In light of the policy debate on too-big-to-fail we investigate evidence of economies of scale for 103 European listed banks over 2000–2011. Using the Stochastic Frontier Approach, the results show that economies of scale are widespread across different size classes of banks and are especially large for the biggest banks. At the country level, banks operating in the smallest financial systems and the countries most affected by the financial crises realize the lowest scale economies (including diseconomies) due to the reduction in production capacity. As for the determinants of scale economies, these mainly emanate from banks oriented toward investment banking, with higher liquidity, lower Tier 1 capital, those that contributed less to systemic risk during the crises, and those with too-big-to-fail status.

/pdf/are-european-banks-too-big-evidence-on-economies-of-scale-5iz3u92hw1.pdf

Are European banks too big? Evidence on economies of scale

With the entry into force in January 2022 of the new market risk framework based on the FRTB, initially planned for 2019, all banks will have to revise its market risk framework and adapt it to the new regulation. Among all changes introduced by the FRTB there are two that stand out of all of them: The complete reform of the Standardised Approach to estimate capital requirements, which estimation will be required by the regulators, even if the entity calculates its requirements through the IMA; and the substitution of VaR by ES as measure of risk. To analyse the consequences of the changes in the calculation of the SA this paper will compare the current SA with the SA under FRTB to estimate the capital increase by market risk that this new method will bring to the entities and the consequences that this can suppose for them. In the same way, results of VaR and ES at different levels of confidence will be compared in order to find the differences between both risk measures when quantifying risk.

The Fundamental Review of the Trading Book

Manufacturing firms manage complex supply chain networks which are exposed to a plethora of hazard events. An essential part of the risk management process is the calculation of the stand-alone risk exposures of the product-specific supply chains, but also of the entire multi-product system. In this paper, first, a global sensitivity analysis of the statistical supply chain risk model is conducted. This method helps the decision-makers to understand the risk of the model they are using. Second, a methodology for risk aggregation in multi-product supply chain networks is proposed. The real-world data is used to analyse and validate the model. Supply chain managers equipped with the proposed method will better cope with the risk in supply chains for different product configurations.

Global sensitivity analysis and aggregation of risk in multi-product supply chain networks

Are mergers among cooperative banks worth a dime? Evidence on efficiency effects of M&As in Italy

This paper introduces a new model of the stochastic production frontier that incorporates an unobservable bound for inefficiency, which is naturally instituted by market competition forces. We consider doubly truncated normal, truncated half-normal, and truncated exponential distributions to model the inefficiency component of the error term. We derive the analytical form of density function for the error term of each specification, expressions of the conditional mean of inefficiency levels, and provide proofs of local identifiability of these models under differing assumptions about the deep parameters of the distributions. We examine skewness properties of our new estimators and provide an explanation for the finding of positive (“wrong”) skewness in many applied studies using the traditional stochastic frontier model. We extend the model to the panel data setting and specify a time-varying inefficiency bound as well as time-varying efficiencies. A Monte Carlo study is conducted to study the finite sample performance of the maximum likelihood estimators in cross-sectional settings. Lastly, we illustrate the use of our model to the analysis of efficiencies in the US banking industry from 1984 to 2009 using a recently developed panel of over 4,000 banks and also compare our findings to those based on a set of competing specifications of the stochastic frontier model. We find substantial increases in efficiency after the regulatory reforms of the 1980s but also substantial backsliding during the 2005–2009 period presaging the financial meltdown experienced in the US and worldwide.

Stochastic Frontier Models with Bounded Inefficiency

A challenge in enterprise risk measurement for diversified financial institutions is developing a coherent approach to aggregating different risk types. This has been motivated by rapid financial innovation, developments in supervisory standards (Basel 2) and recent financial turmoil. The main risks faced - market, credit and operational – have distinct distributional properties, and historically have been modeled in differing frameworks. We contribute to the modeling effort by providing tools and insights to practitioners and regulators. First, we extend the scope of the analysis to liquidity and interest rate risk, having Basel Pillar II of Basel implications. Second, we utilize data from major banking institutions’ loss experience from supervisory call reports, which allows us to explore the impact of business mix and inter-risk correlations on total risk. Third, we estimate and compare alternative established frameworks for risk aggregation (including copula models) on the same data-sets across banks, comparing absolute total risk measures (Value-at-Risk – VaR and proportional diversification benefits-PDB), goodness-of-fit (GOF) of the model as data as well as the variability of the VaR estimate with respect to sampling error in parameter. This benchmarking and sensitivity analysis suggests that practitioners consider implementing a simple non-parametric methodology (empirical copula simulation- ECS) in order to quantify integrated risk, in that it is found to be more conservatism and stable than the other models. We observe that ECS produces 20% to 30% higher VaR relative to the standard Gaussian copula simulation (GCS), while the variance-covariance approximation (VCA) is much lower. ECS yields the highest PDBs than other methodologies (127% to 243%), while Archimadean Gumbel copula simulation (AGCS) is the lowest (10-21%). Across the five largest banks we fail to find the effect of business mix to exert a directionally consistent impact on total integrated diversification benefits. In the GOF tests, we find mixed results, that in many cases most of the copula methods exhibit poor fit to the data relative to the ECS, with the Archimadean copulas fitting worse than the Gaussian or Student-T copulas. In a bootstrapping experiment, we find the variability of the VaR to be significantly lowest (highest) for the ECS (VCA), and that the contribution of the sampling error in the parameters of the marginal distributions to be an order or magnitude greater than that of the correlation matrices.

/pdf/models-for-risk-aggregation-and-sensitivity-analysis-an-w2koxs8t3e.pdf

Models for Risk Aggregation and Sensitivity Analysis: An Application to Bank Economic Capital

A central challenge to the practice of enterprise risk measurement and management faced by diversified financial institutions (e.g., an internationally active bank or insurance company) is developing a coherent approach to aggregating different risk types. This has been motivated by rapid financial innovation, developments in supervisory standards (Basel 2) and recent financial turmoil. The main risks faced - market, credit and operational – have distinct distributional properties, and historically have been modeled in differing frameworks. We contribute to the modeling effort along four dimensions, providing tools and insights to practitioners and regulators. First, we extend the scope of the analysis for these three risks that have been the focus of the literature, analyzing proxies for liquidity and interest rate risk, which has implications for Pillar II of Basel IRB framework. Second, we utilize actual data representative of major banking institutions’ loss experience, extracted from call reports, submitted by banks to supervisory agencies. This allows us to explore the impact of business mix and inter-risk correlations on total risk. Third, we estimate alternative copula models, an established framework for capturing realistic distributional features of different risk types (e.g., non-normality) and cohesively combining such, on the same data-set. We then compare our models to several conventional approaches to computing risk amongst practitioners. Finally, we apply recently developed goodness-of-fit (GOF) tests to the various copula models, which has been a largely neglected in theory and practice. Overall, our results constitute a sensitivity analysis that is evidence for practitioners to consider implementing a simple non-parametric methodology in order to quantify integrated risk. We observe a wide divergence in measured VaR, diversification benefits as well as the sampling variation across different risk aggregation methodologies and types of institutions (the five largest banks by book value of assets as of 4Q08), We observe in our sample that, contrary to asymptotic theory, empirical copula simulation (ECS) tends to produce the highest absolute magnitudes of VaR relative to standard copula formulations (e.g. Gaussian copula simulation - GCS) on the order of about 20% to 30%; while the variance-covariance approximation (VCA) tends to under-state risk. The proportional diversification benefits, as measured by the relative VaR reduction vis a vis the assumption of perfect correlation, exhibit wide variation across banks and aggregation techniques. The ECS generally yields the highest values relative the other methodologies (127% to 243%), while the Archimadean Gumbel copula (AGCS) is the lowest (10-21%). We conclude that while the ECS may over-state absolute risk relative, proportional diversification benefits may be understated by standard methodologies on the order of about 15% to 30%. Through differences observed across the five largest banks, we fail to find the effect of business mix to exert a directionally consistent an impact on total integrated diversification benefits. In the GOF tests, we find mixed results, that in many cases most of the copula methods exhibit poor fit to the data relative to the ECS, with the Archimadean copulas fitting worse than the Gaussian or Student-T copulas. In a bootstrapping experiment, in which we are able to measure the inherent variability in the VaR integrated risk and proportional diversification benefit measures, we find the variability of the VaR to be significantly lowest (highest) for the ECS (VCA) as compared to other copula formulations. We also find that the contribution of the sampling error in the parameters of the marginal distributions to be an order or magnitude greater than that of the correlation matrices. Given the conservatism and stability of the ECS methodology, the poor performance of the standard methodologies in GOF analysis, and the lack of consensus upon the best copula to use, we believe that we have an argument for consideration of the ECS method.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1444359_code97517.pdf?abstractid=1433065&rulid=249187&mirid=3

The recent financial crisis has given rise to a re-examination by regulators and academics of the conventional wisdom regarding the implications of the spectacular growth of the financial sector of the economy. In the pre-crisis era, there was a widespread common wisdom that “bigger is better.” The arguments underpinning this view ranged from potential economies of scale and scope, to a better competitive stance at the international level. However, in the post-crisis world the common wisdom has been altered somewhat as large banks have come to be viewed as problematic for policy makers and regulators, for various reasons. One reason often given is that economic agents who are insured have the incentive to take on too much ex ante risk; also known as the moral hazard problem. Second, there is the “too-big-to-fail” problem: the fear that large and interconnected financial institutions may become a source of systemic risk if allowed to go out of business, especially in a “disorderly” fashion (Bernanke (2009)). Support for or against large banking institutions turns on the central issue of whether or not efficiencies of scale and scope are economically and statistically significant and are positively associated with bank size. If they are positively associated with bank size then the expected benefits of the cost savings generated by increased efficiencies passed on to consumers in terms of better services or reduced banking service fees are traded off with the expected costs implicit in the moral hazard and systemic risk arguments.

http://www.michaeljacobsjr.com/files/InanoglujacobsLiuSickles_BankEfficiency_HndbkPstCrisisFinMdl_PalgraveMacmillan_Ch5_pp110-146_Proof_20151113.pdf

Analyzing Bank Efficiency: Are “Too-Big-to-Fail” Banks Efficient?

This article examines the impact of the new supervisory standards of Basel 2.5 and Basel III for bank trading portfolios with regards to the additional capital requirements developed to mitigate liquidity risk and credit risk. Using the incremental risk charge (IRC), the authors estimate risk measures in several alternate contexts. They find a potentially material increase in capital requirements above and beyond that concluded in the far-ranging impact studies conducted by the international supervisors. This effect is accentuated for financial or sovereign sectors as compared with industrial sectors, and regulatory capital is larger than economic capital. They compare credit risk models and find that the multivariate model reveals larger capital estimates for the financial and sovereign sectors by orders of magnitude versus the industrial sector or the Basel II model. Finally, in a Bayesian experiment, they find that the new requirements may introduce added uncertainty into risk measures as compared with existing approaches.

Bank Capital and New Regulatory Requirements for Risks in Trading Portfolios

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Papers

Models for Risk Aggregation and Sensitivity Analysis: An Application to Bank Economic Capital

Models for Risk Aggregation and Sensitivity Analysis: An Application to Bank Economic Capital

Analyzing Bank Efficiency: Are “Too-Big-to-Fail” Banks Efficient?

Bank Capital and New Regulatory Requirements for Risks in Trading Portfolios

Bank Capital and New Regulatory Requirements for Risks in Trading Portfolios