Riscus

Riscus - "Difficulty to avoid in the sea."

The ancient Greeks had a word for it, ριζα, which made its way to the Latin, Riscus.  The original meaning was a metaphor for “difficulty to avoid in the sea.”  Ancient mariners traveling along the islands that dot the Mediterranean, the Aegean and the Tyrrhenian Seas knew all too well what it meant.  Over time, the word made its way to Italian – risico, rischo, and rischio; to French – risque, and to Spanish – riesgo.  Starting in the 16th century, middle-high-German adopted the term Rysigo in a business sense: “to dare, to undertake, to hope for economic success.”  This weekend, we witnessed an ironic collision of these two uses of the word: a €450M ship ran aground and ‘mostly’ sank – of all places – off the very coast where the ancients learned what ‘riscus’ is all about.

I adopted the cover of Peter Bernstein’s excellent book Against the Gods as the primary image for this blog because of this intertwined history: risk management and humanity’s attempt to conquer the seas is intricately intertwined and is rife with lessons about how to go about improving our odds of success.  It’s also a great story.

It wasn’t until the Renaissance that we started quantifying risk and making any use of mathematics to help mitigate it.  Early on, when the Ancient Greeks and Romans sailed the Tyrrhenian, they assumed that the uncertainty of survival on the seas was caused by the whims of gods like Zeus and Poseidon.  That argument – that gods cause something – has a modern counterpart: “the computer told me so.”  Both are black-box solutions to the problem that conveniently avoid the need for any real understanding.  Both employ a very unsatisfying ‘Deus ex Machina’ to overcome the problem.  Peter Bernstein made this point in Against the Gods with an analogy between the Oracle at Delphi and the modern computer: people believe the output of each because of its established authority, suggesting that trusting modern computers is as dangerous as trusting the Oracle’s advice.  I wrote to Peter objecting to this line of reasoning because modern computers have verifiable benefit, while the Oracle at Delphi was just one in a long line of superstitious beliefs that have distracted people with comfort over reality.  He didn’t budge: about as many people today, he told me in the late 1990’s, understand the output of computer risk models as back then understood that the Oracle was nonsense.  For everyone else – the severe majority of people – they are dangerously equivalent.

But such is the course of progress.  Today’s airline passengers need not understand the Bernoulli principle to take advantage of international flight.  Today’s medical patients need know nothing of molecular biology to benefit from their daily prescriptions.  Similarly, today’s investor should not need worry about Martingales or Lévy distributions to avoid catastrophic losses in their portfolios.  But we’re not quite there yet.  We’ve gotten past the belief in gods, but as I’ve said many times in this blog, we haven’t yet found the underlying dynamics that would allow us to declare a fundamental understanding of these investment waters we keep trying to sail.

The story of risk – of humanity’s struggle against the gods – is, as Pater wrote, remarkable.  The Costa Concordia accident is a vivid reminder that even with the most modern equipment and procedures, risk is very much with us, even in the same waters where the word originated.

2012 Risk

If recent history is any guide, 2012 promises to be quite eventful.  We’re entering the new year with continuing financial turmoil across the Eurozone; the US Presidential race is starting in earnest on Tuesday January 3rd with the Iowa Republican Primary; North Korea’s new regime is promising nothing new, meaning that we can expect the same irrational and erratic behavior; we have Iranian sanctions and threats to close the Straight of Hormuz; and we seem to have a ‘new normal’ 9% unemployment in most of the developed world.  For the first time in memory, the most popular New Year’s resolution, as announced by Dick Clark’s Rockin’ New Year’s Eve, was not “lose weight” or “go to the gym more often.”  This year, or rather – last year – it was “Save Money.”  Better late than never, I suppose.

Arguing to myself that a blog about risk management should not itself engage in needlessly risky forecasting, I vetoed the very idea of presenting my predictions for risk in 2012.  So with reckless abandon that comes with New Year euphoria, here goes anyway…

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Downward Trend Risk

“Will the trend continue?  Will the numbers keep dropping?  They’ve been getting worse — every day! — for several months now.  It’s already getting quite bad and it’s only getting worse.  What can we do to reverse the trend?”

I’m not talking about the stock market or the value of European Sovereign Debt.  I’m talking about the number of hours of daylight for people living in the Northern Hemisphere.  Historically, humans did a lot of [what we now consider] crazy things to reverse the very real and downward trend of diminishing sunlight through the fall months.  In other words, they did whatever they could to get the powers that be to increase the number of daylight hours.  Their actions – some gruesome and tragic – were based on their understanding of how the diminishing sunlight phenomenon worked.  Similarly, our actions today to reverse downward economic trends are based on our understanding of how markets work.  My claim is that the current understanding of our economic issues is not much better than the ancients’ understanding of the dynamics of planetary orbits and the Winter Solstice. Read more of this post

Agent Based Modeling

In the past two weeks I’ve been to two very different conferences: The Santa Fe Institute’s annual Business Network Symposium in New Mexico and the Canadian Pension Fund’s Innovation Conference in Bermuda.  The same important topic was discussed at both conferences: Agent Based Modeling.  At Santa Fe, I did the listening.  In Bermuda, I did the presenting.

The traditional way to model economics or markets is called “top down:” you assume a a distribution for a stock’s returns or you assume a certain shape of the interest rate curve or something similar.  From there, you attempt to calculate things like the probability of portfolio loss or the long-term value of bonds.  In essence, you start with a high level assumption of how the world works and you then calculate determine how something of interest behaves.

Agent Based Models are very different: they don’t start with assumptions of how the world works.  Instead, they start with the players who interact – the “agents.”    They then incorporate the individual agents’ observed behavior in a given circumstance and they then let those agents interact.   They calibrate those “near-neighbor” interactions by changing them until the macro behavior of the ensemble resembles what is observed in reality.  In my opinion, it’s a much more natural modeling approach than the traditional mathematical top-down approach.

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Santa Fe Institute, here I come!

Did you know that there isn’t a single non-stop flight from any of New York City’s three airports to Albuquerque, New Mexico?  This matters to me now that I’ve joined the Santa Fe Institute’s Business Network and will be visiting.  The Santa Fe Institute is a “trans-disciplinary” research institute where the main focus is bringing research experts from different disciplines together to work on a problem of common interest.  It’s exactly the approach that I advocate for risk management: incorporating lessons from other fields into my own.  I learned the value of this approach in college, where my university encouraged students and faculty to work on creative combinations of fields.  In my freshmen year we were amazed by the popular new course – the result of a collaboration between a biologist and a psychologist – called the “Biological Basis of Behavior.”  BBB was scary: those who took it learned that our decisions, while seemingly the result of free will, can largely be understood as a combination of genetics and our experiences that manifest themselves as electrical signals in the 3 pound lump of matter encased in our skulls.  Collaborations between seemingly disparate disciplines can lead to novel discoveries.

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Systemic Risk Rising

Last night three newsworthy events took place, any one of which could make for an interesting blog entry about risk management: President Obama unveiled another attempt to reverse the economic slump, a credible (but uncorroborated) threat against NYC and Washington DC near the 10th anniversary of 9/11 was announced, and California, Arizona and Mexico had a massive blackout.  It’s this last event that inspired this blog entry.

At a Santa Fe Institute presentation last year, I got to hear Duncan Watts (Yahoo!’s Chief Scientist) talk about risk management at the micro-level (think individual fund) and at the macro-level (think the entire economy).  Duncan Watts is a researcher and author on the topic of networks and complexity dynamics, with titles like “Everything is Obvious: *Once You Know the Answer” and “Six Degrees: The Science of a Connected Age.”  The topic of the presentation, though, was about how, in the right kinds of networks, the process of reducing local risk can in fact increase global risk.

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This Isn’t Rocket Science…

My firm’s been growing quite a bit recently and there are plenty of new faces around the office.  In one of the ‘getting to know you’ water-cooler type conversations (which never happen anywhere near the actual water cooler), someone asked me why I went into risk management and why I didn’t stay in nuclear physics.  “Well, this isn’t rocket science” I told him.  “It’s much harder than rocket science.”  He thought I was being sarcastic.  Here’s a summary of the explanation I gave:

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Top 10 Risk Analytics

Comments are Welcome.  Please click LEAVE A COMMENT right above this text…

I’m often asked for my “top 10” risk measures, and it happened again just the other day.  The trouble is that a top 10 list is often interpreted as  “No. 10 does matter all that much” and “I really only care about No. 1.”  We love top 10 lists because they provide a linear answer, and our brains love to think linearly.  We really like it when someone tells us what the single most important answer is to whatever we’re trying to solve.  One of the tenets of this blog is that risk analysis, market behavior and economics are not linear systems and that thinking about them linearly is inappropriate and dangerous.

I’ll give examples from two very different non-linear systems: sports and piloting an airplane.  What is the single most important factor in having a winning baseball team?  Surely it’s pitching, because without an A team in the bullpen, good hitters on the opposing team will dominate.  But what about having your own set of sluggers?  Surely the answer is having quality hitters at the plate so you score more runs.  Wait – what about fielding and hustle and smart base running?  Baseball, like all sports, is a complex dynamic system and there is no answer to “what is the most important attribute.”  Of course, that doesn’t stop the sports announcers from attempting to present it that way.  The amount of debate among experts about the most important factor is in itself a pretty good sign that you’re dealing with a complex system.  Similarly, what is the most important thing for a pilot to monitor while flying?  Should she most closely monitor Altitude?  Air speed?  Weather conditions?  Flaps?? Fuel?? The answer is obvious: all of the above, plus some!  I, for one, would never get on a plane whose pilot was only concerned with a “the top 10 things to keep track of.”

So I hope you agree that I’m not dodging the question when I write that the very idea of a “top 10” list of risk analytics is the wrong thing to look for.   Still, some people press the issue and ask me “if you could only have one risk analytic, what would it be?”  My answer, which is not tongue in cheek nor glib nor snarky, is simply “if I could only have one risk analytic, I would not manage the portfolio.”  Just like I hope the pilot would answer “If I could only have one instrument on my airplane, I would not fly it.

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Alphabet Soup

Note: this posting originally appeared in IPE (Investment & Pensions Europe) on January 4, 2010.  This version differs only in that I converted spellings to standard American English.

04 Jan 2010

W, U, V, L? It’s all a symptom of the misleading ‘patternicity’ that dogs traditional macroeconomic thinking, argues Damian Handzy

Much has been written about the shape of the recovery. Will it be ‘V-shaped’, implying as swift a return as we had a drop? Will it be ‘U’-shaped, implying a period of low economic activity before a swift return? Will it be ‘W’-shaped, implying a second crash before an eventual recovery? Perhaps it will be €-, £-, ¥-, or $- shaped, implying that some lucky country will lead the way and benefit handsomely while it does so. If I am forced to pick some symbol for the shape of the recover, then my choice is the Chinese characters 不 and 複, the first of several that together represent ‘uncertainty’ and ‘complexity.’

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