There is no certainly shortage of circumspect navel-gazers looking to determine what went wrong last year in terms of miscalculating risk. And fair to say that the entire investment industry, hedge funds included, could benefit from a little circumspection in the wake of the worst financial crisis in more than 70 years.
The reality, however, is that post financial market meltdown and Great Recession, looking at what could work going forward in terms of having the bells, whistles and flashing lights go off rather than figuring out what didn’t work is, at this stage, a much more effective and enticing proposition.
Part of which explains why Dr. William Shadwick’s presentation to AIMA Canada and PRMIA Toronto members on “Going to Extremes to Control Risk” a few weeks back pulled in such a strong crowd.
“It’s not that the risk management and valuation practices being used by the banks and others weren’t any good,” Shadwick told roughly 100 intent listeners gathered at the University of Toronto’s Fields Institute, “it’s that they didn’t work.”
No stranger to various valuation and risk management techniques, (and no stranger to AllAboutAlpha, thanks to his generous contributions to our space), Shadwick is a highly-regarded mathematician who crossed over into finance over a decade ago, and has since made his mark on the field of investment performance analysis, developing Omega Metrics® and winning a prestigious award from the Investment Management Consultants Association along the way. He is also the founder of Omega Analysis Limited, a quantitative research firm in London.
Indeed, beyond the collective hope of circumventing another global financial catastrophe, Shadwick, like many others, does have an “angle” of sorts – namely, to get individuals and firms to apply his techniques, specifically his “extreme value theory” in calculating value at risk (VaR) and conditional value at risk (CVaR)
But the basics of his valuation parameters for picking up on the potential of unlikely “Black Swan” events, freely available to anyone who wants to see them, offer some valuable methodologies that any risk-mitigation pro can – and likely should – utilize.
Beyond the mathematical equations and stats, what Shadwick proposes is actually fairly simple: Instead of focusing what might be the worst potential loss one could expect 99 days out of 100, or what the least one might expect to lose one day in 100, the focus should be on what one can expect to lose “that one day in 100.”
“If you can calculate 99% VaR, you can, and should, calculate 99% of any potential earnings shortfall,” Shadwick says.
Put to test, the charts add up. Using a 250-day rolling data window on Citigroup shares, (with the oldest return discarded and the most recent one added each day) Shadwick demonstrates the probability of the worst loss in the sample, and the earnings shortfall conditional on exceeding that loss.
In other words, it’s about using the right statistics that have a better chance of picking up a highly improbable event, rather than using decades-old parameters that, in hindsight, were not successful in providing an appropriate measure and weighting of risk.
“Statistics didn’t fail, and markets didn’t fail: Naive statistical analysis of markets failed,” says Shadwick. “Careful statistical analysis is the appropriate level of mathematical modeling in Finance.”
Meaning have the tools to duck and cover the next time a Black Swan makes an ugly appearance.