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Trading Activities at Systemically Important Banks, Part 3: What Drives Trading Performance?, Accessible Data
Figure 1: Trading revenue for the six largest domestic banks
This figure shows the annual combined trading revenue for Bank of America, Citigroup, Goldman Sachs, J.P. Morgan Chase, Morgan Stanley, and Wells Fargo, in billions of dollars, from 2004 to 2016. The x-axis labels are the years. The Y-axis ranges from -60 billion dollars to 100 billion dollars. Combined trading revenue for these banks ramped up from 20 billion dollars in 2004 to about 50 billion dollars in 2005. It dropped dramatically to 10 billion dollars in 2007, before moving to -40 billion dollars in 2008. Since 2009, trading revenue has fluctuated between 40 to 60 billion dollars.
Figure 2: Relative contributions of drivers of credit and equities trading performance
This figure compromises of two times-series stacked bar charts that display the contributions of various statistically significant regressors to predicted credit and equities VaR-adjusted trading revenue at the weekly frequency. The x-axis labels are months in 2016. The y-axis measures VaR-adjusted trading revenue. The chart on the top shows the attribution for credit VaR-adjusted trading revenue with the y-axis ranging from -.25 to .75. Corporate bond volume has a stable contribution to predicted credit VaR-adjusted trading revenue that ranges from .2 to .25. Investment grade bid-ask spread contributes .05 to .1 to trading revenue, while high yield bid-ask spread presents a stable drag that ranges from -.1 to -.2. The contributions of other regressors are small in comparison.
The chart on the bottom shows the attribution for equities VaR-adjusted trading revenue with the y-axis ranging from -.25 to 1.75. The contribution of S&P 1500 bid-ask spread is by far the most dominant, and ranges from .4 to .5. The contributions of other regressors are small in comparison.
Figure 3: Relative contributions of drivers of foreign exchange and rates trading performance
This figure compromises of two time-series stacked bar charts that display the contributions of various statistically significant regressors to predicted rates and foreign exchange VaR-adjusted trading revenue at the weekly frequency. The x-axis labels are months in 2016. The y-axis measures VaR-adjusted trading revenue. The chart on the top shows the attribution for rates VaR-adjusted trading revenue with the y-axis ranging from -.25 to 1. Interest rate derivatives volume has a fairly stable contribution to predicted rates VaR-adjusted trading revenue that ranges from .1 to .2. The contributions of the change in the 10-year Treasury yield is more volatile without an obvious pattern, and ranges between -.05 to .15.
The chart on the bottom shows the attribution for foreign exchange VaR-adjusted trading revenue with the y-axis ranging from -.5 to 1. Advanced economy currency futures option interests has a stable contribution to predicted foreign exchange VaR-adjusted trading revenue that ranges from .2 to .25. The contributions of changes in the yen/dollar exchange rate fluctuates from -.25 to .15, while that of changes in the euro/dollar exchange rate also make small contributions. The contribution of changes in the currency VIX, the only other regressor, is small in comparison.