The corporate world has traditionally taken a flawed approach to risk management, but a better alternative is readily available.
Over the past decade, and especially over the past few years, a number of the world’s most widely respected companies have collapsed. The authors believe that a major, though not often mentioned, factor was these companies’ traditional approach to risk management, which tends to look for risk in all the wrong places.
Two fundamentally different views have evolved over the years on how risk should be assessed. The first and prevailing view — termed the frequentist view — is based solely on repetitive historical data, such as weather patterns. The second, or Bayesian, view considers risk to be in part a judgment of the observer, or a property of the observation process; repetitive historical data thus are essentially complemented by other kinds of information. Where there is a great deal of relevant data, this information plays a dominant role, with the integration of judgment making a substantial improvement over the traditional approach. Where there is little or no relevant data, judgment plays a dominant role, providing value under conditions beyond the scope of the traditional approach. Either way, recognizing the important, sometimes central, role of judgment can lead to more reasonable and realistic behavior — in large part because we realize that judgment is not perfect and can be refined as more experience is acquired.
The key point is that risk under the Bayesian approach can be measured quantitatively, whatever the amount and quality of the data. And rather than focusing entirely on the observed world, Bayesian risk assessment also reflects the consistency, reliability and precision of the observer.
Many measures are being deployed to recover from the collapses and to build a more robust system that prevents future crises — a shift from frequentist risk management to Bayesian risk management should be a part of this effort.
5 Comments On: How to Manage Risk (After Risk Management Has Failed)
I am less than clear how either the “frequentist” or the Bayesian approach can help a Societe Generale deal with the likelihood of a “rogue trader”, BP with the possibility of a $20 billion + liability because of the Gulf of Mexico, or Lehman Brothers’ Street-wide reputation for taking risks that other firms simply refused. Isn’t the answer what Pierre Wack suggested in his work on Scenarios,namely to imagine “the worst that could happen” and to devise strategies that would cope with such events. That might have led firms such as Societe Generale to raise its Tier 1 capital on its own, to insist on more rigorous audits on its traders to catch the likes of Jerome Kerviel; perhaps even to set aside reserves for “untoward” trades that might cost the bank something? Could a similar approach rely not on Bayesian statistics but the stated penalty per barrel of spilled oil were criminal negligence to be proven, and contrast that with the cost of additional tests, or delays in going ahead ‘regardless’?
My reading of these corporate disasters has little to do with past or future likelihoods of a “Black Swan” event, but the lack of consciousness in top management of what the boys downstairs are actually doing, and what it might ultimately cost. I’ve seen with my own eyes the cost to a public utility in saving money by starving inventories, only to get caught and lose both a multiple of the savings in higher investment, and the replacement of the CEO. As the Bard said:”The fault dear Britus lies not in our stars, buy in ourselves…”
I work for a hi tec company and recently implemented demand forecasting using Bayesian modelling. The solution was provided by a large ERP software company. I would like to highlight the limitations of the Bayesian approach as learnt from the implementation.
1.Bayesian modelling is subject to the same errors of judgement as any other model .
2.Before you even start Bayesian modeling, please look at the system as a whole and understand the interactions between the parts of the system.
e.g The Bayseian forecast was great from a mathematical perspective but our demand planners rejected it because the suppliers could not react to it since the later were used to receiving a smooth forecast.
3.Bayesian does not work well if data exhibits a wide spectrum of patterns.
e.g our data had variability along 5 dimensions – intermittency,volatility,age,volume and revenues. The extreme difference in data patterns caused Bayesian to not perform as well. We had to work around it.
4. The anterior probabilities could be hard to obtain and are often unrealistic.
5. If you are relying on expert judgement anyway , then Bayesian may not add much to our knowledge.
e.g our expert forecasters already knew what Bayesian came out with. Skepticism increases if Bayesian fails to add value repeatedly and has to be countered with sound change management techniques.
6.For macro economic factors to influence Bayesian modelling, the correlation has to be strong . Also the numeric values of those economic factors are by themselves prone to error. e.g we tried to use semiconductor shipment ,GDP and stock indices as a causal factors but gave up due to poor correlation.
The Risk Assessment Method is the most intrigues behavioural issue.
Historic or alternative Bayesian method do not predict ,with desirable precision,all risks possible.
Walter mentioned ,in his comment, the disaster happened in some groups-according the caos theory,predictable!
The risk pendulum movement is caotic ,predictable only according its
relation with the”start pole”.
The simple human good sence proverb “Clean in front of the own door”!
Applying yet another technique of assessing risk does not address the systemic risk nor does it adequately account for erratic human behavior. I agree with the comments of William Blass that models will not predict the rogues like Madoff or Soc Gen’s trader.
I suspect that Goldman and Morgan Stanley did apply risk management techniques along with scenario planning and as a result were less affected by the crises but even they were caught up in the systemic risk having relied on AIG to insure their loses. Only the bailout of AIG saved them. I wonder if they modeled their reputational risk? They are no longer perceived as doing “God’s work”,
There is something fictional about the financial industry and to some degree economics as well. This fiction is something everyone accepts because they have models that represent their perceived risk. The belief is that we’ve “modeled out” the risks.
The public is surprised when a disaster strikes, yet the “insiders” are well aware of the risks and are willing to believe that disaster will not befall them.
The basics of the mortgage crises were evident to even the most unsophisticated. Lending money to those who have a low probability of being able to pay, securitize these loans and use ratings based on known flawed models and sell these securitized products to unquestioning funds who then pass these on to unsuspecting customers and you have the makings of a grand fictional scheme.
Does Bayesian modeling account for these fictional variables? I don’t think the current crisis was the result of lack of models but a collective lack of common sense. And common sense is very difficult to model.
It is not correct to say that the risk management has failed. The economic and financial systems have collapsed, however it does not mean that the risk management and modeling techniques were wrong.
Viktor O. Ledenyov, Ukraine