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How Do Global Systemically Important Banks Lower Capital Surcharges?

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Abstract

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.

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Notes

  1. Because these data started on a quarterly basis only after surcharges were introduced, we cannot use them to determine whether surcharges caused these drops or whether these drops already existed before surcharges. However, assuming that indicators subject to window dressing driven by surcharges would necessarily exhibit larger drops in the fourth quarter at GSIBs in the post surcharge period, we use this initial evidence to screen the indicators that might be subject to manipulation.

  2. Data that start before surcharges were introduced—the FR Y-9C data—are not available for every systemic importance indicator. Also, even for the indicators with some FR Y-9C data available, the definitions of the data collected in the FR Y-9C and the FR Y-15 often differ. For these reasons, we perform the initial screening of systemic importance indicators with the FR Y-15 data exist for all indicators but start on a quarterly basis only in the second quarter of 2016.

  3. See also De Genaro et al. (2021), who study the effects of capital surcharges for domestic systemically important banks in Brazil on the interest rate spreads that banks charge on their loans.

  4. The banking literature has studied other bank characteristics that are not included in the GSIB framework, but are related to categories of systemic importance indicators from the framework. For example, (Goldberg and Meehl 2020) examine how measures of organizational, business, and geographic complexity of large U.S. banks have changed over time and Correa and Goldberg (2022) analyze how these measures affect systemic risk.

  5. The GSIB framework and the U.S. GSIB rule were introduced in publications from the Basel Committee on Banking Supervision and the FRB, respectively. Basel Committee on Banking Supervision (2013, 2018) describe the Committee’s GSIB framework and includes an empirical analysis supporting the calibration of surcharges. Basel Committee on Banking Supervision (2014) provides more details about how GSIB scores are calculated in that framework. Federal Register (2015) contains the U.S. GSIB rule, which adopts the Basel Committee’s framework with some changes. Board of Governors of the Federal Reserve System (2015) discusses the rationale and the calibration of surcharges for U.S. banks. See also Passmore and von Hafften (2018), who argue that GSIB surcharges are too small because the framework “underestimates the probability of bank failure, wrongly disregards short-term funding, and excludes too many banks."

  6. For this calculation, the value of the substitutability score is capped at 100.

  7. D’Errico and Roukny (2021) estimate that about three quarters of the notional amount of OTC derivatives in a sample of contracts from firms in the European Union can be eliminated with compression.

  8. Based on a similar reasoning, (Aramonte and Huang 2019) argue that the increase in clearing by central counterparties across various types of OTC derivatives likely contributed to a decrease in notional amounts in recent years. Because central counterparties can observe the positions of their clearing members, they can also identify the notional amounts that can be compressed.

  9. The FRB introduced the FR Y-15 in 2012 to calculate GSIB surcharges, to monitor the systemic risk profile of BHCs subject to enhanced prudential standards under section 165 of the Dodd-Frank Act, to identify other institutions that may present systemic risk, and to analyze the systemic risk implications of proposed mergers and acquisitions.

  10. The assets threshold was $50 billion at the start of the sample period and the Economic Growth, Regulatory Relief, and Consumer Protection Act of 2018 increased this threshold to $100 billion in the second quarter of 2018. Since the start of the sample period, 8 U.S. banks were GSIBs: Bank of America, Bank of New York Mellon, Citigroup, Goldman Sachs, JP Morgan Chase, Morgan Stanley, State Street, and Wells Fargo. The 18 non-GSIB banks in the sample are Ally, American Express, BMO Financial, Capital One, Citizens Bank, Discover, Fifth Third, HSBC, Huntington Bank, Keycorp, M &T Bank, MUFG, Northern Trust, PNC, Regions Financial, Santander, Truist, and US Bank. BB &T acquired SunTrust in December 2019 and changed its name to Truist. In our empirical analysis, we assume BB &T and Truist are the same bank.

  11. The asset thresholds for the collection of STWF data are as follows: assets greater than $700 billion, assets between $250 billion and $700 billion, and assets between $50 billion and $250 billion, where banks in each threshold are subject, respectively, to the aforementioned reporting dates. For a complete description of STWF reporting thresholds, see Schedule G of the FR Y-15 reporting instructions (Board of Governors of the Federal Reserve System 2019).

  12. Method 2 scores from GSIBs start in the fourth quarter of 2016 because this is the first quarter when GSIBs had to report STWF data, which are necessary to calculate method 2 scores. Also, method 2 scores from non-GSIBs start in the second quarter of 2018 because this is the first quarter when all non-GSIBs had to report STWF data.

  13. Non-GSIBs’ average normalized method 2 score rose more than GSIBs’ over this period to some extent because of mergers and acquisitions involving non-GSIBs. These events include BB &T’s acquisition of SunTrust in December 2019 (when BB &T changed its name to Truist), Huntington Bank’s acquisition of TCF Financial Corporation in June 2021, PNC’s acquisition of BBVA USA in October 2021, and M &T Bank’s acquisition of People’s United Financial in April 2022. The Truist deal, together with a four-fold increase in Regions Financial’s STWF systemic importance indicator, contributed substantially to the jump in the non-GSIBs average score in the fourth quarter of 2019, the largest change over the sample period.

  14. The list of banks that met the criteria to be subject to the GSIB surcharge did not change during the sample period. As a result, the GSIB status is fixed at the bank level throughout the sample period and the variable \(GSIB_{i}\) does not need a subscript t.

  15. These contracts do not include credit default swaps (CDS) because the FR Y-9C started covering CDS data a few years after the start of our series.

  16. This comparison is based on mean method 1 scores reported in Table 1 of our paper, Table 2 of Behn et al. (2022), and Table 3 of Garcia et al. (2023).

  17. Munyan (2017) shows evidence that foreign banks window dress their repo amounts at quarter-end but domestic banks do not, and that this difference in behavior can be attributed to reporting requirements.

  18. Differences in reporting requirements may induce different behavior across jurisdictions even if regulatory requirements are the same. For instance, banks may be less inclined to window dress balance sheet items that they must report as quarterly averages and as quarter-end values because these two numbers together may help regulators estimate the impact of window dressing.

  19. Favara et al. (2021); Degryse et al. (2023), and Behn and Schramm (2021) use credit registry data, which allow them to control for borrower and loan characteristics to estimate effects of GSIB surcharges and designation on corporate lending. However, these papers do not analyze year-end variation driven by surcharges.

  20. The FRB may have anticipated banks’ strategic behavior. Thus, the FRB may have set the parameters of method 2 scores and surcharges higher to account for this behavior. In fact, one objective of surcharges was to motivate GSIBs to reduce their systemic footprints, which are measured with GSIB scores (Basel Committee on Banking Supervision 2013; Board of Governors of the Federal Reserve System 2015; Yellen 2015). Therefore, this behavior may have been not only expected, but also an intended outcome. However, the seasonal variation in measures of systemic importance that we document was most likely unwanted because they have lowered surcharges. In addition, surcharges could not account for GSIBs’ strategic behavior for two main reasons. First, the FRB calibrated surcharges in a way that “a GSIB should hold enough capital to lower its probability of failure so that its expected impact is approximately equal to that of a non-GSIB" (Board of Governors of the Federal Reserve System 2015). Thus, the FRB calibrated surcharges to induce GSIBs to hold just enough capital to meet this expected impact, which does not include a buffer to accommodate their strategic behavior. Second, when the FRB used data from GSIBs and non-GSIBs to calibrate surcharges from the second quarter of 1987 to the fourth quarter of 2014, GSIBs’ year-end reactions to surcharges could not be observed yet.

  21. The Dodd-Frank Act reformulated the regulation of OTC derivatives regarding execution, clearing, and reporting. U.S. regulatory agencies were in charge or implementing those changes, which they did over the following years. For instance, even as late as in 2020, the Securities and Exchange Commission approved a rule requiring counterparties to engage in compression “periodically" (Federal Register 2020). Thus, reforms following the principles of the Dodd-Frank Act have been gradually implemented over time, instead of at a single point in time.

  22. For this calculation, the value of the substitutability score is capped at 100.

  23. In fact, when the U.S. rule of GSIB surcharges was proposed, commenters argued to the Federal Reserve Board that an approach with a fixed denominator would provide more certainty to BHCs about the actions that they could take to reduce their GSIB surcharges (Federal Register 2015).

References

  • Allen L, Saunders A (1992) Bank window dressing: Theory and evidence. J Bank Finance 16(3):585–623

    Article  Google Scholar 

  • Aramonte S, Huang W (2019) Otc derivatives: Euro exposures rise and central clearing advances. BIS Quarterly Review

  • Basel Committee on Banking Supervision (2013) Global systemically important banks: Updated assessment methodology and the higher loss absorbency requirement. Bank for International Settlements, Basel, Switzerland

    Google Scholar 

  • Basel Committee on Banking Supervision (2014) The g-sib assessment methodology-score calculation. Bank for International Settlements, Basel, Switzerland

    Google Scholar 

  • Basel Committee on Banking Supervision (2018) Global systemically important banks: Revised assessment methodology and the higher loss absorbency requirement. Bank for International Settlements, Basel, Switzerland

    Google Scholar 

  • Behn M, Haselmann R, Vig V (2022) The limits of model-based regulation. Journal of Finance 77(3):1635–1684

    Article  Google Scholar 

  • Behn M, Mangiante G, Parisi L, Wedow M (2022) Behind the scenes of the beauty contest: Window dressing and the g-sib framework. Int J Cent Bank 18(5):301–342

    Google Scholar 

  • Behn M, Schramm A (2021) The impact of g-sib identification on bank lending: Evidence from syndicated loans 57

  • Berg T, Koziol P (2017) An analysis of the consistency of banks’ internal ratings. J Bank Finance 78:27–41

  • Board of Governors of the Federal Reserve System (2015) Calibrating the gsib surcharge. July 20, 2015. https://www.federalreserve.gov/aboutthefed/boardmeetings/gsib-methodology-paper-20150720.pdf

  • Board of Governors of the Federal Reserve System (2019) Instructions for preparation of banking organization systemic risk report. https://www.federalreserve.gov/reportforms/forms/FR_Y-1520191231_i.pdf. Accessed on April 29, 2020

  • Carey M (2002) Some evidence on the consistency of banks’ internal credit ratings. In: Ong MK (ed) Credit Ratings: Methodologies, Rationale and Default Risk. Risk Books, London, pp 449–467

    Google Scholar 

  • Correa R, Goldberg L (2022) Bank complexity, governance, and risk. Journal of Banking and Finance 134

  • Covas F, Freedman A (2019) The gsib surcharge and repo markets. November 26, 2019. https://bpi.com/the-gsib-surcharge-and-repo-markets

  • De Genaro A, Salvador PI, Fernandes IF (2021) Market power and banking regulations: Evidence from rdd application to the brazilian banking market. Economics Letters 202

  • Degryse H, Mariathasan M, Tang TH (2023) Gsib status and corporate lending. Journal of Corporate Finance 80

  • Demos T (2019) Bank math adds up to possible december squeeze. Wall Street Journal. November 27. https://www.wsj.com/articles/bank-math-adds-up-to-possible-december-squeeze-11574854200

  • D’Errico M, Roukny T (2021) Compressing over-the-counter markets. Oper Res 69(6):1660–1679

    Article  Google Scholar 

  • Duffie D (2018) Financial regulatory reform after the crisis: An assessment. Manage Sci 64(10):4835–4857

    Article  Google Scholar 

  • Duffie D, Zhu H (2011) Does a central clearing counterparty reduce counterparty risk? Rev Asset Pric Stud 1(1):74–95

    Article  Google Scholar 

  • Egelhof J, Martin A, Zinsmeister N (2017) Regulatory incentives and quarter-end dynamics in the repo market. Liberty Street Economics blog post. August 7. https://libertystreeteconomics.newyorkfed.org/2017/08/regulatory-incentives-and-quarter-end-dynamics-in-the-repo-market.html

  • Ehlers T, Hardy B (2019) The evolution of otc interest rate derivatives markets. BIS Quarterly Review

  • Favara G, Ivanov I, Rezende M (2021) Gsib surcharges and bank lending: Evidence from u.s. corporate loan data. Journal of Financial Economics 142(3):1426–1443

  • Federal Register (2015) Regulatory capital rules: Implementation of risk-based capital surcharges for global systemically important bank holding companies. August 14, 2015. https://www.gpo.gov/fdsys/pkg/FR-2015-08-14/pdf/2015-18702.pdf

  • Federal Register (2020) Risk mitigation techniques for uncleared security-based swaps. February 4, 2020. https://www.govinfo.gov/content/pkg/FR-2020-02-04/pdf/2019-27762.pdf

  • Financial Stability Board (2018) Incentives to centrally clear over-the-counter (otc) derivatives. November 19, 2018. https://www.bis.org/publ/othp29.pdf

  • Financial Stability Board (2020) Evaluation of the effects of too-big-to-fail reforms: Consultation report. June 28, 2020. https://www.fsb.org/wp-content/uploads/P280620-1.pdf

  • Firestone S, Rezende M (2016) Are banks’ internal risk parameters consistent? evidence from syndicated loans. Journal of Financial Services Research 50(2):211–242

    Article  Google Scholar 

  • Garcia L, Lewrick U, Sečnik T (2023) Window dressing systemic importance: Evidence from eu banks and the g-sib framework. Journal of Financial Services Research 64(2):231–264

    Article  Google Scholar 

  • Goel T, Lewrick U, Mathur A (2019) Playing it safe: Global systemically important banks after the crisis. BIS Quarterly Review

  • Goldberg L, Meehl A (2020) Complexity in large u.s. banks. Federal Reserve Bank of New York Economic Policy Review 26(2):1–28

  • Greenwood R, Hanson SG, Stein JC, Sunderam A (2017) Strengthening and streamlining bank capital regulation. Brook Pap Econ Act 48(2):479–544

    Article  Google Scholar 

  • Griffiths MD, Winters DB (2005) The turn of the year in money markets: Tests of the risk-shifting window dressing and preferred habitat hypothesesn. J Bus 78(4):1337–1364

    Article  Google Scholar 

  • Hanson SG, Kashyap AK, Stein JC (2011) A macroprudential approach to financial regulation. Journal of Economic Perspectives 25(1):3–28

    Article  Google Scholar 

  • International Swaps and Derivatives Association (2015) The impact of compression on the interest rate derivatives market. Research Note, July. https://www.isda.org/2015/07/30/the-impact-of-compression-on-the-interest-rate-derivatives-market/

  • Jacobson T, Lindé J, Roszbach K (2006) Internal ratings systems, implied credit risk and the consistency of banks’ risk classification policies. J Bank Finance 30(7):1899–1926

    Article  Google Scholar 

  • Mariathasan M, Merrouche O (2014) The manipulation of basel risk-weights. Journal of Financial Intermediation 23(3):300–321

    Article  Google Scholar 

  • Munyan B (2017) Regulatory arbitrage in repo markets. working paper. http://www.bmunyan.com/5_31_2017--RegulatoryArbitrageinRepoMarkets.pdf

  • Noonan L, Rennison J (2019) Goldman and jpmorgan tweak repo operations to limit basel impact. Financial Times, December 29. https://www.ft.com/content/ac4cc418-243d-11ea-9a4f-963f0ec7e134

  • OSTTRA (2023) Portfolio compression. https://osttra.com/services/optimise/portfolio-compression/. Accessed on September 25, 2023

  • Passmore W, von Hafften AH (2018) Are basel’s capital surcharges for global systemically importantbanks too small? Int J Cent Bank 15(1):107–156

    Google Scholar 

  • Plosser M, Santos J (2018) Banks’ incentives and inconsistent risk models. Review of Financial Studies 31(6):2080–2112

    Article  Google Scholar 

  • Quarles RK (2018) Getting it right: Factors for tailoring supervision and regulation of large financial institutions. speech delivered at the American Bankers Association Summer Leadership Meeting, Salt Lake City, Utah, July 18. Available at https://www.federalreserve.gov/newsevents/speech/quarles20180718a.htm

  • U.S. Congress, Senate (2015) Measuring the systemic importance of u.s. bank holding companies. Hearing Before the Committee on Banking, Housing, and Urban Affairs, United States Senate, 114th. Congress, July 23

  • Violon A, Durant D, Toader O (2020) The impact of the identification of gsibs on their business model. Int J Cent Bank 16(5):95–142

    Google Scholar 

  • Woodall L, Osborn T (2017) Jp morgan urges clients to compress over g-sib fears. December 4, 2017. https://www.risk.net/risk-management/5368461/g-sib-clearing-proposal-sparks-compression-drive-at-jp-morgan

  • Yellen JL (2015) Opening statement by chair janet l. yellen. July 18. Available at https://www.federalreserve.gov/newsevents/press/bcreg/yellen-statement-20150720a1.htm

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Acknowledgements

We thank the editor Mark Carey, two anonymous referees, and seminar participants at the 2021 Luso-Brazilian Finance Meeting, the 2021 RiskLab/BoF/ESRB Conference on Systemic Risk Analytics, the 2021 Annual Meeting of the Central Bank Research Association, and the Federal Reserve Board for comments. We also thank Aaron Garner and Marco Taylhardat for excellent research assistance. The views expressed in this paper are those of the authors and not necessarily those of the Federal Reserve Board or research staff.

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    Jared Berry & Akber Khan

  2. Federal Reserve Board, Washington, 20551, DC, USA

    Marcelo Rezende

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Appendices

Appendix A U.S. GSIB Rule

In 2015, the FRB adopted a rule imposing risk-based capital surcharges on the largest and most interconnected U.S. banks. This rule requires that a bank whose measure of systemic importance exceeds a certain threshold be identified as a GSIB and be subject to a risk-based capital surcharge. The GSIB surcharge was introduced on January 1, 2016, became fully phased in on January 1, 2019, and is applied to three minimum capital requirements: the minimum CET 1 capital ratio, the minimum tier 1 capital ratio, and the minimum total capital ratio. Table 6 shows how the GSIB surchage was imposed on these three minimum capital requirements over time:

Table 6 Minimum Capital Requirements over Time. Capital requirements are valid from January 1 to December 31 of the respective year. CCB, GSIB, and CET 1 are acronyms for capital conservation buffer, global systemically important bank holding company, and common equity tier 1, respectively. “Full GSIB surch." is an abbreviation for fully phased-in GSIB surcharge
Full size table

The minimum required CET 1 capital ratio, tier 1 capital ratio, and total capital ratio were equal to 4.5 percent, 6.0 percent, and 8.0 percent in 2015, respectively. These three minimum capital requirements increased by one quarter of the sum of the capital conservation buffer—equal to 2.5 percentage points for all BHCs—and the fully phased-in GSIB surcharge—which currently varies from 0 to 3.5 percentage points across BHCs—each year from January 1, 2016, to January 1, 2019. A BHC that does not meet these requirements is subject to limitations on capital distributions and on certain discretionary bonus payments.

A BHC is identified as a GSIB if a measure of its systemic importance—the method 1 score—exceeds 130. The FRB set this cutoff at 130 for two reasons. First, estimates of this score from the Board indicated a large drop between the eighth-highest score (146) and the ninth-highest score (51), which would make it natural to locate the threshold between these two values. Second, this threshold aligns the U.S. rule with international standards and facilitates comparisons across jurisdictions, because the Basel Committee on Banking Supervision (BCBS) also uses a threshold equal to 130. In fact, method 1 is also adopted by the BCBS to determine whether a bank is a GSIB and to calculate its GSIB surcharge.

To calculate the method 1 score of a BHC, the FRB uses five broad categories of data correlated with systemic importance: size, interconnectedness, cross-jurisdictional activity, substitutability, and complexity.Footnote 22 Each of these five categories receives a weight of 20 percent in a BHC’s method 1 score. The contributions of these five categories are measured by the 12 indicators listed in Table 7.

Table 7 Systemic Indicator Weights for Method 1 Score
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These 12 indicators are multiplied by their respective weights, shown on the rightmost column of this table, and divided by the respective aggregate global measure of each indicator. Each aggregate global measure is provided annually by the FRB and is the sum of the respective indicator scores from the 75 largest U.S. and foreign banking organizations (as measured by the BCBS) and any other banking organizations that the BCBS decides to include in the sample for the respective year. Each aggregate global measure is converted from euros to U.S. dollars using the exchange rate observed on December 31 of the reference year provided by the BCBS. The 12 indicators are then summed and the total is the method 1 score of the GSIB. Because the method 1 score of a BHC depends on characteristics of other banks and on exchange rates, a BHC cannot accurately manipulate its method 1 score by changing its own characteristics.Footnote 23

The GSIB surcharge of a BHC is the higher of the method 1 and 2 surcharges. The method 1 fully phased-in GSIB surcharge of a BHC is determined by its method 1 score as follows:

Table 8 Method 1 Fully Phased-in GSIB Surcharge
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As shown in Table 8, the method 1 surcharge of a non-GSIB is equal to 0 and the surcharge of a GSIB is equal to 1 percent at least. This surcharge increases 0.5 percentage points for every 100 basis points in the method 1 score between 130 and 529 and 1 percentage point for every 100 basis points above 529. The larger impact of the score on the surcharge above a score of 529 provides a stronger incentive for GSIBs above this score to limit their systemic footprint.

The method 2 score of a BHC is equal to the 10 indicators multiplied by the fixed coefficients in Table 9.

Table 9 Systemic Indicator Weights for Method 2 Score
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The method 2 fully phased-in GSIB surcharge of a BHC depends on its method 2 score as described in Table 10:

Table 10 Method 2 Fully Phased-in GSIB Surcharge
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Table 11 Effects of GSIB Status on Systemic Importance Indicators. This table presents estimates of \(\theta _{4}\) from Eq. 2 without the term \(\sum _{s=1}^{4} \beta _{s} GSIB_{i} \times \mathbbm {1} \{s = q(t) \}\). Each observation is a bank-time pair. In columns 1 to 12, the dependent variable is the natural logarithm of the dollar amount of a systemic importance indicator, and the data range from the fourth quarter of 2013 to the fourth quarter of 2022. In column 13, the dependent variable is the short-term wholesale funding ratio, and the data range from the fourth quarter of 2016 to the fourth quarter of 2022. The data frequency is annual from 2013 to 2015 (collected in the fourth quarter) and quarterly from the second quarter of 2016 on. All specifications include as independent variables total assets, total capital ratio, tier-1 capital ratio, leverage ratio, return on assets, return on equity, net interest margin, delinquency ratio, and charge off ratio. All specifications also include bank and time fixed effects. Standard errors are clustered at the bank level
Full size table
Table 12 Effects of GSIB Status on Systemic Importance Indicators - 2019 to 2022. This table presents estimates of \(\theta _{4}\) from Eq. 2 without the term \(\sum _{s=1}^{4} \beta _{s} GSIB_{i} \times \mathbbm {1} \{s = q(t) \}\). Each observation is a bank-time pair. In columns 1 to 12, the dependent variable is the natural logarithm of the dollar amount of a systemic importance indicator, and the data range from the fourth quarter of 2013 to the fourth quarter of 2022. In column 13, the dependent variable is the short-term wholesale funding ratio, and the data range from the fourth quarter of 2016 to the fourth quarter of 2022. The data frequency is annual from 2013 to 2015 (collected in the fourth quarter) and quarterly from the second quarter of 2016 on. All specifications include as independent variables total assets, total capital ratio, tier-1 capital ratio, leverage ratio, return on assets, return on equity, net interest margin, delinquency ratio, and charge off ratio. All specifications also include bank and time fixed effects. Standard errors are clustered at the bank level
Full size table

The GSIB surcharge that a BHC is subject to from January 1 to December 31 of year t must be calculated by December 31 of year \(t-1\) using data as of December 31 of year \(t-2\). Also, after the initial GSIB surcharge of a BHC is in effect, if the fully phased-in surcharge of this BHC increases, the higher surcharge is applied only two years after the measurement of the systemic indicators. However, if the fully phased-in surcharge decreases, then it becomes effective in the next calendar year.

Appendix B Additional regression estimates

This appendix contains tables with the full set of estimates summarized in Table 3.

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Berry, J., Khan, A. & Rezende, M. How Do Global Systemically Important Banks Lower Capital Surcharges?. J Financ Serv Res (2024). https://doi.org/10.1007/s10693-024-00426-w

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