Regulatory Capital Requirements Research Articles

Regulatory Capital Requirements, Inflation Targeting, and Equilibrium Determinacy

Regulatory Capital Requirements, Inflation Targeting, and Equilibrium Determinacy

What’s Wrong with Annuity Markets?

Abstract We show that the supply of U.S. life annuities is constrained by interest rate risk. We identify this effect using annuity prices offered by life insurers from 1989 to 2019 and exogenous variations in contract-level regulatory capital requirements. The cost of interest rate risk management—conditional on the effect of adverse selection—accounts for about half of annuity markups, or 8 percentage points. The contribution of interest rate risk to annuity markups sharply increased after the Global Financial Crisis, suggesting new retirees’ opportunities to transfer their longevity risk are unlikely to improve in a persistently low interest rate environment.

Bank capital and liquidity creation: Evidence from the Russian experience

In this paper we examine the relationship between regulatory bank capital and liquidity creation. We test the “financial fragility‐crowding out hypothesis” and the “risk absorption hypothesis” on the sample of 885 Russian banks over the 2010–2019 period. Inspired by Berger and Bouwmanʼs (2009) approach, we develop a liquidity creation measure that simultaneously considers the category and maturity of secondary accounts. After conducting several robustness checks, we report that bank capital is negatively related to liquidity creation which supports the “financial fragility‐crowding out hypothesis”. We also show that this relationship is independent of the bank size and the economic cycle. Our findings suggest that the Central Bank of Russia faces a trade‐off between financial stability and liquidity creation by the banking system, as tougher regulatory capital requirements decrease banks’ abilities to finance the real sector of the economy.

Drivers of moral hazard in banks

AbstractThe objective of this research is to identify factors that determine the moral hazard in banks by using discrete choice models (probit and logit regression). A specially constructed indicator was used to quantitatively assess moral hazard as the average difference between the better rating of a client in a bank and the most conservative rating of the same client in the banking sector. This is an example where moral hazard manifests itself as the tendency of management in banks to underestimate credit risk. The results showed that state-owned banks and foreign privately-owned banks with evident problems at the level of their headquarters had higher values of this quantitative indicator of moral hazard. Also, banks whose financial result and capital are highly sensitive to a small increase in non-performing loans, as well as banks that at any time in the observed period had a problem with meeting regulatory capital requirements, showed a greater propensity to moral hazard, as measured by this indicator. In the above cases, in the absence of quality corporate governance, management in banks tends to show performance better than it actually is. The obtained results for Serbia in comparison with the previous research give the possibility to quantitatively confirm additional specific factors important for explaining moral hazard (composite variable of the type of ownership and capital restrictions, variables that perform only the transmission of certain forms of management behaviour, size, capital and profit sensitivity, credit risk level, etc.), in addition to the common factors of moral hazard, such as the type of ownership in the bank, capital restrictions, etc.

Reviving securitisation in the EU: A critical analysis of the reporting requirements

The EU Securitisation Regulation (SECR) came into force in 2018, applying to securitisations issued after 1st January, 2019 and to eligible legacy securitisations. This is a cornerstone of the Capitals Market Union (CMU), which, together with the amendment of the Capital Requirements Regulation (CRR), aims at reviving the European securitisation market. By introducing a robust and harmonised framework, the regulator intends to resolve the following conundrum: how to relaunch the securitisation market, which is essential for financing the economy, while mitigating its vulnerabilities and the stigma associated with it? More specifically, the CRR amendment contains the calibration of capital requirements in accordance with the updated hierarchy of methods while the SECR defines the criteria for securitisations to qualify as ‘simple, transparent and standardised’ (STS). The SECR also sets common rules for the due diligence obligations of institutional investors, regardless of the sector to which they belong. An intrinsic (although not specific) problem with securitisation is the question of asymmetry of information, since investors have access to less information about the loans backing the tranches than lenders involved in their origination. Article 7 of the SECR addresses this issue by defining high standards to transparency requirements using a very granular reporting method. While market participants recognise the necessity of fostering transparency and due diligence to increase confidence in the market, the current reporting framework, because of the burden it represents, may discourage potential investors and originators. This paper provides a thorough overview of the reporting obligations set on credit institutions in the context of securitisation. It demonstrates how the lack of integration and proportionality combined with technical limitations perpetuates regulatory fragmentation and associated high costs. It proposes potential solutions to integrate requirements from various sources into a unified model and concludes with the necessity to improve the governance of reporting and data requirements at a European level.

Lost in noise? Some thoughts on the use of machine learning in financial market risk measurement

Machine learning has permeated almost all areas in which inferences are drawn from financial data. Nevertheless, in financial market risk measurement most machine learning techniques struggle with some inherent difficulties: Financial time series are very noisy, not stationary and mostly considerably short. This paper contains an easy to implement sequential learning algorithm that overcomes some of these disadvantages. It is based on a Kalman filtering mechanism for quite general stochastic processes and provides a first step in the direction of separating parameter dynamics from the ubiquitous noise component. The core idea here is to use some stylised facts inherent to financial markets time series such as time varying measures of volatility. The new approach is tested using real market data in two different settings. First, a hypothetical portfolio containing credit spread and equity risk is analysed over a time frame containing the outbreak of the global pandemic in 2020 and the beginning of the Russian attack on Ukraine in 2022. Another analysis is focused on US$/EUR exchange rate during a time span containing the global financial crisis of 2008 and the subsequent European sovereign crisis. In all test calculations the proposed sequential learning algorithm performs better than the historical simulation approach used by many firms in the banking industry to meet regulatory capital requirements. Due to its simplicity this method has a high degree of explainability and interpretability which will decrease the inherent model risk. The paper concludes with a discussion of model risk for machine learning in financial institutions. Compared to classical model risk frameworks, the emphasis must be put on the more prominent role of data. The simple approach described in this paper shows that machine learning in financial market risk does not have to get lost in noise.

Modeling your stress away

This paper investigates the validity of banks' credit loss projections in the bi-annual EU-wide bank stress tests, which inform regulatory capital requirements. It finds that banks “re-optimized” their models in 2016 to bring down credit losses, exploiting flexibility in the stress test framework. Specifically, banks whose losses would have increased the most from 2014 to 2016 because of changes in the adverse scenario saw the largest decrease in projected losses thanks to model changes. Upon the release of the 2016 stress test results, stock prices and credit default swap spreads increased more for banks that achieved a greater reduction in credit losses through “re-optimization”, consistent with investors anticipating lower future capital requirements for these banks.

An axiomatic approach to default risk and model uncertainty in rating systems

In this paper, we deal with an axiomatic approach to default risk. We introduce the notion of a default risk measure, which generalizes the classical probability of default (PD), and allows to incorporate model risk in various forms. We discuss different properties and representations of default risk measures via monetary risk measures, families of related tail risk measures, and Choquet capacities. In a second step, we turn our focus on default risk measures, which are given as worst-case PDs and distorted PDs. The latter are frequently used in order to take into account model risk for the computation of capital requirements through risk-weighted assets (RWAs), as demanded by the Capital Requirement Regulation (CRR). In this context, we discuss the impact of different default risk measures and margins of conservatism on the amount of risk-weighted assets.

Regulatory capital and asset risk transfer

AbstractWe explore whether life insurers use a unique reinsurance arrangement to manage assets tied to their regulatory capital. Typical reinsurance allows insurers to reduce their regulatory capital by transferring liabilities (reserves), and the associated assets, to reinsurers. With modified coinsurance (ModCo), insurers maintain control of their liabilities and assets while transferring regulatory capital requirements to the reinsurer. Holding fixed an insurer's reported capital, we find that ModCo allows insurers to report higher risk‐based capital ratios. Insurers with ModCo are less likely to fire sale downgraded bonds. We also find suggestive evidence of regulatory arbitrage, as most ModCo is purchased from reinsurers in countries with low capital requirements or within the same insurance group.

Proposal for calculating regulatory capital requirements for reverse mortgages

In the current context in which many people worry about the sustainability of pension systems, reverse mortgages are gaining popularity because they are a way to supplement elderly people's incomes. However, it is necessary to provide banks with an adequate risk measurement and management procedure for reverse mortgages to increase the commercialization of these products, which will result in greater well-being for the retirement age population. In this paper, we propose a method to measure risk and estimate the regulatory capital requirements for a portfolio of reverse mortgages owned by a financial institution according to Basel II and III. The method considers house price risk, mortality risk and interest rate risk; consequently, regulatory capital requirements need to be computed using a Monte Carlo simulation procedure. The proposed method is general and can accommodate several scenarios for reverse mortgage specifications, including fixed or variable mortgage rates and different income stream schemes (with the lump sum as a particular case). The results for the U.K. show that reverse mortgage providers face higher risk when the lender initially advances a higher amount, with the lump-sum case indicating the highest risk, for relatively younger borrowers, the female population, higher interest rates and floating mortgage rates.

Counterparty credit risk: Lessons from recent events

This paper looks at the lessons learned for risk management from two recent events, the default of Archegos Capital Management in March 2021 and the unusually large price jumps in energy markets in summer 2022. The paper finds that the counterparty exposure from margined derivatives transactions exceeded the required initial margin significantly in both cases, so that the exposures were largely uncollateralised when it mattered. In addition, the standardised approach for counterparty credit risk (SA-CCR) resulted in regulatory capital requirements which were insufficient to cover the banks' losses from the unwinding of large and concentrated derivatives exposures. This made it difficult, even for some large banks, to identify the high loss potential of the transactions with a single client.

Taxes, risk taking, and financial stability

AbstractAfter the global financial crisis, the use of taxes to enhance financial stability received new attention. This paper analyzes the corrective role of taxes in banking and compares two instruments, namely, an allowance for corporate equity (ACE), which mitigates the debt bias in corporate taxation, and a Pigovian tax on bank debt (bank levy). We emphasize financial stability gains driven by lower bank asset risk and develop a principal‐agent model, in which risk taking depends on the bank's capital structure and, by extension, on the tax treatment of debt and equity. We find that (i) the ACE unambiguously reduces risk taking, (ii) bank levies reduce risk taking if they are independent of bank performance but may be counterproductive otherwise, and (iii) taxes are especially effective if regulatory capital requirements are constrained to low levels.

Investigation and Modelling of Economic Systematic Risk and Capital Requirement: A Monte Carlo Simulation

This paper tests the ability of the regulatory capital requirement to cover credit losses at default, as carried out by the economic (optimal) capital requirement in Tunisian banks. The common factor in borrowers that leads to a credit default is systematic risk. However, the sensitivity to these factors differs between borrowers. To this end, we derived two kinds of sensitivity to systematic risk: the first is recognised by the Basel Committee; the second is derived from an economic approach. Hence, we can observe the impact of sensitivity to systematic risk on capital requirements. Empirically, we studied a sample of 100 individual borrowers from a Tunisian deposit bank that had credit in January 2020. We estimated the default probability for each borrower and then simulated their systematic risk sensitivity using the Monte Carlo approach, and compared them with the regulatory risk sensitivity. Then, we tested their effects on the economic and regulatory capital requirements. The results indicate that regulatory capital overestimates economic capital. This is due to the overestimation of borrowers’ contagion in terms of default risk, as shown by the superiority of their regulatory sensitivity systematic risk compared to the simulated risk. This leads banks to devote more capital than is really necessary to reach the regulatory standard. Hence, there was an increase in capital costs and the possibility of an arbitrage opportunity.

Impact of capital regulation on interest rate pass‐through in Sub‐Saharan Africa

AbstractUsing monthly data from 2005 to 2019, we employ a dynamic heterogeneous cross‐sectionally autoregressive distributed lag (CS‐ARDL) model to examine the impact of higher regulatory capital requirements on the interest rate pass‐through (IRPT) to bank lending and deposit rates in 22 Sub‐Saharan Africa (SSA) countries. Two key findings emerge from the investigation: (i) the average IRPT in SSA is incomplete in the long run, and (ii) stringent (higher) regulatory capital requirements reduce the pass‐through of monetary policy to commercial bank lending and deposit rates. The findings suggest that although higher regulatory capital requirements are an effective macro‐prudential tool for enhancing the stability of the banking sector, they could also have the unintended consequences of limiting economic expansion. This trade‐off calls for a careful analysis and balance in the implementation of monetary and bank regulatory policies in the region.

THE INFLUENCE OF BASEL III ON ISLAMIC BANK RISK

This paper investigates the impact of bank regulatory capital on Islamic bank risk using bank-level data from 29 countries and covering the period from 2004 to 2020. Applying the Generalized Method of Moments technique for dynamic panels, we discover that, on average, Islamic bank regulatory capital ratios exceed the level required by Basel III. Our findings provide evidence in support of the Moral Hazard Hypothesis; that is, there is a negative relationship between capital and risk. These findings indicate that Islamic banks are better protected against risk when they fulfil Basel III and IFSB regulatory capital requirements. According to our findings, authorities that aim to improve the financial stability of the banking industry should reinforce their policies and oblige banks to adhere to regulatory capital requirements during a crisis, such as COVID-19. Finally, we observe that different risk indicators have diverse correlations with regulatory capital, and our findings are robust across a variety of estimation methodologies.

Derivatives risks as costs in a one-period network model

In counterparty credit risk complete markets, collateral and capital requirements would be indifferent to banks. The quantification by banks of market incompleteness based on various XVA metrics ([11]) has emerged as the unintended consequence of the FRTB banking reform ([26]) and of the more demanding regulatory capital requirements ([38]). The related risks are in fact reckoned today as the major risks for banks, well ahead market risk ([35, Figure 65 page 67]). The XVA metrics have been introduced and traditionally used by investment banks for pricing and collateral/capital optimization purposes. We demonstrate in this paper that they can be fruitfully used for risk management, suggesting a sound approach to regulatory requirements. We present a one-period cost-of-capital XVA setup encompassing bilateral and centrally cleared trading in a unified framework, with explicit formulas for most quantities at hand. We illustrate possible uses of this framework for running stress test exercises on financial networks with one and two clearinghouses from a clearing member's perspective or for optimizing the porting of the portfolio of a defaulted clearing member using Monte-Carlo technique with corresponding confidence errors in elliptical models. A continuous-time extension of this approach is provided in the companion paper [7].

The role of the banks' rating system in the allocation of loans

The subject of this paper is the analysis of the application of banking internal credit risk measurement models for the purpose of calculating the minimum regulatory capital. The Basel Committee established proposals for an internal rating-based approach (IRB approach-internal rating-based) to capital requirements for credit risk. Such an approach, which relies on the bank's internal assessment of counterparties and exposures, can ensure two key objectives: the first is additional risk sensitivity, in which capital requirements based on internal ratings can be much more sensitive on the drivers of credit risk of economic losses in the banking portfolio; the second is incentive compatibility, where the appropriate structure of the RBI approach can provide a framework that encourages banks to continue to improve their internal risk management practices. The internal ranking approach aims to improve the safety and soundness of the financial system. The paper defines the terminology and classification of the rating system. The probability of default (PD-probability at default) and the other two risk components LGD (loss given default) and EAD (exposure at default) are key input parameters for the calculation of regulatory capital. The rating system is, therefore, a significant driver of risk management and financial performance measurement. To be in a position to demonstrate to supervisors that an internal rating system should be used for the purposes of determining minimum regulatory capital requirements, banks must first demonstrate that the rating system is an integral part of their ongoing operations and risk management culture.

CRR III implementation: Impact on capital requirements, performance and business models of European banks

The European Banking Package II finalises the implementation of the final Basel III standards, which the industry refers to as ‘Basel IV’. It entails many changes to the methods used to determine capital requirements and represents a significant challenge for the European banking sector. Based on the Capital Requirements Regulation (CRR) III draft, this paper provides an overview of the main implementation issues in the European Union, discusses the potential impact on banks' capital requirements and makes policy recommendations. This paper uses primary sources such as the Basel Committee on Banking Supervision, the European Banking Authority and the European Commission. Secondary sources, academic articles or analyses from various stakeholders are also included in the analysis. This paper also provides an analysis of the impact of the new prudential regulations on banks based on 30 detailed Basel IV impact studies conducted over the past two years in consulting projects with banks from almost all EU countries. The impact analysis covers a wide range of different business models, bank sizes and countries. We believe the anonymised data we use is far more representative of the EU banking system and other jurisdictions than the impact studies performed by the European Commission or the BCBS. The new CRR III regulations will pose strategic, operational and regulatory challenges for the banks concerned. The paper concludes that the European implementation of the reforms will not burden a specific group of banks, but banks with different business models and of different size will be impacted differently but still significantly. This makes Basel IV and CRR III unique compared to previous reforms of the Basel framework. The EU Commission's goal of proportionality of regulations will not provide much relief in this regard. The paper provides an up-to-date and comprehensive overview of the planned changes in CRR III, ie in capital adequacy requirements. It analyses the implementation of the standards and compares them with the Basel IV requirements. Recommendations for supervisors, risk management practitioners and other interested parties conclude the paper.

Global financial crisis, international capital requirement and bank financial stability: an international evidence

PurposeFinancial crises (FC) remain a global threat to the financial stability of financial institutions and international bank regulatory capital requirement (IBRCR) by the Committee on Banking Supervision provides mechanism for curbing the adverse effect of FC on financial stability. Hence, the purpose of this study is to provide, evidence on how IBRCR tones down the adverse FC effects on bank financial stability (BFS).Design/methodology/approachThe study uses 102 economies between 2006 and 2016 in a two-step dynamic generalized method of moments model.FindingsThe results show that while FC and IBRCR negatively and positively impact BFS, respectively, it is observed that under the increasing presence of IBRCR, the negative effect of FC on BFS declines. Additionally, the results show that economies that maintain minimum IBRCR above 10.5% recommended by BASEL III are able to reinforce a significant reduction in the negative effect of FC on BFS.Practical implicationsThese findings imply that in as much as financial crisis is injurious to BFS, regulators and policymakers can rely on IBRCR to avert the injurious effects of FC on BFS. Clearly, while IBRCR is necessary for reinforcing BFS through FC, bank managers who maintain IBRCR above the recommended 10.5% stands a better chance to taming the avert effect of FC on BFS. Additionally, economies that have not full adopted the BASEL minimum capital requirement may have to do so given its potential of dampening the adverse effect of FC on BFS.Originality/valueThe study presents an international perspective of how BASEL capital requirements can help tame global financial crisis using a global sample of 102 economies.

Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio

We study the impact of machine learning (ML) models for credit default prediction in the calculation of regulatory capital by financial institutions. We do so by using a unique and anonymized database from a major Spanish bank. We first compare the statistical performance of five models based on supervised learning like Logistic Lasso, Trees (CART), Random Forest, XGBoost and Deep Learning, with a well-known model like Logit. We measure the statistical performance through different metrics, and for different sample sizes and features available. We find that ML models outperform, even when relatively low amount of data is used. We then translate this statistical performance into economic impact by estimating the savings in capital when using an advanced ML model instead of a simpler one to compute the risk-weighted assets following the Internal Ratings Based (IRB) approach. Our benchmark results show that implementing XGBoost instead of Logistic Lasso could yield savings from 12.4% to 17% in terms of regulatory capital requirements.