Regulation and De-Risking: Theoretical and Empirical Insights
Abstract
:1. Introduction
2. Literature, Research Issues and Hypotheses
2.1. Why Regulation
- How should capital be defined?
- How much capital is sufficient?
- How liquid should capital be?
- Should there be a maximum level of leverage?
- Against which risks capital should be held?
- Should capital requirements be a percentage of face value or be risk weighted?
- How should systemic issues such as pro-cyclicality be addressed?
2.2. Theoretical Perspectives
2.3. Empirical Research
3. Method and Research Hypotheses
4. Data and Descriptive Statistics
5. Statistical Analysis and Interpretation of Results
6. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
1 |
|
2 | In focusing on VaR, our concern is market risk as regulated under Basel-inspired regulations. Other types of risk such as Credit Risk and Operational risk also have regulatory capital requirements but are not examined in our research. Arguably, Market Risk is the most identifiable with systematic risk, as per the Capital Asset Pricing Model. |
3 | See Note 9 concerning Silicon Valley Bank. |
4 | The work of Kaplanski and Levy (2015) is distinct in positing more than one CML, i.e., one with and without regulatory impositions. While intriguing, we ask, how can there be more than one Capital Market Line? Moreover, in their work, a constraint is imposed upon VaR, which is curious. Subject to model approval, VaR is not regulated; rather, the amount of capital required to support a level of VaR is regulated. |
5 | We do not discuss the optimality of aligning private benefits with social costs through other means such as restricting the quantity of asset creation and lending by banks. How regulation has affected the nature and scope of credit creation are beyond the scope of the present research. |
6 | We examine the impact of the various Basel accords regulating the inputs to the creation of market risk through trading. As per the previous Note 5, examining the creation of credit risk and even examining how the two types of risk are jointly affected by regulation may be an area for future research. |
7 | See Note 1. |
8 | In contrast, according to the option valuation theory, unanticipated redistributions to wealth are possible, see (Copeland and Weston 1992, pp. 507–9). |
9 | According to some experts, the recent protection of large deposits of the non-systemically important Silicon Valley Bank in the USA is yet another example of moral hazard and lax oversight (Wall Street Journal, https://www.wsj.com/articles/did-esg-help-sink-svb-progressive-climate-bank-bailout-federal-reserve-treasury-biden-insurance-9db64b0b (accessed on 21 September 2021). |
10 | Non-linearity of systemic risk means that the “macro-prudential” exposure facing the economy may exceed the aggregate risks facing individual institutions. |
11 | We follow the approach found in chapter 8 of Mathematical Optimisation and Economic Theory by Michael D. Intriligator, Philadelphia, USA: Society for Industrial and Applied Mathematics (Intriligator 1971). |
12 | We assume that a bank is a “price-taker” with regard to the cost of debt and equity according to its capital structure. |
13 | We note that banks are required by their regulators to report VaR along with related statistics. Though it has been argued that banks may have an incentive to under-estimate their exposure, according to research, the opposite is possible as well as diversification benefits are under-estimated (Pérignon et al. 2008). Notwithstanding such concerns, as officially reported metrics, we assume the validity of the reported VaR. |
14 | Although the various major financial centres where these banks are head-quartered may have implemented the Basel Accords in not entirely consistent manners hypothetically leaving scope for regulatory arbitrage, all these banks operate globally. The major regulators moreover regularly compare how their respective institutions and non-headquartered institutions measure their risk exposures. |
15 | Adjusted Beta vs. Raw Beta—The beta of a share may be presented as either an Adjusted Beta or a Raw Beta. A Raw Beta is obtained from the linear regression of a stock’s historical data. Raw Beta, also known as Historical Beta, is based on the observed relationship between the security’s return and the returns of an index. The Adjusted Beta is an estimate of a security’s future Beta. Adjusted Beta is derived from historical data but modified by the assumption that a security’s true Beta will move towards the market average, of 1. The formula used to adjust Beta is (0.67) × Raw Beta + (0.33) × 1.0. All Betas are computed using the relevant markets-exchanges on which they trade. |
16 | The regulatory changes moving from Basel I to II to III are well known but importantly with the last phase, there has been a greater focus on the quality of capital and its liquidity reflecting the lessons of the 2008 financial crisis. |
17 | ). |
18 | To address the inherent funding risk of banks, creating illiquid assets from liquid deposits, two new liquidity constraints were introduced under Basel III: The Liquidity Coverage Ratio (LCR) to promote the short-term resilience of banks and the Net Stable Funding Ratio (NSFR) to incentivise a stable and reliable source of funds. Compared to capital regulation, there has been less theoretical and empirical investigation of their respective impacts. To the extent that the two ratios encourage banks to behave differently than they would otherwise, holding more liquid assets for stress events, there may be a cost in impeding the transformation function but measuring the impact is difficult (Elliot et al. 2012). According to research on the EU’s largest banks, both the LCR and the NSFR increased capital requirements by reducing bank fragility (Chiaramonte and Casub 2017). Both ratios may lead to holding greater capital, but this may have occurred for other reasons as well. If Liquidity Regulation makes banks safer, then the cost of both debt and equity should fall if everything else were equal…but it is not. Assessing the impact of these two ratios hinges on whether the cost of capital is appropriate to its risks both individually as well as for the sector, but controlling for other effects would be difficult. |
19 | While acknowledging the many limitations of VaR, we utilise it as a widely accepted summary measure of market risk (See, Lesnevski et al. (2007)). Its identification, measurement, reporting and management are regulatory requirements. Credit risk and Operational Risk while also falling within the Basel agenda are not examined in this research. |
20 | Some smaller but prominent institutions, such as Standard & Chartered, through their antecedents have been involved in edible oils and minerals on behalf of their clients going back to the 19th Century. The origins of Rabo Bank of the Netherlands Bank began with agricultural credit unions. |
21 | As reported by Consultancy Coalition, the commodity trading performance of the top 12 banks improved somewhat in 2019 through involvement in metals and petroleum markets (Reuters, 21 February 2020. https://www.reuters.com/article/banks-commodities-revenue/top-banks-2019-commodities-revenue-climbs-11-consultancy-coalition-idUSL8N2AL2R4, accessed on 3 March 2020). |
22 | This situation is unlikely as banks tend to set position limits according to VaR. If VaR were to fall because of a secular reduction in volatility, permitted positions might be increased. VaR is regulated by stipulating the required capital. |
23 | Further, the risk of bankruptcy is not rewarded by higher returns with the CAPM framework (Dichev 1998). |
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Ticker | Full Name | Ticker | Full Name |
---|---|---|---|
JPM | JP Morgan | PNC | PNC FINANCIAL SERVICES GROUP |
BAC | Bank of America | BNP | BNP Paribas |
RY | Royal Bank of Canada | CM | Canadian Imperial Bank of Commerce |
TD | Toronto Dominion Bank | ISP | Intesa Sanpaolo SpA |
WFC | Wells Fargo Bank | INGA | ING Groep NV |
C | Citibank | ACA | Credit Agricole |
BNS | Bank of Nova Scotia | FRC | First Republic Bank |
HSBA | HSBC Holdings | NA | National Bank of Canada |
TFC | Truist Financial Corp. | LLOY | Lloyds Banking Group PLC |
USB | U.S. Bancorp | BARC | Barclays PLC |
BMO | Bank of Montreal |
Data Series and Frequency | |
---|---|
Alpha | Daily |
Adjusted Beta | Daily |
Cost of Debt | Quarterly |
Cost of Equity | Quarterly |
Debt to Equity Ratio | Quarterly |
Return on Equity | Quarterly |
VaR by Asset Classes | Quarterly |
Weighted Average Cost of Capital | Quarterly |
Financial Statistics for Selected Banks | ||||||
---|---|---|---|---|---|---|
Metric | Descriptive Statistics | 2000 to 2020 | Pre-Basel II 2000 to 2009 | Basel II to Basel III Transition 2009 to 2016 | Basel III Post 2016 | Percent Change Pre-Basel II to Post Basel III |
Alpha | Mean | 0.0001 | 0.0003 | 0.0002 | −0.0002 | |
Stdev. | 0.0010 | 0.0009 | 0.0012 | 0.0007 | ||
Adjusted Beta | Mean | 1.08 | 0.98 | 1.21 | 1.07 | 8.87% |
Stdev. | 0.26 | 0.21 | 0.31 | 0.16 | ||
Cost of Debt | Mean | 2.21% | 3.52% | 1.55% | 1.24% | −104.49% |
Stdev. | 1.38 | 1.16 | 0.74 | 0.80 | ||
Cost of Equity | Mean | 11.62% | 9.43% | 13.88% | 11.48% | 19.67% |
Stdev. | 3.58 | 1.97 | 4.01 | 2.56 | ||
Debt to Equity Ratio | Mean | 3.31 | 4.05 | 3.14 | 2.24 | −59.25% |
Stdev. | 3.61 | 4.63 | 3.02 | 1.29 | ||
Return on Equity | Mean | 11.28% | 14.23% | 9.17% | 9.98% | −35.48% |
Stdev. | 8.25 | 8.59 | 8.26 | 6.06 | ||
WACC | Mean | 5.02 | 4.44 | 5.59 | 5.20 | 15.66% |
Stdev. | 2.73 | 2.49 | 3.08 | 2.41 |
Fall in WACC 2000 versus 2020 | |||||
---|---|---|---|---|---|
BANK | BANK | BANK | |||
JPM | −34% | HSBA | −108% | ISP | −62% |
BAC | −48% | TFC | −6% | INGA | −123% |
RY | 17% | USB | −26% | ACA | −261% |
TD | 9% | BMO | 13% | FRC | −54% |
WFC | −67% | PNC | −5% | NA | 27% |
C | −82% | BNP | −274% | LLOY | 47% |
BNS | 7% | CM | −3% | BARC | −187% |
Summary Statistics on VaR for Selected Banks | ||||||
---|---|---|---|---|---|---|
Metric | Descriptive Statistics | 2000 to 2020 | Pre-Basel II 2000 to 2009 | Basel II to Basel III Transition 2009 to 2016 | Basel III Post 2016 | Percentage Change Pre-Basel II versus Post Basel III |
Total VaR (USD, Millions) | Mean | 66.22 | 72.84 | 72.11 | 51.56 | −35% |
Total VaR (USD, Millions) | Stdev. | 61.87 | 68.40 | 65.54 | 36.41 | −63% |
Rate VaR (USD, Millions) | Mean | 51.47 | 75.26 | 48.93 | 36.01 | −74% |
Rate VaR (USD, Millions) | Stdev. | 56.08 | 69.13 | 58.71 | 32.38 | −76% |
Equity VaR (USD, Millions) | Mean | 19.26 | 30.24 | 18.99 | 12.49 | −88% |
Equity VaR (USD, Millions) | Stdev. | 18.15 | 23.16 | 18.23 | 8.59 | −99% |
FX VaR (Millions) | Mean | 11.43 | 14.77 | 13.80 | 7.13 | −73% |
FX VaR (Millions) | Stdev. | 13.14 | 13.54 | 16.27 | 6.83 | −68% |
Commodity VaR (Millions) | Mean | 9.03 | 13.04 | 8.64 | 6.50 | −70% |
Commodity VaR (Millions) | Stdev. | 9.61 | 12.30 | 8.41 | 7.50 | −49% |
Z-Test for Difference in Mean WACC and Mean VaR during Pre-Basel II, II to III Transition and Post Basel III Periods | |||
---|---|---|---|
Changes in WACC Z-Test for Sample of Two Means | One-Tail Z Value | P(Z ≤ z) One-Tail | Z Critical One-Tail 95% |
WACC: Pre Basel II vs. Transition II to III | −2.6743 | 0.0037 | 1.6449 |
WACC: Pre Basel II vs. Post Basel III | −1.7447 | 0.0405 | 1.6449 |
WACC: Transition Basel II to III vs. Post Basel III | 0.8000 | 0.2119 | 1.6449 |
VaR: Pre Basel II vs. Transition | −2.8335 | 0.0023 | 1.6449 |
VaR: Pre Basel II vs. Post Basel III | 10.5448 | 0.0000 | 1.6449 |
VaR: Transition Basel II to III vs. Post Basel III | 12.7669 | 0.0000 | 1.6449 |
Bank | Observations | Coefficient on WACC | T-Statistic on WACC | Coefficient on D/E | T-Statistic on D/E | Left-Tail T-Distribution WACC | Left-Tail T-Distribution D/E | Standard Error Y-Estimate | p-Value on WACC | p-Value on D/E | WACC | D/E | F-Statistic | F-Significance | Adjusted R-Squared | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Lower 95% | Upper 95% | Lower 95% | Upper 95% | ||||||||||||||
JPM | 79 | 3.80 | 1.39 | 17.63 | 5.94 | 80.11% | 94.69% | 52.49 | 0.1695 | 2.585 × 10−08 | −1.66 | 9.27 | 11.72 | 23.54 | 129.19 | 3.546 × 10−25 | 75.44% |
BAC | 80 | −5.78 | −1.58 | 29.07 | 6.19 | 82.00% | 94.90% | 52.68 | 0.1191 | 2.585 × 10−08 | −13.08 | 1.52 | 19.72 | 38.41 | 91.44 | 4.586 × 10−21 | 68.44% |
TD | 39 | −1.20 | −1.44 | 17.00 | 4.93 | 80.72% | 93.64% | 8.17 | 0.1572 | 1.729 × 10−05 | −2.89 | 0.49 | 10.02 | 38.41 | 158.82 | 1.379 × 10−18 | 86.58% |
WFC | 68 | excluded | n.a. | 13.66 | 7.14 | n.a. | 95.57% | 36.53 | n.a. | 8.664 × 10−10 | n.a. | n.a. | 9.84 | 17.49 | 50.91 | 9.298 × 10−10 | 41.69% |
CITI | 79 | 4.62 | 1.52 | 19.35 | 7.88 | 81.48% | 95.98% | 57.19 | 0.1326 | 1.744 × 10−11 | −1.43 | 10.68 | 14.46 | 24.24 | 197.13 | 8.359 × 10−31 | 82.15% |
PNC | 49 | excluded | n.a. | 3.17 | 14.50 | n.a. | 97.81% | 2.66 | n.a. | 3.611 × 10−19 | n.a. | n.a. | 2.73 | 3.60 | 210.37 | 5.643 × 10−19 | 79.34% |
BNP | 57 | excluded | n.a. | 3.14 | 16.53 | n.a. | 98.08% | 18.61 | n.a. | 3.345 × 10−23 | n.a. | n.a. | 2.76 | 3.52 | 273.22 | 5.412 × 10−23 | 81.20% |
ACA | 55 | 13.56 | 12.97 | excluded | n.a. | 97.55% | n.a. | 18.52 | 3 × 10−18 | n.a. | 11.46 | 15.65 | n.a. | n.a. | 168.21 | 4.476 × 10−18 | 73.85% |
Average 30 Volatility during Basel Implementation Periods | |||||||||
---|---|---|---|---|---|---|---|---|---|
Periods | Commodity Index | Euro Index | USD Index | FTSE 100 | Nasdaq Index | Dow Jones Industrial Index | Nikkei Index | US Treasury Index | EU Rate Index |
Pre Basel II-2000 to 2009 | 16.41 | 5.28 | 9.31 | 17.51 | 31.03 | 7.05 | 23.39 | 4.73 | 3.00 |
Basel II to Basel III Transition 2009 to 2016 | 15.69 | 7.17 | 10.16 | 17.34 | 18.93 | 6.01 | 22.29 | 4.23 | 3.01 |
Basel III Post 2016 | 12.39 | 5.29 | 8.31 | 15.13 | 19.01 | 5.50 | 17.62 | 3.89 | 2.85 |
Percent Change First and Last Periods | −28% | 0% | −11% | 15% | −49% | −10% | −28% | −20% | −5% |
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Haar, L.; Gregoriou, A. Regulation and De-Risking: Theoretical and Empirical Insights. Risks 2023, 11, 104. https://doi.org/10.3390/risks11060104
Haar L, Gregoriou A. Regulation and De-Risking: Theoretical and Empirical Insights. Risks. 2023; 11(6):104. https://doi.org/10.3390/risks11060104
Chicago/Turabian StyleHaar, Lawrence, and Andros Gregoriou. 2023. "Regulation and De-Risking: Theoretical and Empirical Insights" Risks 11, no. 6: 104. https://doi.org/10.3390/risks11060104