Why Is It Only The B.Music Grads Who Are Driving Lamborghinis?

by Rick Nason, PhD, CFA

Partner RSD Solutions Inc.

I recently gave a speech with the above title to the risk group at a large financial institution. It was a very exciting yet intimidating audience to talk to. Most of the audience members had advanced degrees in Mathematics or Physics, while most of the younger audience members had Masters degrees in Financial Engineering or Financial Mathematics. It was a thrill and an honour for me to be invited to spend some time with them.

The title of my talk might seem a bit strange – and perhaps even insulting to such an audience. To insult them however was the exact opposite of my objective. The point of my talk instead was to focus on how the career in risk pendulum may have shifted and that mathematical skill may not be the competitive advantage it once was. Instead my hypothesis is that mathematical skill combined with creativity, artistic and humanistic skills are the portfolio of skills that are now necessary in risk management.

Before continuing I should explain some of my personal basis for my arguments. I am a physicist that converted a graduate career in physics into a graduate degree in finance during the early 1990’s. I very much benefited from the Wall Street hunger for physicists at the beginning of the “math boom” in finance. However it is my liberal arts undergraduate degree that I consider to be the most valuable in my development as risk manager. I believe that the ability to think in liberal arts terms as well as mathematical terms is the new black in risk management.

Knowledge of risk techniques is a commodity. The ability to think and implement risk management is the value added, and in order to think and implement risk you need to understand a variety of aspects of risk – some quantitative but most qualitative. Additionally you need to be creative and flexible in your thinking. Unfortunately the business schools and financial engineering schools (and corporate training programs that produce cookie cutter analysts and consultants) are very good at disseminating knowledge, facts and frameworks, but knowledge, facts and frameworks are not the same as the ability to think and do!

One could argue (and I will argue in a future blog) that risk management is more about understanding people at both the individual level (within an organization and within the level of immediate stakeholders) as well as at the sociological level. Economists and Finance academics are currently rushing to make their mea culpas that their models cannot work in a world composed of irrational masses. The Behavioural Finance field has evolved to chronicle our collective mass irrationality (stupidity?) but offers few if any solutions for overcoming this collective handicap.

We thus come back to the fact that it is the artists of the world who may be best prepared to deal with the current shortcomings of quantitative risk management and repair the image of the field. Ironically physics and mathematics used to attract the creative types – those who dreamed and thought creatively. When I was a graduate student in physics it was common for students to use psychedelic drugs not for pleasure but to help them think more creatively and more outside the box. (Names withheld for obvious reasons.) While I am most certainly not advocating illegal drug use, I do believe that there needs to be a call made for more creativity and artistic license in the field of risk management. Physicists and financial engineers also like to drive Lamborghinis and should release their inner artist in order to regain the comparative value added that allows them to do so.

Simple, Complicated or Complex?

Rick Nason, PhD, CFA
Partner, RSD Solutions Inc.

I have a confession; my first training and career was as a physicist. As a physicist we calculate natural phenomena to astounding degrees of precision, often under highly controlled laboratory conditions. Yet, with all of that precision, and hundreds of years of theoretical and experimental development, physicists still embrace and accept the concept of complexity as the term is scientifically defined.

Conversely, in finance and risk management – fields with much shorter histories of study, and nothing like laboratories with controlled conditions – we insist on making measurements to ever higher degree of precision (such as in option pricing), even though it is widely accepted that the models may be incomplete and the input data subject to uncertainty or interpretation.

In the sciences there is a classic distinction between those processes which are Basic, Complicated and Complex processes and systems. The best explanation that I have ever read is in the book “Getting to Maybe: How the World is Changed” by Frances Westley, Brenda Zimmerman, and Michael Quinn Patton, with a forward by Eric Young. (Random House 2006) [1] This book has nothing to do with risk management but I would most highly recommend it to every risk manager as one of the best books on how to think about risk management and how the practice of risk management might evolve.

In Getting to Maybe, the authors explain Basic, Complicated and Complex systems using the analogies of “Baking a Cake”, “Sending a Rocket to the Moon” and “Raising a Child” respectively. Their description, greatly summarized and adapted, is as follows. In baking a cake it helps to follow a recipe and it helps to have experience, but even if a few mistakes are made, and a few measurement errors are incorporated it is likely that something resembling a decent and edible cake will be produced. In sending a rocket to the moon it is critical that each and every step (and there are a great many steps) must be followed exactly, and if each step is followed exactly then the space mission will be a success. In other words, getting a rocket to the moon is a complicated process, but if followed exactly then success is assured. Raising a child on the other hand is a complicated process. Even if the parent does everything correctly, there is no guarantee of success. Furthermore you cannot break the process of raising a child into a set of discrete steps or processes. Raising the child is an integrated and holistic process. Furthermore it is impossible to a-priori say exactly what the most necessary or important steps for raising a successful child are going to be as each child is unique even though most children exhibit common characteristics. Finally post raising a child it is virtually impossible to state what the most critical factors were in the success or failure.

A moments thought allows one to see the many parallels between raising a child and implementing risk management. We tend to think of risk management as a complicated process, where if we simply (?) get all the pieces in place then success will follow. But the risk management field is littered with risk debacles that occurred at organizations that were the acknowledged leaders in risk management[2]. The point is that risk management is complex, not complicated. Best practices in risk management do not guarantee success. Furthermore, it is nearly impossible to tell exactly and precisely after the fact what were the key factors that led to success or failure of a risk management program. Additionally risk management is a holistic process – for example you cannot manage market risk while ignoring liquidity or credit risk.

That is not to say that risk management is an alchemist’s game. As in raising a child, we know there are factors and actions that lead to a higher probability of success or failure. Also, as anyone who is a parent knows, raising children requires experience, wisdom and flexibility to adapt to changing environments and to changing cycles in a child’s development. In other words, it is actions (technical knowledge), wisdom (experience and listening / watching the best and worst practices of other parents) as well as intuition all taken together that lead to higher probabilities of success or failure.

To put it bluntly, technical knowledge alone is not sufficient for success in risk management. Realizing that risk management is a complex, and not a complicated profession is what is required. In a complex system you need the technical knowledge, wisdom, intuition, and as I argued in a previous blog creativity are all necessary to increase the probability of risk management success.

Risk management is not basic. Risk management is not complicated. Risk management is complex, and accordingly requires a complex package of knowledge and skills to manage.

[1] The amazon.com link is http://www.amazon.com/Getting-Maybe-How-World-Changed/dp/067931444X/ref=sr_1_1?ie=UTF8&s=books&qid=1252101549&sr=8-1

[2] For more on this point, see my forthcoming article “Is Your Risk System Too Good” in the October issue of the RMA Journal.

The Most Important Training Question

Rick Nason PhD, CFA
Partner, RSD Solutions Inc.

I often get requests for training seminars where the client has a long list of topics that they want covered. These lists will often be accompanied by varying degrees of details on the current knowledge level of the participants as well as their current job functions. Clients that having training departments will also have suggestions as to timing, format and pre-seminar and post-seminar activities. While this data is most welcomed, it misses the most important training question of all: What is it that you want the seminar participants to do after the training that they could not, or would not do before?

When confronted with this question, training managers (or more often than not now with the parsing of training departments it will be the line manager) often stumble with their words. They simply do not know what the change in action is that they want to have occur because of the seminar. They have a well defined body of knowledge that they believe needs to be conveyed, but the exact business reasons why the participants need this knowledge is generally extremely hard for them to articulate.

Often when I actually conduct the seminar I realize that many of the participants already have a fairly comprehensive knowledge – or at least a working knowledge of the material to be covered. The issue therefore is not knowledge per se, but performing actions that utilize that knowledge. There are many reasons why employees may know something yet not act on it. Self-esteem, confidence, not understanding the rationale behind the knowledge, not understanding how important acting on the knowledge is for the company or unit to achieve its objective, or simply that it is much easier to act on other knowledge by default or through habit.

This brings home the point that professional training should be about changing actions, changing confidence and changing the ability of the seminar participant in helping the organization to succeed. Successful training is only 1 part technical knowledge. It is 10 parts action, 10 parts rationale behind the action, 20 parts confidence and motivation in taking that action and 30 parts having the right objectives for the training program in the first place.

One training manager that I once worked with strongly questioned this line of reasoning, until I asked the question: “Do you want employees who know things, or employees who do things?” That question brought it all home.

The next time you call us (or another training firm for that manner) be prepared to have a dialogue about the answer to the question, “What is it that you want the seminar participants to do after the training that they could not, or would not do before?” You will be glad you did.

Rules of Risk Management

This article is authored by David Finnie, Principal of RSD Solutions.

We all love lists and sets of rules providing insights, especially when those lists and rules are easily digested and meaningful in our lives (or humorous as are David Letterman’s 10 reasons …).

I recently found a list of 101 rules of risk management[1] and I was impressed that such a list had been compiled. Unfortunately, it was primarily an insurance based list although it did, of course, have significant overlap with similar issues in financial risk management. The list intrigued me, but as I worked my way through the list, I realized that it was simply too long. Certainly not too long in terms of coverage since risk management is a large, complex and growing field, but too long to capture the key “truths” that help guide risk management professionals through their day-to-day activities and too long to be memorable.

I found another list with only nine rules[2]. These rules are geared toward traders and money managers and they are good rules for them. But, their range is too limited to cover the risk management spectrum for a risk professional.

There are also books on the rules of risk management. Of course, these books go well beyond the simple expression of some key rules. They capture some good rules to varying degrees, but they do not provide them in an easily digestible or truly memorable form.

Here is my attempt to provide a short list of key rules and insights. Of course, these rules come from my experience and are to some degree specific to the companies and situations with which I gained that experience. They also, I will admit, come from Tolkein’s Lord of the Rings trilogy.

Rule 1: All that is gold does not glitter

This line makes it clear that not all that we need to know will be clearly evident and easily discovered. To be effective, the risk manager must understand the business, must be able to undertake effective risk analysis and must always look beyond the superficial and the enticingly obvious in order to fully understand the risks being taken. This need to search for the “gold” also warns against complacency when everything looks good.

Rule 2: Not all those who wander are lost

There is a discovery process in risk management which necessitates a willingness to wander or explore. This involves looking for unusual, unexpected occurrences within a business’s operations and results. It means always being willing to question and to go deep into what is happening in both the organization and the environment in which it operates.

Rule 3: The old that is strong does not wither

This line speaks to the value of experience and implies a wisdom arising from that experience. Risk management includes a “gut feel” or “trust your instincts” element that can only come from experience, listening to those with experience and taking the time for careful reflection and consideration.

Rule 4: Deep roots are not reached by the frost

Strong, well founded risk management approaches, processes and governance allow a firm to grow and prosper during the good times and weather the frost of the downturns. For me, it also suggests that this ability to thrive throughout the business cycles comes from strong, risk-based, well-managed capital.


So, these rules warn against complacency, encourage exploration and questioning, recognize the value of experience and wisdom, and demand strong risk governance and risk-based capital. Not bad for the first stanza of a poem about a solitary ranger in a fantasy world.

I have to admit that I did find a set of nine rules focused on risk management put out by RiskMetrics Group. These rules are good, straightforward and very sensible. But, risk management is as much art as it is science and I like being able to capture the same concepts and themes using poetry so I will use the ones above.

I invite you to critique these rules and to provide the rules that you have found important.

[1] Compiled by the late Tom Hallet with help from his colleagues and associates. This list can be seen on the Harvard Aimes Group web site - www.riskmanagementsearch.com/index.htm
[2] Nine Risk Management Rules It Pays Not To Forget, Bensman, Miriam, Futures, June 1994

Rules of Risk Management

This article is authored by David Finnie, Principal of RSD Solutions.

We all love lists and sets of rules providing insights, especially when those lists and rules are easily digested and meaningful in our lives (or humorous as are David Letterman’s 10 reasons …).

I recently found a list of 101 rules of risk management[1] and I was impressed that such a list had been compiled. Unfortunately, it was primarily an insurance based list although it did, of course, have significant overlap with similar issues in financial risk management. The list intrigued me, but as I worked my way through the list, I realized that it was simply too long. Certainly not too long in terms of coverage since risk management is a large, complex and growing field, but too long to capture the key “truths” that help guide risk management professionals through their day-to-day activities and too long to be memorable.

I found another list with only nine rules[2]. These rules are geared toward traders and money managers and they are good rules for them. But, their range is too limited to cover the risk management spectrum for a risk professional.

There are also books on the rules of risk management. Of course, these books go well beyond the simple expression of some key rules. They capture some good rules to varying degrees, but they do not provide them in an easily digestible or truly memorable form.

Here is my attempt to provide a short list of key rules and insights. Of course, these rules come from my experience and are to some degree specific to the companies and situations with which I gained that experience. They also, I will admit, come from Tolkein’s Lord of the Rings trilogy.

Rule 1: All that is gold does not glitter

This line makes it clear that not all that we need to know will be clearly evident and easily discovered. To be effective, the risk manager must understand the business, must be able to undertake effective risk analysis and must always look beyond the superficial and the enticingly obvious in order to fully understand the risks being taken. This need to search for the “gold” also warns against complacency when everything looks good.

Rule 2: Not all those who wander are lost

There is a discovery process in risk management which necessitates a willingness to wander or explore. This involves looking for unusual, unexpected occurrences within a business’s operations and results. It means always being willing to question and to go deep into what is happening in both the organization and the environment in which it operates.

Rule 3: The old that is strong does not wither

This line speaks to the value of experience and implies a wisdom arising from that experience. Risk management includes a “gut feel” or “trust your instincts” element that can only come from experience, listening to those with experience and taking the time for careful reflection and consideration.

Rule 4: Deep roots are not reached by the frost

Strong, well founded risk management approaches, processes and governance allow a firm to grow and prosper during the good times and weather the frost of the downturns. For me, it also suggests that this ability to thrive throughout the business cycles comes from strong, risk-based, well-managed capital.


So, these rules warn against complacency, encourage exploration and questioning, recognize the value of experience and wisdom, and demand strong risk governance and risk-based capital. Not bad for the first stanza of a poem about a solitary ranger in a fantasy world.

I have to admit that I did find a set of nine rules focused on risk management put out by RiskMetrics Group. These rules are good, straightforward and very sensible. But, risk management is as much art as it is science and I like being able to capture the same concepts and themes using poetry so I will use the ones above.

I invite you to critique these rules and to provide the rules that you have found important.

[1] Compiled by the late Tom Hallet with help from his colleagues and associates. This list can be seen on the Harvard Aimes Group web site - www.riskmanagementsearch.com/index.htm
[2] Nine Risk Management Rules It Pays Not To Forget, Bensman, Miriam, Futures, June 1994

Donald Rumsfeld - Risk Guru?

This article is authored by Dr. Rick Nason, Principal of RSD Solutions and Associate Professor of Finance at Dalhousie University, Canada.

Regardless of what you think of Rumsfeld’s policies during his tenure in President Bush’s inner circle, a case can be made that he was a risk guru. What the heck am I talking about you might ask?! It’s really quite simple – namely his well known quote, (reprinted below), automatically qualifies him for the Risk Hall of Fame of Quotable Quotes.

"As we know, there are known knowns,
There are things we know we know.
We also know there are known unknowns.
That is to say we know
there are some things we do not know.
But there are also unknown unknowns,
The ones we don't know we don't know."

Donald Rumsfeld, February 2, 2002

Mr. Rumsfeld’s quote was widely ridiculed by the media, and taken as a sign by the general public that he had truly lost it. However, every experienced risk manager understood completely and intuitively the message that Mr. Rumsfeld was trying to convey. The three buckets of data (i. known known, ii. known unknowns, and iii. unknown unknowns) are the cornerstones of any successful risk management strategy. The problem is that as practitioners and academics we really only know (no pun intended) how to deal with the known knowns, while we develop increasingly sophisticated yet mostly ineffectual methods for dealing with the known unknowns. To compound the problem, the preferred method of dealing with the third cornerstone (unknown unknowns) is some combination of prayer, hope or keeping an updated resume so a new position can be found quickly when the inevitable happens. Unfortunately the unknown unknowns almost inevitably become the most significant elements with hindsight.

The above paragraph might give you the impression that I am incredibly jaded and cynical about the current state of risk management profession. In reality the opposite is true. The fact that there are known knowns implies that there is a body of knowledge that we can draw upon to deal with a portion of the risk management issues that we face. The existence of known unknowns presents us with a set of intellectual challenges that stimulate and allow the field to grow in relevance and importance. However it is the unknown unknowns that imply that risk management is a field for creativity – a field for futurists and wild dreamers and schemers, if you will.

The diversity of mindsets, and the diversity of knowledge skills needed, makes the study and practice of risk management so exciting. Understanding and practicing risk management provides a full body workout for the mind. The known knowns require linear and often quantitative type thinking. The known unknowns generally require both linear and non-linear algorithms combined with quantitative and qualitative measures. The unknown unknowns however require the full spectrum of human thought, from deterministic to stochastic, qualitative to quantitative and rule-based heuristics to free-form creativity.

Unfortunately, (or fortunately for those who can break out of the mold), current corporate policies, often based on traditional academic dichotomies of right and wrong, do not usually stimulate, foster or allow for the type of creative, non-linear, out of the box thinking required to successfully grasp the three elements of risk management. The evidence for this of course is the ridicule that Rumsfeld faced upon uttering his now famous statement.

It has often been said that a genius is never understood in his own time. While there are likely to be legitimate and serious doubts about many of Rumsfeld’s political policies for generations to come, it is my prediction that his quote from above will eventually be seen for the risk management genius that it is.

Arbitrage Models vs. “Almost” Arbitrage Models

This article is authored by Dr. Rick Nason, Principal of RSD Solutions and Associate Professor of Finance at Dalhousie University, Canada

Arbitrage models are the basis of pricing for many investment structures. Arbitrage pricing is a very powerful and useful methodology for determining the fair value of many different types of structure – both simple and complex. In fact, arbitrage models are so useful that there is a great temptation to apply them to “almost arbitrage” situations by using increasing levels of mathematical sophistication. They work quite well most of the time when the necessary assumptions about the “almost arbitrage” conditions are not too wild or inconsistent with actual market activity. This memo will claim however that this is when reliance on arbitrage models is most dangerous. All available and necessary mathematical tools should be applied in attempts to price financial instruments when true arbitrage conditions apply. However mathematical sophistication should be used very warily when “almost” arbitrage conditions exist.

Arbitrage in finance is akin to finding a tautology. In other words, arbitrage is discovering two different investment strategies that produce identical outcomes under ALL future economic scenarios. Note that the definition does not rely on any assumptions whatsoever. Mathematically we can state that we have one investment portfolio A, which at some time in the future will be economically identical to an investment portfolio B. If the current price of portfolio A is different from that of portfolio B then a risk-free arbitrage profit is to be made.

It is rare for a risk-free arbitrage profit to exist for an extended period of time. Rational investors will exploit the situation which in turn forces the relative prices of portfolio A and portfolio B back into an equilibrium whereby the risk-free arbitrage profit will no longer exist. The fact that rational traders will quickly drive risk-free arbitrage situations out of existence and restore equilibrium to the markets is the basis for many of the pricing models and pricing formulas in existence. For instance the models for pricing index futures and interest rate swaps and currency forwards are all based on the principle of no risk-free arbitrage.

Arbitrage models tend to work very well; particularly over extended periods of time. Markets may occasionally stray from equilibrium, but when the possibility of setting up a risk-free arbitrage exists, it is virtually certain that rational traders will quickly pounce on the situation, take their profits, and in the process drive prices back into line. Therefore arbitrage models have become the staple of the financial modeler or pricing analyst. The success of arbitrage pricing has led to a natural increasing of the sophistication of the field, with modelers building ever more complex arbitrage structures, with ever increasing levels of mathematical sophistication in order to price ever more complex financial instruments and structures.

As arbitrage pricing models become ever more complex, it is almost always necessary to stray from “perfect arbitrage” to “almost arbitrage” by introducing either market based assumptions (such as markets trade continuously), or statistical assumptions (market returns are normally distributed), or behavioural assumptions (traders remain profit seeking). Generally all three types of assumption are made. Furthermore these very benign looking assumptions are made with little concern, and indeed such assumptions often prove to be suitably valid. This gives the analysts increasing confidence to raise the level of sophistication of their models and of their subsequent mathematical apparatus.

It is when there is a straying from perfect arbitrage to “almost arbitrage” that great danger lurks. Furthermore this great danger lurks under a heavy veil of sophisticated mathematics – mathematics which few people other than the analyst are likely to understand with any degree of confidence.

Take for example what is probably one of the most famous and the most successful “almost arbitrage” models that exists – the Black-Scholes Option Pricing Model (BSOPM). The BSOPM is based on the arbitrage situation that an option on an asset can be “almost” perfectly replicated by dynamically trading the underlying asset according to a set of parameters known to traders as the “Greeks”. While the underlying mathematics (stochastic calculus) that went into the development of the model is beyond the understanding of the vast majority of financial traders, the dynamic replication concept itself is relatively well understood and easily illustrated. The Nobel Prize winning BSOPM is widely used and successfully so. It has been so widely accepted that few traders ever pause to question its validity and most financial analysts will use it to benchmark other models they have under development.

The assumptions of the BSOPM are relatively benign. The first major assumption is you can trade the underlying – utilizing both long and short trades – without affecting the actual market price. With 24 hour trading, and the huge amount of liquidity available for most securities this appears to be an innocent assumption. The second assumption is that market prices move smoothly – that is that market prices do not gap from one price level to another. This too appears to be an innocent assumption when one considers that most exchanges require market specialists to limit price moves and have circuit breakers in place to limit extraordinary large daily moves. There are a few more equally innocent assumptions to the BSOPM, but these two will suffice to illustrate the argument for now.

The problem with BSOPM is that it works very well almost all of the time, but very poorly when you need it to work the most – i.e. during a market crisis. This was a lesson learned by Fisher Black and Myron Scholes in the early days of development of the formula, and then relearned (?) by Myron Scholes and others during the meltdown of Long Term Capital Management. Upon discovering the formula, Black and Scholes decided that they would exploit their knowledge by trading options. Their strategy was simply to buy underpriced (according to the formula) options and sell overpriced options. According to an article by Fisher Black , their results were less than profitable. Later when the hedge fund Long Term Capital Management (LTCM) started trading they were initially very profitable by using the BSOPM (amongst other models one assumes). However these two the assumptions came back to haunt LTCM traders. The supposedly benign assumptions of being able to trade continuously, and that the markets do not gap, broke down simultaneously. Since they had high confidence in their models (and why shouldn’t they have confidence as LTCM had been massively profitable from start-up) they were highly leveraged, and carrying very large positions. When they tried to trade the underlying assets (to capture the arbitrage of the replicating portfolio) LTCM’s traders realized that their assumption of being able to trade continuously without affecting market prices was invalid. This was compounded by the fact that the markets started to gap with the absence of liquidity which only made their situation exponentially worse. The result of course is that LTCM was wiped out.

Similar mistakes are currently being made in the credit markets. Sophisticated models, based on benign assumptions, are being utilized to price complex structures such as CDO’s. As with all progress, the mathematical tools being used are increasingly sophisticated, and their use is justified by the reasonableness of the assumptions, and the fact that the models appear to be valid as confirmed by market results. The issue is that these are “almost arbitrage” models, not true arbitrage models. What exists is not a tautology, but instead a financial construction (generally based on modeled cash flows) that looks “almost” like a tautology, but only after imposing a few innocent assumptions, such as default rates and default correlation levels will remain in their traditional range of values. Thus we find investors realizing that their AAA tranches might now be worth less than 10% of face value relatively soon after issuance.

The point of this article is not to argue against the use of sophisticated mathematics. In fact mathematics is not the problem at all. The issue is the assumption of arbitrage when a true arbitrage - a tautology - does not in fact exist. Mathematics is a field of tautology. Assumptions are not.

When a true arbitrage situation exists, then financial modelers should apply all the mathematics that is necessary (but not any more than is necessary) to successfully uncover the valid price of an asset or structure. However when an “almost arbitrage” situation exists, caution should be the keyword, as sophisticated mathematics will only obscure the situation and mislead the trader when the underlying assumptions start to slip – as they inevitably do just when you need them to remain valid the most. The tough part is figuring out what is a true arbitrage versus an “almost arbitrage”.