Accessible Version
Trading Activities at Systemically Important Banks, Part 2: What Happened during Recent Risk Events?, Accessible Data
Figure 1: Asset price moves surrounding key risk events since 2013
This figure shows the one-day changes in asset prices around four separately identified events: the "taper tantrum" (June 19th, 2013), the devaluation of the Swiss Franc (January 1st, 2015), the devaluation of the Chinese Renminbi (August 24th, 2015), and Brexit (June 24th, 2016). The x-axis on each chart marks three days before the event and three days after the event, with a vertical event line drawn at day zero. The y-axis is either in basis points of percent.
The top panel shows one-day changes in the 10-year treasury yield around the events. The yield decreases by about 15 basis points on Brexit and by about seven basis points on the SNB devaluation day. The yield did not move much on the RMB devaluation day, although it spiked by 15 basis points one day after the event. During the taper tantrum, the yield spikes by around 15 basis points.
The middle panel shows the one-day change in the S&P 500 index in percent, with the y-axis ranging from -5 percent to 5 percent. On the day of the day chosen for the taper tantrum, the S&P 500 fell by more than 1 percent. On the day of the SNB devaluation, the S&P 500 also fell nearly 1 percent. On the day of the RMB devaluation, the S&P fell by almost 4 percent. On the day of Brexit, the S&P fell by about 3.5 percent.
The bottom panel shows the one-day change in the CDX Investment-Grade Index in basis points, with the y-axis ranging from -8 basis points to 16 basis points. On the day of each event, there is a spike in the CDX IG index. On the day of the taper tantrum, the CDX index rose by about 5 basis points. On the day of the SNB devaluation, the CDX index rose by about 2 basis points. On the day of the RMB devaluation, the CDX index rose by about 6 basis points. On the day of Brexit the CDX index rose by almost 12 basis points.
Figure 2: Percent of banks with unusually good or poor trading revenue following events
This figure shows the percentage of banks experienced unusually large trading profits or losses on six event days. Each bar in this chart summarizes trading revenue across banks after an event day, and stacks up to 100 percent. The components of each bar represent the percentage of banks that experienced trading revenue above the 95th percentile of their respective historical distributions, the percentage of banks that experienced trading revenue below the 5th percentile of their respective historical distributions, and the percentage of banks that fall into neither categories.
The top panel shows revenue trading revenue on each event day. The x-axis identified each event which includes the taper tantrum (June 19th, 2013), the flash rally (October 15th, 2014), SNB devaluation (January 1st, 2015), RMB devaluation (August 24th, 2015), Brexit (June 24th, 2016), and the U.S. election (November 9th, 2016). On the day chosen for the taper tantrum, no banks experienced trading revenue below the 5th percentile and 8 percent experienced trading revenue above the 95th percentile. On the flash rally, 46 percent of banks experienced trading revenue below the 5th percentile and no banks experienced trading revenue above the 95th percentile. On the day of the SNB devaluation, 25 percent of banks experienced trading revenue below the 5th percentile and 16 percent of banks experienced trading revenue above the 95th percentile. On the day of the RMB devaluation, 23 percent of banks experienced trading revenue below the 5th percentile, and 8 percent of banks experienced trading revenue above the 95th percentile. On the day after Brexit, no banks experienced trading revenue below the 5th percentile, and 33 percent of banks experienced trading revenue above the 95th percentile. On the day after the U.S. election, no banks experienced trading revenue below the 5th percentile and 10 percent of banks experienced trading revenue above the 95th percentile.
The bottom panel is the same as the top panel, but instead of summarizing the trading revenue on the day after the event, it summarizes trading revenue over the week after the event. The distribution of each bank's trading revenue is computed using weekly data. In the week after the chosen date for the taper tantrum, 8 percent of banks experienced weekly trading revenue below the 5th percentile and 8 percent experienced trading revenue above the 95th percentile. In the week following the flash rally, 8 percent of banks experienced trading revenue below the 5th percentile and no banks experienced trading revenue above the 95th percentile. In the week after the SNB devaluation, no banks experienced trading revenue below the 5th percentile and 15 percent of banks experienced trading revenue above the 95th percentile. In the week of the RMB devaluation, 15 percent of banks experienced trading revenue below the 5th percentile and no banks experienced trading revenue above the 95th percentile. In the week after Brexit, no banks experienced trading revenue below the 5th percentile and 15 percent of banks experienced trading revenue above the 95th percentile. In the week after the U.S. election, 8 percent of banks experienced trading revenue below the 5th percentile and no banks experienced trading revenue above the 95th percentile.
Figure 3: Pooled distributions of VaR-adjusted trading revenue on large move days
This figure shows distributions of VaR-adjusted trading revenue for the total trading as well as those for credit and equities trading. Each distribution is smoothed density estimate applied to the pooled (across banks and time) observations of VaR-adjusted trading revenue; the smoothing is based on a Gaussian kernel with the bandwidth selected by the Silverman's Rule of Thumb. Each panel shows separate pooled distribution for the 50 days with the largest S&P 500 declines, the 50 days with the largest 10-year Treasury yield increases, and the 50 days with the largest CDX IG increases. Some trading days included in these 50-day distributions overlap. In each panel, the x-axis shows the values of VaR-adjusted P&L ranging from -3 to 3, and the y-axis shows the kernel density ranging from 0 to 2.25; the area below the curve integrates to 1.
The top panel shows the distribution of total VaR-adjusted trading revenue. On days without large market moves, the distribution is centered at .5, with almost all the mass between -.5 and 2 on the x-axis. The density peaks at around 1. The distribution of total VaR-adjusted trading revenue on days with large S&P 500 declines is centered slightly to the left of that on days without large moves, although it also has fatter tails, with the right tail exhibiting a slight kink at values above 1.2 on the x-axis. The distribution on days with large CDX IG increases is very similar to that on days with large S&P 500 declines. The distribution on days of large 10-year treasury yield increases is wider that on days without large moves. This distribution peaks at a noticeably lower point of about .75, and appears to have somewhat fatter tails, particularly the right tail.
The middle panel shows the distribution of credit VaR-adjusted trading revenue. On days without large market moves, the distribution is centered at .3, with almost all the mass between -1 and 2 on the x-axis. The density peaks at around 1.75. The distribution of credit VaR-adjusted trading revenue on days with large S&P 500 declines exhibits more irregular left tails relative to that on days without large moves, with some mass between -2 to -1. The distribution on days with large CDX IG increases is very similar to that on days with large S&P 500 declines. The distribution on days of large 10-year treasury yield increases is quite similar to that on days without large price moves.
The bottom panel shows the distributions of equities VaR-adjusted trading revenue. On days without large market moves, the distribution is centered at .5, with almost all the mass between -.5 and 2 on the x-axis. The density peaks at around 1. The distribution of equities VaR-adjusted trading revenue on days with large S&P 500 declines is centered to the left of that on days without large moves, although it also has fatter tails, with the left tail noticeably thicker, which indicates instances of trading losses. The distribution on days with large CDX IG increases is very similar to that on days with large S&P 500 declines. The distribution on days of large 10-year treasury yield increases is quite similar to that on days without large moves, and slightly to the right.