Systemically Important Banks Research Articles

Jump volatility spillover network based measurement of systemic importance of Chinese financial institutions

AbstractThe identification of systemically important financial institutions (SIFIs) is an important measure to deal with systemic risks. To achieve this goal, we first use generalized variance decomposition method and granger causality test to construct jump volatility spillover networks of Chinese financial institutions based on the 5‐min high‐frequency data. Then, out‐strength and in‐strength are adopted to analyze the SIFI. Finally, we use panel data regression model to investigate the determinant of the SIFIs. The empirical results show that: (a) The network density reaches a peak when the financial system under pressure during the China's stock market disaster of 2015. (b) Large banks and insurances usually display systemic importance, while some small financial institutions are also SIFIs due to their high value of out‐strength and in‐strength. (c) There are obvious differences in the factors that affect the out‐strength and in‐strength based on panel data regression model, but turnover rate, jump volatility, firm size and growth rate of total assets are the common driving factors.

Global banking: Endogenous competition and risk taking

When banks expand abroad, their riskiness decreases if foreign expansion happens in destination countries that are more competitive than their origin countries. We reach this conclusion in three steps. First, we develop a flexible dynamic model of global banking with endogenous competition and endogenous risk-taking. Second, we calibrate and simulate the model to generate empirically relevant predictions. Third, we validate these predictions by testing them on an original dataset covering the activities of the 15 European global systemically important banks (G-SIBs). Our results hold across alternative measures of individual and systemic bank risk.

Window Dressing Systemic Importance: Evidence from EU Banks and the G-SIB Framework

Building on previous research, we study banks’ balance sheet year-end patterns in the European Union (EU) to assess the impact on supervisory measures of their systemic importance. We find that some global systemically important banks (G-SIBs) in the EU compress their balance sheet at year-end to an extent that it allows them to reduce their systemic importance, thus potentially mitigating the impact of the G-SIB capital surcharges or avoiding G-SIB designation altogether. Since some year-end adjustments are a common feature, we compare G-SIBs’ adjustments to those of other systemically important institutions (O-SIIs) and observe that the compression of the latter banks’ balance sheets is notably smaller. G-SIBs’ balance sheets adjustments reflect several drivers, with the most notable year-end declines observed for intra-financial assets and liabilities as well as banks’ notional amounts of over-the-counter derivatives. Reduction is most pronounced for G-SIBs characterised by comparatively high leverage. This evidence of possible window dressing underscores the importance of supervisory judgement in the assessment of G-SIBs, which is indeed a core component of the identification process. We also suggest greater use of average as opposed to point-in-time data in the quantitative part of the G-SIB identification process.

Is window dressing by banks systemically important?

We study banks' year-end window dressing in the European Union to assess how it affects the identification of global systemically important banks (G-SIBs) and the associated capital surcharges. We find that G-SIBs compress their balance sheet at year-end to an extent that they can reduce their surcharges or avoid G-SIB designation altogether. G-SIBs use several levers to adjust their balance sheets. Most notably, they compress intra-financial system assets and liabilities as well as their derivative books at year-end. Moreover, G-SIBs that are more tightly constrained by capital requirements window dress more than their peers. Our findings underscore the importance of supervisory judgement in the assessment of G-SIBs and call for greater use of average as opposed to point-in-time data to measure banks' systemic importance.

Computing the Probability of a Financial Market Failure: A New Measure of Systemic Risk

This paper characterizes the probability of a market failure defined as the simultaneous default of two globally systemically important banks (G-SIBs), where the default probabilities are correlated. The characterization employs a multivariate Cox process across the G-SIBs. Various theorems related to market failure probabilities are derived, including the impact of increasing the number of G-SIBs in an economy and changing the initial conditions of the economy's state variables.

How Do Global Systemically Important Banks Lower Capital Surcharges?

Global systemically important banks (GSIBs) are subject to capital surcharges that increase with systemic importance indicators. We show that U.S. GSIBs lower their surcharges to a large extent by reducing one indicator---the notional amount of over-the-counter derivatives---in the fourth quarter of each year, the quarter that determines surcharges. This seasonal drop is stronger at GSIBs than at other banks; it increased after GSIB surcharges were introduced; and it is largely driven by interest rate swaps. We discuss implications of these results for the design of systemic importance indicators.

How Does Exogenous Shock Change the Structure of Interbank Network?: Evidence from China under COVID-19

This paper utilizes China’s interbank deposit system under COVID-19 to examine how interbank network changes with considerable uncertainty surrounding exogenous shock. We investigate the interbank network in China by specifying the core-periphery pattern to disentangle the network development and the pandemic onset. In order to model the interbank deposit system, maximum entropy and partition-based approach are employed to estimate the bilateral exposures for banks and identify the network structure, respectively. We find that the four largest global systemically important banks (G-SIBs) in the world play a critical role in bearing interbank burden under this crucial period by increasing interbank assets and decreasing interbank liability. By comparing the situations before and after the outbreak of the pandemic in stress-testing, the empirical evidence shows that the number of core banks and capital of banks is declined in 2020Q2; however, the topological structure of the network is increasingly similar to a typical core-periphery pattern, and the changed interbank network can better absorb the loss and interrupt the contagion of systemic risk.

Online Appendix: Systemic Implications of the Bail-In Design

The 2007-2008 financial crisis forced governments to choose between the unattractive alternatives of either bailing out a systemically important bank (SIB) or allowing it to fail disruptively. Bail-in has been put forward as an alternative that potentially addresses the too-big-to-fail and contagion risk problems simultaneously. Though its efficacy has been demonstrated for smaller idiosyncratic SIB failures, its ability to maintain stability in cases of large SIB failures and system-wide crises remains untested. This paper’s novelty is to assess the financial-stability implications of bail-in design, explicitly accounting for the multi-layered networked nature of the financial system. We present a model of the European financial system that captures all five of the prevailing contagion channels. We demonstrate that it is essential to understand the interaction of multiple contagion mechanisms and that financial institutions other than banks play an important role. Our results indicate that stability hinges on the bank-specific and structural bail-in design. On one hand, a well-designed bail-in buttresses financial resilience, but on the other hand, an ill-designed bail-in tends to exacerbate financial distress, especially in system-wide crises and when there are large SIB failures. Our analysis suggests that the current bail-in design may be in the region of instability. While policy makers can fix this, the political economy incentives make this unlikely.

Systemic Implications of the Bail-In Design

The 2007-2008 financial crisis forced governments to choose between the unattractive alternatives of either bailing out a systemically important bank (SIB) or allowing it to fail disruptively. Bail-in has been put forward as an alternative that potentially addresses the too-big-to-fail and contagion risk problems simultaneously. Though its efficacy has been demonstrated for smaller idiosyncratic SIB failures, its ability to maintain stability in cases of large SIB failures and system-wide crises remains untested. This paper’s novelty is to assess the financial-stability implications of bail-in design, explicitly accounting for the multi-layered networked nature of the financial system. We present a model of the European financial system that captures all five of the prevailing contagion channels. We demonstrate that it is essential to understand the interaction of multiple contagion mechanisms and that financial institutions other than banks play an important role. Our results indicate that stability hinges on the bank-specific and structural bail-in design. On one hand, a well-designed bail-in buttresses financial resilience, but on the other hand, an ill-designed bail-in tends to exacerbate financial distress, especially in system-wide crises and when there are large SIB failures. Our analysis suggests that the current bail-in design may be in the region of instability. While policy makers can fix this, the political economy incentives make this unlikely. In all, our findings indicate that the too-big-to-fail problem remains unresolved today.

<i>Resolution of Systemically Important Banks: Methodological Aspect</i>

<i>Resolution of Systemically Important Banks: Methodological Aspect</i>

Decentralized Financial Market Infrastructures

Financial market infrastructures (FMIs) have evolved as core elements of highly intermediated financial markets partly due to the technological limitations of the time when they were first architected. Organizations and firms were unable to share records without having to entrust a single party to manage them; hence this phenomenon of intermediation has led to significant information silos. Simultaneously, it has driven the structure of business models, as well as regulatory supervision and oversight, in ways that furthered intermediation and also created a misalignment of incentives and risk taking between entities now categorized as systemically important financial institutions (SIFIs) and systemically important financial market infrastructures. Over time, this consolidation has led to highly concentrated FMIs and with it, concentrated risks. Some of these risks go beyond the credit risks of just one or two institutions, becoming instead systemic risks that are continuously monitored by regulatory bodies based on coordinated sets of principles and guidelines including the 2012 Principles for Financial Market Infrastructures from CPMI and IOSCO. Over the past decade, advances in public key cryptography, hash functions, virtualisation, distributed consensus, multiparty computation, and peer-to-peer networking have led to experimentation around record sharing between erstwhile competitive firms. Over the past five years, a series of independent efforts has chaperoned regulatory requirements into a digital, automated state that enables secure information sharing in full compliance with the law, while simultaneously enabling market participants to mutualise infrastructure that would otherwise be run by a single trusted party. With these developments, many of the services that centralised intermediaries currently provide could potentially be replaced by decentralised infrastructures or decentralised financial market infrastructure (dFMI). dFMI also enables a change in business structure, where a re-alignment of incentives can take place such that those firms taking risks can fully bear the consequences of these risks.

The triple (T3) dimension of systemic risk: Identifying systemically important banks

AbstractThis paper proposes a new systemic risk measure based on a multi‐way analysis. The systemic risk is composed of two different components: the time and the cross‐dimension. The first refers to the accumulation of banking risk and its interaction with the business cycle, while the second concern, the high‐level concentration of the specific risk on relevant financial institutions. Thanks to the three‐way analysis, we can, on the one hand, estimate a new measure of systemic risk based on these both components, and, on the other hand, we can transparently identify the SIBs (systemically important banks). To this end, we have empirically evaluated and compared the marginal expected shortfall (MES), the SRISK measure and the conditional value at risk (CoVaR) based on a representative sample of Eurozone institutions listed on the stock exchange for the period from June 2005 to May 2018. Our results show how these systemic risk measures produce different systemic risk classifications for the same bank. The findings, therefore, highlight the fragility and structural dependence of these measures, which may not be used for the estimation of a stable rank. Applying three‐way factorial analysis, we show how our measure gives a more stable score. Moreover, our index is the first one to be composed of both the cross‐section and the temporal components, essential elements for a proper assessment of systemic risk.

A Riskmas Carol

Do you believe in Santa Claus (rally)? This study investigates the existence of the ‘Santa Claus rally’ in bank systemic risk. Christmas rally describes a persistent rise in the stock market during the final week of December through the first two trading days in January. In this article, we evaluate this calendar effect, focusing on systemic risk measures for global systemically important banks (GSIBs). First, we estimate the three popular systemic risk measures (DCoVaR, marginal expected shortfall [MES] and SRISK), and then we use an event study approach to analyse the reaction of risk. The results support the existence of Santa Claus. We find that the arrival of Santa Claus has a positive effect on systemic risk, that is, a reduction in bank systemic risk.

CoMap: Mapping Contagion in the Euro Area Banking Sector

This paper presents a novel approach to investigate and model the network of euro area banks’ large exposures within the global banking system. Drawing on a unique dataset, the paper, for the first time, documents the degree of interconnectedness and systemic risk of the euro area banking system based on bilateral linkages. We then develop a Contagion Mapping (CoMap) methodology to study contagion potential of an exogenous default shock via counterparty credit and funding risks. We construct contagion and vulnerability indices measuring respectively the systemic importance of banks and their degree of fragility. Decomposing the results into the respective contributions of credit and funding shocks provides insights to the nature of contagion which can be used to calibrate bank-specific capital and liquidity requirements and large exposures limits. We empirically confirm that tipping points shifting the euro area banking system from a less vulnerable state to a highly vulnerable state are a non-linear function of the combination of network structures and bank-specific characteristics.

Measuring the deadly embrace: Systemic and sovereign risks

This study contributes to the literature by making a first step toward implementing a comprehensive internally coherent measurement of systemic risk in a country. It measures systemic risk and the ensuing conditional contingent liabilities of the sovereign stemming from Luxembourg’s Other Systemically Important Institutions (OSIIs), the Global Systemically Important Banks (G-SIBs) to which they belong, the investment funds sponsored by the OSIIs, the household and the non-financial corporate sectors. The ensuing estimated systemic contingent claims are included in a stochastic version of the general government’s balance sheet to gauge their impact on the country’s sovereign risk. Results indicate that time-varying conditional implicit guarantees from OSSIs are larger than those from G-SIBs and investment funds, while systemic risk stemming from the household and non-financial corporate sectors is moderate. The robustness of the sovereign is not drastically affected by systemic risk stemming from the rest of the economy. However, illustrating the so-called “deadly embrace”, sovereign risk would significantly rise as a result of a historically plausible increase in sovereign assets’ value volatility combined with an economy-wide shock. The main policy implication is that financial stability stands on two columns, a resilient financial sector and a sustainable fiscal position.

Global systemically important banks regulation: Blessing or curse?

Do market participants believe that recent regulations will curb moral hazard at global systemically important banks (G-SIBs)? We analyze market returns to three events: the 2011 designation of certain banks as G-SIBs, the November 2014 identification of banks subject to additional capital surcharges, and the November 2015 announcement of Total Loss Absorption Capacity (TLAC) requirements. We find that reaction to the “designation event” fits the profit-based-reaction hypothesis, reaction to the “additional capital surcharges event” fits the regulatory burden hypothesis, and reaction to the “TLAC event” fits the irrelevance hypothesis. Weaker banks benefited more from being designated as G-SIBs, while EU banks suffered more from the additional capital surcharges.

Assessing global systemically important banks and implications for entrepreneurship: a hierarchy stochastic multicriteria acceptability analysis

PurposeThe financial crisis of 2007–2008 has brought critical attention to those financial institutions whose high destabilizing effects can have an impact on the financial system. Basel Committee (BIS) has introduced a set of regulations to identify such banks, defined global systemically important banks (G-SIBs). The purpose of this paper is to investigate the robustness of the BIS's methodology.Design/methodology/approachIn this study, a multicriteria decision aid (MCDA) approach is considered, specifically HSMAA (hierarchy stochastic multiobjective acceptability analysis), which can take into account uncertainty on BIS's criteria weights as standard SMAA, but also uncertainty with respect to the subcriteria of the considered criteria. The results are analyzed to provide insights into the robustness of the BIS's methodology.FindingsThe obtained results indicate that overall the BIS's methodology does not appear robust. Within the two uncertainty scenarios considered, the results show a great variability in identifying the G-SIBs' systemic risk.Originality/valueThe paper proposes an analytical way to assess the robustness of the BIS's methodology. Moreover, the authors’ analysis reveals a great variability on the results, which highlight different issues for banks, regulators and enterprises.

A Note on Regulatory Responses to Covid-19 Pandemic: Balancing Banks’ Solvency and Contribution to Recovery

We see spikes in unemployment rates and negative economic growth during the COVID-19 pandemic. Governments are responding with aggressive monetary expansions and large-scale economic relief plans. We discuss the implications on banks and the economy of prudential regulatory intervention to soften the treatment of non-performing loans and ease bank capital buffers. We apply these easing measures on a sample of Globally Systemically Important Banks (G-SIBs) and show that these banks can play a constructive role in sustaining economic growth during the COVID-19 pandemic. However, softening the treatment of non-performing loans along with easing capital buffers should not undermine banks’ solvency in the recovery period. Banks should maintain usable buffer in the medium-term horizon to absorb future losses, as the effect of COVID-19 on the economy might take time to fully materialise.

The Danger of Developing Then Dashing Expectations: The Lesson from Lehman?

In the economy expectations exercise enormous effects. Indeed, in finance prices and yields predominantly reflect current expectations about future cash flows and future interest rates. Change those expectations, prices and yields change as well. Change expectations radically enough, the change in prices and yields may be large enough to impact financial markets and the economy at large. The failure of Lehman in September 2008 is a case in point. Prior to its bankruptcy on 15 September 15, 2008, the economy was treading water. Thereafter it sank like a stone, until the massive stimulus programs put in place by governments and central banks began to pull the economy up again.This paper suggests that what made the Great Recession great was the entry of Lehman into bankruptcy. This suddenly swung market expectations concerning government policy from “too big to fail” to “let the chips fall where they may”. Following Lehman’s declaration of bankruptcy, panic ensued, as market participants rushed to adjust to the new situation. This set off a downward debt-deflation spiral that caused the world economy contract rapidly. Only massive monetary and fiscal stimulus, together with explicit government support to systemically important financial institutions (SIFIs) arrested the decline and turned what might have become the Great(er) Depression into the Great Recession. This weakened economies around the world, leading to further crises and changing the political landscape.In concept therefore, allowing implicit guarantees such as “too big to fail” to develop generates not only moral hazard but also creates what might be called public hazard. This is the risk to financial stability and the economy at large, if the government fails to fulfil the implicit guarantee, when called upon to do so. This is a lesson that governments would do well to bear in mind as they wrestle with how to taper off the massive support that they are currently giving to firms and individuals to enable them to offset the economic consequences of COVID-19.

Quantifying systemic risk with factor copulas

ABSTRACT We propose a tail dependence based network approach to study systemic risk in a network of systemically important financial institutions (SIFIs). We utilize a flexible factor copula based method which allows to measure the level of extreme risk in a portfolio when dependence is driven by one or several factors. We identify the most ‘connected’ SIFIs based on an eigenvector centrality approach applied to copula-implied dependence structures as ‘central’ SIFIs. We then demonstrate that the level of systemic risk implied by such SIFIs chosen as conditioning factors in the factor copula setup exceeds that which is implied by non-central SIFIs in terms of portfolio Value-at-Risk and the portfolio return under stress. This study contributes to quantification and ranking of the systemic importance of SIFIs which is important for setting adequate capital requirements in particular and stability of financial markets in general.