We enter risk management without a crystal ball to predict events. However, we do possess a wealth of information about our past. We know magnitudes of past price movements, we can study relationships between products during crisis periods, we know that our fears can be rational and/or irrational, and we know that we are all human beings who act emotionally in times of uncertainty. The phrase “the market can remain irrational longer than you can remain solvent” will always ring true. This information can help us prepare for the next set of unforeseen conditions.
Because the odds of one of these “this could never happen” events occurring are not high, they can often be left as an afterthought. This is the “we will deal with it if it happens approach”. However, addressing an asset meltdown using post-trade risk scenarios daily is the best way to understand the magnitude of these risks before the event occurs. Following this simple protocol will allow an organization to 1) gain a daily forecast of losses from a global asset downturn or spike over a one-day period or over a series of days and 2) determine if taking this risk is acceptable.
While there is no single correct answer on how to assess the “unpredicted event” risk (many times referred to as a Black Swan event), potential methods to be calculated daily include:
A recent example of an extreme and unusual market event was witnessed in April 2020 at the expiration of the CME WTI Crude Oil futures contract. The price of the May 2020 contract traded at a negative price, as low as $-40.00 USD. Yes, you got paid to purchase Crude Oil. The rest of the non-expiring Crude Oil contracts remained at a positive price. The price spread between the May 2020 contact and June 2020 contract expanded to over $50. This phenomenon was brought about by decreasing short term demand and a lack of available storage for those taking Crude Oil delivery in May 2020.
What is your defense against this extreme risk? First, having the financial model flexibility to allow for negative commodity prices is a good start. A “normal distribution” asset model allows for prices to trade below zero. As a result, hypothetical post-trade stresses on futures positions using a normal model will produce an accurate P&L when the zero barrier is breached.
Second, stressing the front month of a futures contract by a larger magnitude than the rest of the futures curve can expose risks that at first seem to be “spread off” or “delta neutral”. This is always wise but especially for deliverable commodity contracts near expiration. The prices of these front month contracts are greatly influenced by short term events which may not impact the longer dated expiries.
The methods we discussed focus on post trade analysis applied to existing inventory. The use of risk scenarios created by knowledgeable risk managers and accompanied with a VaR calculation (Historical, Parametric, Monte Carlo) give a comprehensive view of potential losses. These calculations need to be available before a crisis begins. It is generally too late to establish an efficient mitigation plan once the event is in progress.
Preparing a daily market risk assessment for crisis events before an event occurs should be combined with the scaled adjustment of “pre-trade” limits based on the degree the severity of the event. These “pre-trade” limits include “buy/sell quantity” limits and “Greek” limits which will be adjusted for the increased volatility in the crisis period.
The existence of these calculations and a plan to manage the risk can be the difference between survival and extinction.