Risk management combines skills, time, and tools to formulate options for any series of events that may occur during the life of a project. Those options range from negative risk, where an event will harm the project, to positive risks, where an event reveals a new opportunity to pursue.
But the real challenge is when a black swan appears. Black swans are big, bad, unanticipated catastrophes that force change. These VUCA events (volatility, uncertainty, complexity and ambiguity) demand a new way of thinking and responding to risk.
Black swans are forcing organizations to engage in new forms of strategic planning. In finance they present mathematical challenges that can earn Nobel Prizes for those who find new ways of modeling risk; folks like Robert Engle, who successfully brought the concepts of historical analysis and correlating multiple data sets together.
The 2008 sub-prime mortgage crisis was a black swan and provides new data to evaluate worst-case scenarios in risk management. Recently, the business graduate school at Simon Fraser University (SFU) in Burnaby Canada took a deep-dive into how black swan events affect Value at Risk (VaR) strategy. Using a 10 million dollar student services fund, SIAS, as their case study, they examined the top four methods used to determine the portfolio’s Value at Risk, commonly know as VaR.
The study, while looking at the most commonly used models in risk management practices, pointed to three myths about which we should be aware.
MYTH ONE: THINGS WORK WHEN WE CHOOSE A BEST PRACTICES STRATEGY
Historical Simulation is considered an industry best practice. Yet, when the folks at SFU took a closer look at its performance with black swan data they found it over-estimated the swan event for one year. That means the fund didn’t take advantage of stocks that were actually in positions to increase returns.
Over 73% of banks use historical simulation because of its simplicity. But when anomalies are in the data timeline, that strategy holds back investments that could stimulate growth. Decisions are made based on the limitation of the model, not the actual opportunity at hand.
MYTH TWO: WHAT’S GOOD FOR A FORTUNE 50 COMPANY IS GOOD FOR US
The case study shows us that what is right for one corporation is not right for another. There is inconsistency – even among the biggest players who are responsible to protect the public from financial risk.
In banking, exceptions are a key risk metric and set by federal regulators. Researchers Xiaoya Chen and Duo Zheng studied VaR data from the Big 5 Banks in Canada. They reviewed 4340 trading days, including the beginning of the black swan. Regulations set the standard for that period at 43 exceptions (days where losses exceed the VaR.) However, 47 exceptions were created.
When the researchers drilled down into each bank they found larger variances. While two of the banks (BNS and TD) were very close to the norm, two others (BMO and RBC) significantly exceeded it. Each used best practices and standards yet two failed to meet federal regulations, even with them.
MYTH THREE: CHOOSING A WELL ENGINEERED BEST PRACTICE MEANS WE CAN GET SHORT CUT ANALYSIS
In the SIAS case study, governance actually weakened the risk management strategy because it focused on individual stock volatility rather than portfolio risk.
It wasn’t until Hsieh and El-Hourani took on a deeper analysis of the portfolio for their thesis that it came to light that the ratios used for individual stock evaluation were flawed. The short cuts taken, despite using best practices and industry standards, were not good enough to optimize results and the black swan event pointed out the deficiency in the strategy.
IN SUMMARY
Risk management depends on good engineering. When faced with complexity, tons of data, multiple events, and the pressure of making decisions quickly, we like to find the shortest path to completion.
But engineering isn’t about short cuts. It’s about building things that last. Bridges that survive despite the worst assaults are examples of careful engineering practice. Risk management needs the same level of quality engineering. Projects without that level of care are, in themselves, a risk that needs mitigation.
Bio:
David Pederson is COO of Hannon Technology, an integration company working with clients in the entertainment industry.
David sponsors Koan Briefs, reports that translate complex academic studies into plain-speaking English for project managers who need to be in the know but can’t spend more than 30 minutes learning the research.
The case study referred to in this article can be accessed by going to http://koffeekoans.com/var