation of eligible entities as sovereign exposures significantly decreases the risk exposure capital in the standardised approach. There are, however, specific criteria highlighted in this paper that must be met by a PSE to be categorised as a sovereign exposure.

In addition to testing various scenarios of calculating risk weights, this paper highlights regulatory areas that require further improvements and immediate attention from the policymakers and practitioners. At this point, the paper reports that the proposed changes to the risk weight buckets for PSE exposures may be erroneous and resulting from the typos in the second consultative paper.

This paper aims to discuss ideas of factoring in external loss data to the internal loss data sets to obtain a true picture of operational losses for non-bank financial services firms, focusing on a case study of the interdealer brokers business and a specific Basel II category of the operational risk capital charges. As it transpires, financial services firms are increasingly required by regulators to merge external loss data with their internal data sets when using a loss distribution approach. However, there is a significant constrain on the availability and completeness of the external data for non-bank financial services firms.

Embarking on a modified Kaplan-Meier method is a clever way of factoring in external loss data into the internal data set. It allows non-bank financial firms to choose which fragments of the data constitute “the best fit”. In choosing the external data, this paper posits that such firms need to rely on loss-type events that display similar patterns in probabilities of occurrence. This method eliminates over-reliance on the external data that are specific for a different entity. One of the most important assumption underpinning the method presented in this paper is the fact that constant time intervals between the recorded operational loss events are assumed. Hereto, reaching a certain level of loss is used as the event of interest in both groups. For simplification purposes and to eliminate the noise and capture significant losses, we set this level as a multiplicity of the interdealer broker’s loss threshold.

Obtaining external loss data is difficult for the non-bank financial services firms. Furthermore, institutions operating as interdealer brokers are exposed to different levels of operational risk that affect their own Advanced Measurement Approach to capital charges under Basel II. The existing consortium data sets are not suitable for non-bank financial institutions. With this in mind, the non-bank firms should select only the parts of the external data that fit their business environment.

This paper should be of interest to any financial services firms that is required by regulators to merge its internal loss data sets with external loss data. Furthermore, this paper makes strong recommendations for regulators who should understand that the contemporary operational risk consortium data sets are not suitable for non-bank financial services firms.

This paper aims to discuss the impact of nascent Markets in Financial Instruments Directive (MiFID II) initiatives and, thus, to deliver practical insights into MiFID II implementation, compliance and cost reduction MiFID II constitutes the backbone for the upcoming financial market reforms. With the first proposal of MiFID drafted in October 2011, this regulatory framework has undergone over 2,000 amendments. As MiFID II currently stands, this Directive attempts to address issues exposed by the global financial crisis.

This study, based on secondary research and an in-depth analysis of the MiFID II framework, investigates structural and technological challenges entailed by this Directive. The analysis is broken down into the following sections: technological and structural challenges; costs of implementation; MiFID II teams; facilitating near real-time regulatory reporting; increased transparency requirements; and information technology (IT) initiatives for MiFID II compliance.

MiFID II commands significant changes in business and operating models. With this in mind, the study indicates current technological and structural challenges faced by financial institutions and advises on ways of mitigating MiFID II risks. Although it is too early to assess the costs of implementing MiFID II, this paper suggests ways of reducing MiFID II-related costs. The study also advises on organising dedicated teams to deal with MiFID II. Furthermore, this paper argues that early investments in IT systems and processes would allow financial services firms to gain a competitive advantage and, hence, scoop up market share or launch new, lucrative services – especially in the area of collateralisation and market data processing.

This paper shows that the current version of MiFID II still requires a great deal of attention from the regulators that need to readdress contentious issues revolving around the links between MiFID II and other regulatory frameworks such as European Market Infrastructure Regulation and Dodd–Frank. This study addresses the MiFID II compliance issues by adopting European Union and non-European Union banks’ and asset managers’ perspectives and, hence, delivers practical implications for risk managers and compliance officers of various financial institutions.

The purpose of this paper is to outline how banks are coping with the new regulatory challenges posed by stressed value at risk (SVaR). The Basel Committee has introduced three measures of capital charges for market risk: incremental risk charge (IRC), SVaR and comprehensive risk measure (CRM). This paper is designed to analyse the methodologies for SVaR deployed at different banks to highlight the SVaR-related challenges stemming from complying with Basel 2.5. This revised market risk framework comes into force in Europe in 2012. Among the wide range of changes is the requirement for banks to calculate SVaR at a 99 per cent confidence interval over a period of significant stress.

The current research project is based on in-depth, semi-structured interviews with nine universal banks and one financial services company to explore the strides major banks are taking to implement SVaR methodologies while complying with Basel 2.5.

This paper focuses on strengths and weaknesses of the SVaR approach while reviewing peer practices of implementing SVaR modelling. Interestingly, the surveyed banks have not indicated significant challenges associated with implementation of SVaR, and the reported problems boil down to dealing with the poor quality of market data and, as in cases of IRC and CRM, the lack of regulatory guidance. As far as peer practices of implementing SVaR modelling are concerned, the majority of the surveyed banks utilise historical simulations and apply both the absolute and relative measures of volatility for different risk factors.

The academic studies that explicitly analyse challenges associated with implementing the stressed version of VaR are scarce. Filling in the gap in the existing academic literature, this paper aims to shed some explanatory light on the issues major banks are facing when calculating SVaR. In doing so, this study adequately bridges theory and practice by contributing to the fierce debate on compliance with Basel 2.5.

Compliance is defined as conforming to a rule, such as a policy framework, standard or law. Regulatory compliance encompasses all processes that require an entity to be aware of and conform to relevant regulations. As a result, organisation of compliance function remains complex due to the overwhelming set of compliance requirements that exert pressure on various business segments. This report aims to investigate how banks and financial services firms are responding to the regulatory-driven changes to the current compliance landscape, with particular attention paid to nascent challenges and structural changes affecting the organisation of compliance.

The current research project is based on in-depth, semi-structured interviews with five universal banks and three financial services firms to pursue the best practices of adapting to the accelerating change in the regulatory-driven compliance landscape.

In the aftermath of the global financial crisis, banks and financial institutions across the globe have been required to adapt to numerous regulatory reforms that are exerting increased pressure on compliance functions. Amid recent events of multi-million fines to banks that displayed flawed surveillance systems and control failings, the changing regulatory landscape has shown that the relationship with the regulators and compliance with the new regulatory frameworks is a difficult process even for the tier-1 global banks.

Embarking on a peer review of the structures, roles, strategies and responsibilities of different compliance functions across banks and financial services institutions, this paper provides advice to financial institutions on ways of dealing with the complex emerging issues to ensure that the regulatory and compliance arrangements do not turn detrimental. At this point, the paper recognizes that the precise design of a compliance function will vary across individual banks and financial services firms. Nonetheless, this paper addresses the root issues and characteristics that are commonly shared despite the differences in organisations of compliance.

This paper aims to provide an index benchmarking financial services firms against their environmental performance. The paper also introduces a new definition of the environmental risk that fits the current business and regulatory landscape of financial services firms. The Environmental Risk Index (ERI) helps financial services firms review their corporate social responsibility (CSR)/environmental and social governance (ESG) frameworks and address any shortcomings. With this in mind, every financial institution should understand that the Index is not primarily about the ranking, but about the highlighted areas that require significant investment to improve the overall management and understanding of environmental risk. This paper points to the link between being “green” and financial performance. As it transpires, addressing environmental risk serves not only the planet but also banks themselves by bringing in new business, reducing costs and avoiding reputational damage.

The ERI relies on 44 variables grouped into ten thematic vectors that relate to different aspects of environmental risk management. Data for calculating the ERI are obtained by reviewing CSR and Sustainability Reports produced annually by financial services firms. Reports encompassing 2013 have been analysed to ensure objectivity and comparability of the results. A universal approach to all organisations has been taken in the numerical calculation of this index. The variables have been constructed such that they fit a wide range of institutions, from G-SIB banks and international asset managers through to smaller, domestic firms. As it transpires, the efforts to become “greener” are similar for financial institutions regardless of their market capitalisation or international reach.

As far as the general ranking for the ERI is concerned, the range between the first and the last bank equals 524 points. With the maximum of 1,000 points that could be achieved in the ranking, the average score is 633 points and over 50 per cent of the banks have scored above the average. As it transpires, European banks outperform institutions from other regions with the average ERI score of 700. Banks repressing the Middle East region lag behind in their environmental performance. Interestingly, ERI scores and revenue figures are almost uncorrelated for large banks. This proves that any bank, despite its global presence and revenue, can develop similar environmental risk initiatives. The empirical analysis of the index results and revenue figures suggest that the revenue is related to the environmental performance. In other words, it is profitable to become “greener”. For every point in the ERI score gained, the revenue should increase slightly by a factor of 0.02.

This paper cuts through the environmental jargon, extensive literature review on environmental issues, socio-political issues and scientific study to deliver a clear picture of what needs to be done in the area of the environmental risk for financial services firms to reduce costs, increase business, improve reputation, address certain regulatory initiatives, strengthen the environmental and social governance and become more environmentally responsible.

This paper, in a pioneering attempt, has presented the ERI encompassing financial services firms. At this point, the paper serves as a benchmarking tool for financial institutions willing to compare their “green” status. Looking at environmental risk has become an important part of the journey towards carefully managing business processes to generate a positive image. The importance of environmental risk is further underscored by investors, shareholders and regulators taking an increasing interest in banks’ activities. With this in mind, financial services firms need to scrutinise their operations with a particular focus on the quality of management in terms of people, environment and processes.

This paper, based on case-studies with five universal banks from Europe and North America, aims to investigate which types of comprehensive risk measure (CRM) models are being used in the industry, the challenges being faced in implementation and how they are being currently rectified. Undoubtedly, CRM remains the most challenging and ambiguous measure applied to the correlation trading book. The turmoil surrounding the new regulatory framework boils down to the Basel Committee implementing a range of capital charges for market risk to promote “safer” banking in times of financial crisis. This report discusses current issues faced by global banks when complying with the complex set of financial rules imposed by Basel 2.5.

The current research project is based on in-depth, semi-structured interviews with five universal banks to explore the strides major banks are taking to introduce CRM modelling while complying with the new regulatory requirements.

There are three measures introduced by the Basel Committee to serve as capital charges for market risk: incremental risk charge; stressed value at risk and CRM. All of these regulatory-driven measures have met with strong criticism for their cumbersome nature and extremely high capital charges. Furthermore, with banks facing imminent implementation deadlines, all challenges surrounding CRM must be rectified. This paper provides some practical insights into how banks are finalising the new methodologies to comply with Basel 2.5.

The introduction of CRM and regulatory approval of new internal market risk models under Basel 2.5 has exerted strong pressure on global banks. The issues and computational challenges surrounding the implementation of CRM methodologies are currently fiercely debated among the affected banks. With little guidance from regulators, it remains very unclear how to implement, calculate and validate CRM in practice. To this end, a need for a study that sheds some light on practices with developing and computing CRM emerged. On submitting this paper to the journal, we have received news that JP Morgan is to pay four regulators $920 million as a result of a CRM-related scandal.

This report aims to investigate the approaches taken by financial institution to implement and compute the funding valuation adjustment (FVA). FVA can be defined as the incremental cost attributable to trades with non-collateralised counterparties. This cost emerges related to the need to fund the collateral calls associated with collateralised hedge trades. In this respect, one common challenge faced by banks relates to designing appropriate methodological approaches to compute an FVA.

Recognising the growing importance of an FVA, this report is designed to investigate different approaches to computing FVA and pricing funding costs into the uncollateralised positions. Embarking on semi-structured interviews, the report explores the methodologies and structural solutions utilised by global banks.

This report has indicated several influential trends that shape the nascent FVA landscape, as well as innovative initiatives undertaken by the banks to effectively use this metric. Going forward, this paper has pointed to a number of current and future challenges faced by the participating banks with regard to implementing the FVA.

Before regulators make FVA punitive, unprofitable or inadequate by propagating the move to collateralised trading or introducing sanctions on banks recognising FVA in their financial statements, and thus reducing banks’ exposure to arbitrage opportunities, FVA will remain a challenging metric for the banking industry in the near future. Therefore, it is pivotal to understand any potential risks and operational difficulties arising from “sailing on the uncharted waters with FVA”. Moreover, it is necessary to understand market consensus on methodological approaches to computing FVA, as well as practices around constructing the bank’s own cost of funds curves.