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Banks' Backtesting Exceptions during the COVID-19 Crash: Causes and Consequences, Accessible Data
Figure 1: Exception counts and risk factor movements
Figure 1 aggregates exceptions across all banks in our sample, separately highlighting systemic and non-systemic banks. Across both types of banks, the number of backtesting exceptions jumped around the beginning of March 2020 and remained elevated until close to the end of the month. The figure superimposes percentage changes in the S&P 500 and the VIX, showing that the backtesting exceptions occurred amid high realized stock market volatility.
Note: Key identifies from top to bottom. Shaded gray area represents the backtesting exceptions window from March 6, 2020, to March 27, 2020, explained in section 4.
Source: Supervisory data from banks and Bloomberg. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee.
Figure 2: Systemic banks' losses on weeks of exceptions by LoB
Figure 2 aggregates LoB hypothetical losses associated with BHC-level exceptions on a weekly basis during late-February and late-March 2020. It shows the largest losses for the week of March 9, followed by smaller, but still substantial, losses in the weeks of March 16 and 23. First, for the week of March 9, credit and rates LoBs have the largest losses. Next, for the week of March 16, all LoBs have similar losses. And finally, for the week of March 23, the credit and other LoBs have the largest losses.
Note: Key identifies from top to bottom.
Source: Supervisory data from 9 systemic banks. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee. Categories appear in order according to legend. Feb 24 mostly shows Equity; Mar 02 only shows Rates and Credit; and Mar 30 shows Other, Equity, and Credit.
Figure 3: Three largest ratios of risk factor movements during COVID to those during the Great Financial Crisis for systemic banks on exception dates
Figure 3 shows the largest 3 relative move ratios across all high-level MRFs per day and LoB during dates systemic banks incurred exceptions throughout the COVID period of March 2020. All moves above the dotted line representing a one indicates that the MRF move during the COVID period was greater than in the GFC. Ratios associated with Credit had the largest moves followed by Equity and Rates.
Source: Supervisory data from 9 systemic banks. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee.
Figure 4: Trading across all desks in 2020 Q1
Figure 4 shows in Panel A the breakdown of Actual P&L into new position P&L and Hypothetical P&L on existing positions new position P&L and Panel B shows the trading transaction volumes during 2020 Q1. This sample consists of systemic banks that report the Volcker Metrics which is slightly different than previous data and thus includes supervisory data from 8 systemic banks. Even on the most turbulent days in 2020 Q1, the figure shows that new positions' profits and losses largely offset hypothetical losses on existing positions and that trading volume increased during the COVID crash.
Source: Volcker Rule Quantitative Measurements (FR VV-1). Supervisory data from 8 systemic banks, since one of the 9 banks does not submit FR VV-1. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee.
Figure 5: 2020 Q1 – Largest three ratios of COVID to Great Financial Crisis risk factor movements for systemic banks
Figure 5 shows the largest 3 relative move ratios across all high-level MRFs per day and LoB during all 2020 Q1 dates. It shows that during the COVID crash in March 2020, there were many moves that were higher than the GFC moves. In particular, Equity had the largest moves with many being above 5 times the GFC moves whereas commodities had the lowest relative moves staying at around the same magnitude as the GFC.
Source: Supervisory data from 9 systemic banks. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee.
Figure 6: Commodities LoB – Largest three ratios of COVID to Great Financial Crisis risk factor movements for systemic banks on exception dates
Figure 6 shows the largest 3 relative move ratios across Commodities high-level MRFs per day during dates systemic banks incurred exceptions throughout the COVID period of March 2020. It illustrates that only seven moves that contributed to exceptions were larger than the GFC moves, making Commodities MRFs the fewest drivers of exceptions.
Source: Supervisory data from 9 systemic banks. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee.
Figure 7: FX LoB – Largest three ratios of COVID to Great Financial Crisis risk factor movements for systemic banks on exception dates
Figure 7 shows the largest 3 relative move ratios across FX high-level MRFs per day during dates systemic banks incurred exceptions throughout the COVID period of March 2020. It illustrates that in the second and third week of March, FX MRF that were associated with firm exceptions experienced moves larger than in the GFC.
Source: Supervisory data from 9 systemic banks. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee.
Figure 8: Credit LoB – Largest three ratios of COVID to Great Financial Crisis risk factor movements for systemic banks on exception dates
Figure 8 shows the largest 3 relative move ratios across Credit high-level MRFs per day during dates systemic banks incurred exceptions throughout the COVID period of March 2020. It illustrates that Credit MRF that were associated with firm exceptions experienced larger moves than in the GFC and in particular the largest were in the second and third week of March.
Source: Supervisory data from 9 systemic banks. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee.
Figure 9: Securitization LoB – Largest three ratios of COVID to Great Financial Crisis risk factor movements for systemic banks on exception dates
Figure 9 shows the largest 3 relative move ratios across Securitization high-level MRFs per day during dates systemic banks incurred exceptions throughout the COVID period of March 2020. Most of these MRF experienced movements larger than in the GFC.
Source: Supervisory data from 9 systemic banks. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee.
Figure 10: Rates LoB – Largest three ratios of COVID to Great Financial Crisis risk factor movements for systemic banks on exception dates
Figure 10 shows the largest 3 relative move ratios across Rates high-level MRFs per day during dates systemic banks incurred exceptions throughout the COVID period of March 2020. It shows that the largest move occurred on March 10 with about 9 times more than in the GFC period. Most large moves are concentrated in the second and third week, followed with lower moves at the end of the month.
Source: Supervisory data from 9 systemic banks. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee.
Figure 11: Equity LoB – Largest three ratios of COVID to Great Financial Crisis risk factor movements for systemic banks on exception dates
Figure 11 shows the largest 3 relative move ratios across Equity high-level MRFs per day during dates systemic banks incurred exceptions throughout the COVID period of March 2020. The largest move occurred during March 13 and 24 but other smaller yet greater than the GFC moves occurred throughout the month. The figure also shows our previous example were the relative move ratio for the VIX of 3.6 was the highest daily COVID/GFC market move ratio on March 12.
Source: Supervisory data from 9 systemic banks. Systemic banks are defined as those that are supervised by the Large Institution Supervision Coordinating Committee.
Figure 12: Portfolio proxy exceedances per model specification
Figure 12 shows the 2020 Q1 time series of various models daily forecasted VaR and the actual returns. It highlights how many of those returns would be considered an exception under each of the six models for the portfolio proxy. Portfolio proxy is calculated as an equal-weighted average of the S&P 500 Index, S&P U.S. Treasury Bond Total Return Index, and the Bloomberg Barclays U.S. Corporate Investment Grade (USD) Unhedged returns. Panels A and B show historical simulation and panels C through F GARCH (1,1) Model VaR with different parameters and data lags. These exceptions are further described and summarized in Table 2.
Source: Bloomberg and staff calculations.
Note: Black line represents the daily forecasted VaR and red points refer to returns lower than VaR. Portfolio proxy is calculated as an equal-weighted average of the S&P 500 Index, S&P U.S. Treasury Bond Total Return Index, and the Bloomberg Barclays U.S. Corporate Investment Grade (USD) Unhedged returns.
Figure 13: Selected market risk factors
Figure 13 shows time series data from 2006 to 2020 Q1 for selected representative benchmark risk factors across different asset classes. Panel A illustrates 2-year, 10-year, and 30-year U.S. Treasury Yield Curve Rates showing a steep decline in 2020 Q1. Panel B shows Euro Stoxx 50, NASDAQ and S&P 500 Index data also experiencing a sharp decline in 2020 Q1. Panel C shows the VIX Index reached levels as high as the GFC, and Panel D shows Markit's CDX High Yield and Investment Grade 5-year spreads increased substantially.
Source: Bloomberg and IHS Markit.