As we’ve been hearing, the collapse of Lehman Brothers, Merrill Lynch, AIG, Bear Sterns, Washington Mutual, and Wachovia can certainly be blamed on corporate greed, lax oversight, and out of control executive incentive plans. However, what gets lost in the noise is the need for more fundamental quantitative risk analysis at real decision-making levels. Sure, lots of mid-level analysts may have run simulation models showing that many sub-prime mortgage customers wouldn’t be able to make their payments after the low fixed rate expired, but obviously nobody at the top was listening. It’s easy to blame greed, but what top-level exec would have sanctioned these loans if they had known with near-certainty that this would be the result? It’s important to consider the communication process—or lack thereof—within these organizations. Is there an easy way for “quant jocks” to demonstrate to managers who set lending policies that these kinds of risks are real?
Using Monte Carlo simulation, the answer can be “yes.” Surely Monte Carlo simulations were used at Lehman Brothers, WaMu and the rest. But a lot of people, especially managers, especially managers with short-term bonuses on the line, glaze over when presented with reams of figures and endless charts. Instead, the value of Monte Carlo simulation can lie not just in the data it generates or the random number seeds that can be used or the distributions it can incorporate, but rather in its ability to generate simple, easy-to-follow graphs to communicate key points. One large oil company requires every report on a new investment to be accompanied by a single graph from @RISK Monte Carlo software. A single picture like the one below could have shown—at a glance—that the chances of disaster were not trivial (24.6% in the example).