The most troublesome case is when some scenarios are particularly dangerous, yet quite unlikely. How can we trust estimates of very low probabilities? To get the message, consider financial risk management. Here we need to work with extreme events (stock market crashes, defaults on sovereign debt, etc.), whose probabilities can be very low and very difficult to assess, because of limited occurrence of such events in the past. Can we trust our ability to estimate the probability of a rare event? And what if we are missing some scenarios completely?
Example 14.2 (Barings Bank and Kobe earthquake) Barings bank was founded in 1762 and it had a long and remarkable history, which came to an abrupt end in 1995, when it was purchased by another bank for the nominal price of £1. The bank went bankrupt as a consequence of highly leveraged3 and risky positions in derivatives, taken by a rogue trader who managed to hide his trading activity behind some glitches in the internal risk management system of the bank. These strategies lead to disaster when Nikkei, the Tokyo stock market index, went south. When does a stock market crash? This can be the result of real industrial or economical problems, or maybe the financial distress of the banking system. Risk management models should account for the uncertainty in such underlying factors, and even unlikely extreme scenarios should enter the picture, when taking very risky positions. However, many have attributed that drop in the Nikkei index to swinging market mood after an earthquake stroke Kobe. Luckily, the earthquake was not hard enough to cause a real economic crisis; yet, its effect was pretty concrete on Barings, which had to face huge losses, ultimately leading to its demise.
Very sophisticated models have been built for financial applications, accounting for a lot of micro- and macro-economic factors, but it must take much imagination to build one considering the potential impact of an earthquake.
Indeed, the most radical form of uncertainty is when we cannot even trust our view about the possible scenarios. To reinforce the concept, imagine asking someone about the probability of a subprime mortgage crisis twenty years before 2007. In Fig. 14.1 we have depicted a black scenario with which it is impossible to associate a probability, for the simple reason that we do not even know beforehand that such a scenario may occur. In common parlance, these scenarios are referred to as “black swans.”4 When black swans are involved, measuring risk is quite difficult if not impossible. The term Knightian uncertainty was proposed much earlier to refer to unmeasurable uncertainty, after the economist Frank Knight drew the line between risk and uncertainty.5 It is quite difficult to assess the impact of unmeasurable uncertainty on a decision model, of course; maybe, there are cases in which we should just refrain from making decisions that can lead to disaster, however small its probability might look like (if nothing else, because of ethical reasons).
Leave a Reply