It is easy to manipulate risk forecasts. If your regulator or compliance officer sets a risk target you don’t like, just tell them what they want to hear and continue taking the risk you like.
Suppose my job is to forecast the risk of some portfolio, measured by VaR. One day my boss calls me into her office:
“Jon, the VaR is too high, and you have to make it lower. However, our traders are doing a fantastic job, and I want to keep the nice returns. Now go and play with your computers and make it happen. And by the way, don’t
get caught make sure you comply with all regulations.”
The easiest way to manipulate the risk measurements is to pick a riskometer that delivers a lower number. On my website, extremerisk.org, I forecast risk every day, using the most commonly used techniques.
Let’s focus on the SP-500 index and suppose we have a $1000 portfolio.
The following figure shows today’s risk forecast from six of the most commonly used riskometers.
So if you want to minimize risk, just pick MA, which gives $46.5, and not tGARCH at $86.7.
OK, I can imagine the objections, there is no guarantee that MA will be lowest in the future. So, let’s look at the average for the past year, and if anything, the difference is even bigger.
So by just switching riskometers, I can halve my risk forecast!
Sadly, it does not meet our criteria for not being detected. The bank’s compliance people and the regulators will notice and are likely to take a dim view of such switching of riskometers, especially if done often.
A better way to manipulate is to cherry-pick the assets one puts into the portfolio. In particular, we can pick assets that robustly provide the juicy returns we want, but do not contribute very much to the measured risk. The reason this is possible is that the riskometer is a caricature.
What we are doing here is to search for the riskometers that have the desired properties. Easy enough to do for anyone with moderately good quantitative skills, and practically undetectable by both the compliance department and the regulators.
When I tried to do this for a sample portfolio, it took me a few minutes to reduce the VaR by over 70% — without changing the expected returns.
The final way to manipulate is to use options. I show a particularly egregious example in Chapter 4 in my book Financial Risk Forecasting. The trick is simply to put a kink in the distribution of profit and loss around the risk number you want to manipulate.
Suppose you buy a put option at strike -VaR1 and write a put at a lower strike price, -VaR2. The effect of the former is to provide risk against the price of the asset falling below -VaR1, while the second exposes you to large losses if the price of the asset drops below -VaR2.
VaR drops significantly, but the risk of large losses increases and overall profits drops slightly. Mission accomplished.
The particular example shown here is, of course, blatant abuse of the bank’s risk management system and presumably would be picked up by the risk controllers. However, if one uses more subtle methods, it may be fully non-detectable.
There are many similar ways one can manipulate riskometers. Some are easy to detect, but others are only known to the particular individual taking the risk.
If you are sceptical and think I am just an academic taking extreme examples that would never see the light of day in the real world, think again. The reason why the UBS bank failed in 2008 was precisely for the reasons I’m describing here.
I may return to the case of UBS soon.
© All rights reserved, Jon Danielsson, 2020