Russian Ruble Risk Redux

Ukraine Pipeline

Gas pipelines from Russia through Ukraine

Putin’s Ukraine Gambit is reverberating through the markets: Monday, the first trading day after Russia’s invasion of Crimea, the Russian Micex stock index was down 11% representing a drop of about 80B USD. The commodities in play are Oil, Natural Gas (both of which Russia exports), Corn and Wheat (both which Ukraine exports). The Ruble, the Euro and the German Bund are also at risk. It’s not just the supply of the commodities that may lead to a rise in their prices – there’s also a real chance of economic sanctions being imposed on Russia by both the EU and the US, which expressed the possibility of economically “isolating Russia”.

According to the Telegraph, Monday’s emergency meeting of EU foreign ministers shows important divisions in countries’ willingness to impose sanctions on Russia: eastern European countries led by Poland and Lithuania called for strong sanctions but Germany and Italy, both of which import a lot of Russian gas, wanted to soften the message. The EU delayed any decisions about expelling Russia from the G8 until Thursday. They gave Russia until then to return all of their forces back to their base in Crimea or face sanctions like visa restrictions, arms embargoes and asset freezes. Should Russian troops still be occupying Crimea come Thursday, we should expect to learn from the strength of the EU’s sanctions just how important Russian gas really is to Germany and Italy. But US Secretary of State John Kerry said on the Sunday morning talk shows that the US is prepared to sell Europe Natural Gas to make up for any diminished supply due to any sanctions that are imposed. The US finds itself energy rich, and wants to use that leverage in its foreign affairs efforts.

Ukrainian Flag

The bottom half of Ukraine’s flag represents the country’s wheat fields

According to the NY Times, Germany imports 24% of Russia’s Natural Gas exports and Italy imports 11%. It points out that Germany has a 6% trade surplus with Russia but that it gets about 75% of its gas and oil from Russia. Current European stockpiles of gas is high – about 2 months’ worth, enough to take them through the remainder of the winter. Ukraine’s agriculture exports are significant: it is the fifth largest wheat exporter and the world’s third largest corn exporter. Archer Daniels Midland and Bunge having large operations in Ukraine. Ukraine’s flag pays homage to its nickname “The Breadbasket of Europe” – the yellow bottom half of the flag represents the flowing wheat fields found throughout the country. Any war in Ukraine would seriously disrupt that distribution, as most of the farmland is in the Eastern part of the country, near Russia.

Back in 1998 before the Ruble collapsed, many investors believed themselves to be insulated from Russian risk by investing in German stocks, only to find a high correlation of the Ruble with the DAX, which suffered at 37% drop in 1998 from which it too over a year to recover. Other markets, like the US and Japan, has losses of about 20% and took only a few months to recover. Today, Germany is still highly dependent on Russian exports, so we should look for any impact on Russia – whether through sanctions or because they decide to attack – to reverberate through the German markets.

Stress testing this situation includes modeling spikes in energy prices and agriculture products. I think moves of 10% to as high as 50% are not out of the realm of possibility, depending on how the political and military situation plays out. Also be sure to stress correlations quite high between Russia and its main trading partners – Germany, Italy, and France to a lesser extent. Contagion to the interest rates and bond prices in Russia’s largest trading partners should not come as a surprise to anyone.

On the upside, if Russia does back down and withdraws its military threat, look for a new more democratic and European leaning trading partner to emerge in the coming years. The EU announced a $15B loan and grant program today and is eager to sign the previously stalled EU Association Agreement with Ukraine, whose new government is equally eager to sign. As the largest country by area in Europe (yes, Ukraine is slightly bigger than France) with a highly-educated population of 46 million people, if Ukraine is able to adopt real democratic reforms and European business practices, the business landscape on the Continent could look quite different a decade from now.

What Goes Up Must Come Down?


Relationship between one year’s returns and the next. Essentially, there’s not much of a relationship except in cases of extreme losses, which are often followed by a better year.

Investor Analytics just published the fifth in a series of articles in a new column I have in Risk Magazine’s Hedge Fund Review, which you can find here. The topic for this article is both simple and profound: since 2013 was a great year for stocks, chances are that 2014 will be bad so that the stock market maintains its long-term average. The phenomenon is call “reversion to the mean” and is the underlying logic behind thinking that a sports player is “due” (a fallacy) and for the notion that a tall parent is more likely to have a shorter child (a truth).

We looked at the returns of the S&P 500 over the past 86 years and constructed rolling 1-year windows to generate over 20,000 data points to examine in our hunt for signs that if you have a “good year” that the next year has an increased likelihood of being a “bad year”. It turns out that it’s just not so. You can read the article for the details, but it’s very clear that having a good year really doesn’t change the odds of the next year being good or bad. The average return of the next year is slightly lower than usual, but the range of returns is tremendously wide. Specifically, the overall average for the S&P500 is 7.5%, with a volatility of 20%. That means that for any given year, at the 95% confidence interval, the stock market gives a return somewhere between -25% and +40%. But following a year like 2013 (up 30%), the market returns on average 4.7% with a volatility of 17.5% which translates to a 95% confidence interval between -24% and 33.6%. See the big difference? Neither do I.

The plot in this post shows the overall relationship between two subsequent years: the first year on the horizontal axis, the second on the vertical. The large blob in the middle represents about 98% of the data, which essentially shows that one year tells you next to nothing about the next year.

The graphs we published showed a striking lack of relationship between one year’s returns and the next, except in the most extreme cases. Our conclusion is simple: your risk is not really changed from last year, and this year is has just a good chance of being good as it does of being bad. It’s up to you to make the most of it.

The Most You Can Lose?

In yesterday’s Wall Street Journal, there’s an article entitled Tracking Risk Isn’t So Easy that overall is a solid description of some of the problems with measuring risk and comparing quantitative risk levels from one firm to another. The article is worth reading because it redeems itself quite nicely despite a major blunder early on. You’d think that an outfit like the Journal wouldn’t make a rookie mistake in one of the first few paragraphs where they wrote: “…value-at-risk, or VAR,[is] the most common yardstick of trading risk. VAR is designed to measure the maximum trading losses faced by a bank in a single day” (my emphasis). Wrong. Very, very wrong. Believing that VaR measures the most you can lose is a mistake that reveals just how misunderstood risk management is, even by experts at the WSJ.

What if someone asks for the risk of a river overflowing and flooding a city? One response might be a statistical analysis to calculate the “Floors-at-Risk” or FaR. I can just image the deadlines: “This month’s flood had a FaR of 2.5”, and the article would go on to explain that meant experts expected water to rise to 2 1/2 floors before receding. Would anyone ever suggest that the FaR number is the highest the water could ever get to? Of course not! Everyone understands that water can rise much farther than the expected level. It’s pointless to talk about the “maximum possible height” of the water. What matters is the associated likelihood of each possible water height, with the expectation (and hope!) that the likelihood drops off quickly with increasing height.

The Value-at-Risk number is similar: it’s an estimate of how bad things can get most of the time, with the same expectation of diminishing likelihoods for bigger loses. VaR is certainly not the most you can lose. In just about every case involving “plain vanilla” securities like stocks and bonds, the most you can lose is everything: 100% of your investment (in cases of derivatives, you can actually lose more than that amount). So thinking about the most you can lose is not really informative – rather, risk managers talk about the probabilities associated with losing various amounts.

Investor Analytics once had a client, years ago, confused by this: they called to ask how it was possible that they lost more than their “Value-at-Risk” number. I pointed out that if, in fact, their VaR number was set (by them!) to be at the 95% threshold, that they had to lose more than that VaR 5% of the time – that’s what the 95% level means: it’s true 95% of the time. In other words, it’s not true 5% of the time so they should expect to lose more than this number 5% of the time which works out to 1 day out of 20 (there are 20 business/trading days a month). In this case, losing more than the VaR amount should be as common as the full moon! Unswayed by this line of reasoning, they again asked how it was possible to lose more than that number. I was tempted to point out that they could have sold all the securities, taken the cash and used a match — that would certainly lose more than any reasonable estimate of risk! Of course, decorum dictated that I phrase it a bit more politely.

Although yesterday’s WSJ article got this one very important point dead wrong, the rest of the article is quite good about explaining the difficulties in comparing different banks’ VaR estimates and even in understanding a proper interpretation of a particular bank’s calculation. There are so many acceptable variations in the VaR calculation methodology that it simply cannot be relied on for meaningful comparisons. Unfortunately, that doesn’t stop everyone from trying. In attempting to impose a standard measure of risk in VaR, regulators have given the impression that there is consistency in interpretation. But because banks and other investment managers are given so much latitude in acceptable calculation approaches, time-horizons and confidence intervals, setting that false expectation will inevitably result in bigger problems down the road.

Be Prepared

The scout motto tells us to be prepared. My children’s schools practice fire drills and intruder drills as often as monthly. And first responders practice the “be prepared” philosophy as well. At least Boston’s clearly does. I just read an inspirational article in the Wall Street Journal about the preparedness of their emergency medical teams and the speed with which they were able to save people’s lives. “The efficiency of the rescue reflected careful planning, heroic execution and elements of good fortune.” The article went on to state that “Rescuer reaction was so instantaneous that it appeared to be rehearsed.” It appeared that way because in fact, it was. I know of no better way to be prepared than to rehearse. Thespians do it before the curtain rises, politicians do it before a debate and athletes do it before a game. So why don’t more portfolio and risk managers do it before a market crash?

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Doctor, Heal Thyself!

A client recently emailed me this link to an article about JP Morgan having discovered an error in their firm-wide calculation of Value-at-Risk, the industry standard measurement used to quantify risk.  His email concluded with:

“They are using a spreadsheet!!!”

You read that correctly.  JP Morgan – the firm that famously invented Value-at-Risk in the 1990’s – is apparently using a spreadsheet for this calculation.  This revelation is simply astounding.  If true, it would mean they really are sitting on a house of cards.  The article quotes the JP Morgan Task Force on VaR: “the spreadsheet divided by their sum instead of their average, as the modeler had intended. This error likely had the effect of muting volatility by a  factor of two and of lowering the VaR…. It also remains unclear when this error was introduced in the calculation.”

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Fiscal Cliffhanger

So will we go over or won’t we?  Every day, it seems the answer is different.  Economic brinkmanship.  Today is the first time I heard serious talk of strategically going over the cliff to strengthen a negotiating position, reminding me of Henry Kissenger’s MAD Strategy (Mutually Assured Destruction) during the Cold War.  Today is also the last day for negotiations or voting before the Christmas break, making it look more and more likely that things will get worse before they get better.

There have been lots of commentaries about how ‘ordinary citizens’ should prepare for possible outcomes: higher taxes on some (maybe all?) of us and fewer government programs, but I haven’t seen much on how this might effect markets and portfolios.  How might a good risk manager go about analyzing this situation?

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Hurricanes, Correlations and Wall Street

This post is based on an interview I had with Institutional Investor that you can read here.

News flash: New York is a coastal town and the entire Wall Street area is only a few feet above sea level!  For anyone not familiar with NYC geography, Manhattan is an island sandwiched between two rivers (Hudson and Harlem), an estuary (known as the “East River”) and NY Harbor, which is open to the Atlantic Ocean.  Click on the map to see in detail what New Amsterdam looked like in 1660 when it was still controlled by the Dutch.

Lower Manhattan in 1660

Note the wall on the right side of the map (North), where Wall Street now stands. Follow Prince Straet south from the wall until first bridge over the canal.  Investor Analytics is located on the corner of what was Begijn Gracht (Beaver Street) and that main canal, now Broad Street.  The NYSE occupies the corner of Prince (also now Broad Street) and Het Cingel (the Wall), just North of us.  All of this land is in the flood zone, as it was when the Dutch ran the place, and they know a thing or two about dealing with water.  For the past 400 years, this island has been an important part of world commerce, and during that time it has been hit by quite a few hurricanes.  According to NYC’s Office of Emergency Management, Lower Manhattan was completely flooded as far North as Canal Street during a hurricane in 1821, and a category 3 hurricane hit the city in 1938.  I count eight different significant storms hitting or affecting NYC from the OEM’s website: 1821, 1893, 1938, Carol, Donna, Agnes, Floyd, and Sandy.  That’s an average of 4 per century – quite a bit more than the “one in a  hundred” we hear about.  Given the reality that Earth is getting warmer, regardless of the cause, and the Northern Atlantic can support larger storms, we should expect even more storms of such strength or worse to hit NYC and other Northeastern coastal cities.  What’s a business located in such a city to do about it?  We at Investor Analytics have a few ideas, based on the very same tools we offer our clients to avoid financial risk.

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Italy’s Penchant for Convicting Scientists

No, this post isn’t about Galileo’s persecution and how Italian authorities suppressed scientific advancements for the past four hundred years.  But that tradition appears to be alive an well on the Apennine Peninsula.  After today’s verdict – in which seven geophysicists were convicted because they didn’t predict an earthquake– all I can say is that Italy really doesn’t deserve to have scientists anymore.  Four hundred years ago they burned Giordano Bruno for his heliocentric beliefs.  Ten years later, they confined Galileo to his house for the same crime.  Today, they convicted seven risk experts for not predicting an earthquake.

The earthquake leveled L’Aquila.

It is sad but true that the Italian city L’Aquila lies in what you might call earthquake alley.  It’s been hit many times in the past and I’m quite sure it will be hit again.  But the matter before the court was about the quake that struck on April 6, 2009 and that geologists in charge of risk didn’t warn the people enough.  The prosecution stressed that their crime was not an inability to predict the quake, but rather, that they did not adequately characterize the risks to people who live on a peninsula known around the world for catastrophic volcanoes and earthquakes.

Let that sink in a bit.

You can read the BBC account of the verdict here.  It stretches the farthest limits of my imagination to try and understand this court’s decision.  It demonstrates a complete failure to understand risk, chance, probability and uncertainty.  The prosecution maintained that the scientists made misleading statements and used the power of authority to put people in danger.  Well, duh.  Probabilities – chance – is inherently unclear and difficult to understand.  To all but the most knowledgable experts, this stuff is misleading, difficult to grasp, and easily misinterpreted.  And fundamentally, it is impossible to ‘adequately characterize’ the risks of a single event.  It’s impossible — even after you know the result!

If I make a probabilistic prediction that something has an x% chance of happening, it is fundamentally impossible to know if I was right or wrong by looking at only one event.  If I say there is only a 2% chance of rain tomorrow so you don’t need to take your umbrella and then it rains, that doesn’t mean I was wrong.  It’s quite possible that we just happened to have been in the 2% zone of “rain.”  In order to test my accuracy, I need to make several predictions and each of then needs to be tested.  And it is impossible – not hard mind you, but impossible – to correctly determine if a probabilistic prediction is accurate with only one observation.  In today’s court case, the judge clearly believes that the risks were higher than the scientists said.  This is probably the case because he only sees this one result – which was unfortunately fatal.  But he fails to understand that a fatal earthquake can occur even if the chance of it happening is extremely small.  And the chance of an earthquake happening at any point in time is extremely small.  So exactly how much warning should the geophysicists have given to these inhabitants of earthquake prone Italy?!?  Not more than it deserved, which is about as much as they gave.  Accurately.  His ignorance of probabilities results in a no-win situation to anyone who understands even the basics of risk management.

I can get quite passionate about science, education, and about a real need to understand probabilities even on a normal day.  In this case, my blood is boiling.  It’s simply sickening to think that experts’ careers are over and they are suffering in jail because the person in charge of deciding if they should suffer is ignorant of a basic understanding of what they’re accused of failing to do!

The seven scientists were sentenced to prison and barred from holding public office after only four hours of deliberation.  They were all members of the National Commission for the Forecast and Prevention of Major Risks.  I wouldn’t be surprised if all of the other members resigned today and left.  I know I would.

Occupying Wall Street

Investor Analytics is moving back to the Wall Street area.  We’ve taken the entire 25th floor of 55 Broad Street, just a few doors from the NY Stock Exchange, to serve as our corporate headquarters.  For the past several years, we’ve had an office in New Jersey and a small office near Grand Central, which made for convenient meetings in NYC.  But we’ve expanded a lot in the past year – we’re up to 35 people – and we’ve needed a single space where we can all work together, where we can meet clients and business partners, and where we can continue to attract top talent to join our firm.

Floorplan of the new headquarters (click to enlarge)

When we started looking for space last Spring we focused on midtown Manhattan, especially near Penn Station, and on parts of Jersey City.  But lower Manhattan was just too compelling – Commercial rent near Wall Street is about half of what it is in midtown.  And landlords in the Financial District pay for office build-outs (that means walls, carpets, doors, etc.) and give rent-free periods of several months that midtown and Jersey City landlords just don’t offer.  Besides, there’s a vibe to downtown that we like.  It’s the oldest part of the city and it’s steeped in history going back to 1624 when it was founded as New Amsterdam, and it’s never lost its Dutch roots: commerce and tolerance.

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Is It Better To Have Loved and Lost?

Alfred Lord Tennyson may have had a thing with words, but he didn’t always get his facts right.  But then again, a good poet never let reality get in the way of composing compelling verse!  On this Valentine’s Day, let’s take a closer look at Tennyson’s haunting words (In Memoriam: 27, 1850):

“I hold it true, whate’er befall;
I feel it, when I sorrow most;
‘Tis better to have loved and lost
Than never to have loved at all.”

According to both behavioral economists and recent advances in neuroscience, it’s actually much worse “to have loved and lost / than never to have loved at all.”  In fact, it’s twice as bad to lose the same amount than to gain it.  The amount of pleasure we extract from winning something is about 1/2 the amount of pain we feel at losing the same amount.  In other words, losing a love imposes as much grief as there is pleasure in having two such loves.  Perhaps Tennyson should have written:

“The mind holds it true, whate’re the mood
Avoiding loss is what we do;
To have loved and lost is as bad for us,
As loving twice is good.”

I know, I know: “stick to your day job.”  So why do behavioral economists claim that the pain associated with losing something is twice the strength of the pleasure at gaining the same thing?  Here are three supporting stories:

  1. They’ve conducted plenty of experiments with physical objects (coffee mugs seem to be the canonical choice) in which students are divided into two groups: mug owners and mug purchasers.  The owners are allowed to take the mug home that night and the next day they have to sell the mug to one of the ‘buyer’ students.  Sellers usually want about twice as much as the buyers are willing to pay.
  2. Imagine a simple game of fair chance (50/50) in which you bet a certain amount of money.  Tails, you lose your money.  Heads, you win $X.  When people are asked what value of $X would entice them to play — the answer is that X has to be twice the amount you can lose.
  3. In brain studies, they have measured a factor of two in the sensitivity of the brain to pain than to equal pleasure.  For some reason, getting and losing a certain amount of gourmet chocolate is the canonical prize.

In all of these studies, it seems that the human brain is twice as sensitive to an object’s loss than to its gain.  If only poets read more science, they might get it right!

But hang on … perhaps Tennyson knew this all along, and his words are meant to express how valuable love is in the first place – so valuable that it’s worth the (doubled) pain of loss!

Now that is completely irrational.  And it’s also very human.  🙂

Up In Smoke

I started preparing for a cross-industry conference I’m excited to be speaking at this Spring on the topic of “Innovations in Risk Management.”  As you know, I’m a big fan of interdisciplinary applications to improve risk management, and I’ve often made analogies with the transportation (air travel) and medical industries to make my points about financial risk management.  Today, while thinking about different industries’ approach to risk management, I received the following email from the Headmaster at my eldest son’s school (names removed for anonymity):

Dear Parents,Today at around 10 am, we had a small fire at school in the boys bathroom nearest the ****** Room.  The fire occurred close to a plastic device, which emitted a significant amount of smoke, and our alarms sounded.  We evacuated promptly and determined that all of our students and employees were safe and accounted for.  The fire department responded promptly and put out the smoldering remains of the fire.

The fire department began an investigation of the fire, as did the responding police.  I do not have a report yet about the cause of the fire.  Health department representatives also arrived on the scene.  While this activity proceeded, we moved the students into ****** Gym and awaited their instructions.  Pizza was ordered for the Lower School kids, and they returned to their classrooms at around 11:30, where they had lunch.  Near noon, we received clearance to serve Middle School and Upper School lunch at the usual time in the ***** room, and then resumed classes.  We are having the affected area assessed for clean-up purposes and will move promptly to restore it to working order.

Safety is always our paramount concern, and we conduct frequent evacuation drills so we will know how to conduct ourselves in case of a fire.  That practice proved very useful today, and the students handled themselves with patience and grace.  We will continue to investigate the cause of the fire.

Best regards,

Head of School


After I realized that no one was hurt and that everything is OK, it hit me that fire drills are another excellent example of best practices in risk management.  Knowing what to do before an emergency/crisis makes all the difference, and it’s on the list of best practices produced by the Greenwich Roundtable, on which I participated.  I have to remember to thank the Head of School the next time I see him for pointing out to me the obvious example of good risk management.

This will also be a good way to start the presentation at the cross-industry conference.

No (prospect of) Pain, No (prospect of) Gain

Did you know just about every year, someone falls to their death from the Rim of the

Following the Park Ranger along the Kaibab trail.

Grand Canyon, but that no one has ever fallen off the trail while hiking into the Canyon?!  When I heard this – from the National Park Ranger who was guiding us down the one and a half mile South Kaibab Trail to Cedar Ridge in the Canyon this past summer – I couldn’t help wondering why.  The trail is somewhat narrow – about six feet wide, with a sheer drop on one side.  But the Rim has a sturdy fence or stone wall to protect all those would be Ansel Adams-es.  Why do people fall at the Rim but are OK on the trail?  The explanation I settled on last summer came up in a conversation last week on a seemingly unrelated topic: how to grow a successful business.

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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.

Correlated Stresses

No, this post about “Correlated Stresses” isn’t about having both your in-laws and your weird uncle over for the holidays.  Nor is it about how your trainer at the gym has you work on abs, biceps and cardio all in the same session.  But it is about two very different ways to perform ever increasingly popular stress tests of financial portfolios.  In a nutshell, uncorrelated stresses are your worst nightmare: everything goes haywire at once.  Correlated stresses, on the other hand, only allow one thing to go haywire and then use realistic assessments of how badly every else will react.  In some sense they’re more realistic, but they may not capture enough severity.  In other words, they may not really be all that stressful.

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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

Biggest Risk

“The biggest risk is hiring the wrong person.”  That was my swift answer when asked to identify the most important risk facing hedge funds.  In that case, I was talking about hiring the wrong Chief Risk Officer — the person who sets the risk policies and monitors and enforces the risk management practices of an asset manager.  Little did I realize how applicable my advice was to my own firm.  At 25 people, it’s safe to argue that my firm is at material risk of hiring ‘the wrong person’ every time we make an offer.  It’s especially true for the person who is our equivalent of a ‘risk manager’ – the person who makes sure bugs don’t make it through the code and errors don’t end up in client’s reports: our head of Quality Assurance.  I can now with personal experience enhance my previous answer: the biggest risk facing any business is hiring the wrong person.

Two weeks ago we suddenly found ourselves needing to hire a new Head of Quality Assurance and my highest priority became writing a new job description.

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Volatility is not Risk

At a Pension Fund industry conference last week, I saw a rather small diversity in quality of presentation but a large diversity in quality of content.  Every speaker was polished and held the audiences’ attention (good!) but about half of the presentations were, well, void of real content.  This is an ongoing problem in most industries — pundits are chosen not because their content has passed some independent tests but rather because the speaker has risen to prominence in his/her field and is therefore considered “an authority.”  Pardon my bluntness, but appeals to authority are not exactly the hallmark of critical thinking or knowledge transfer.  In this case, several of the speakers referred to risk as equivalent, or at least reasonably measured by, volatility.  Ugh.  I made a very clear statement when it was my turn at the microphone: “Volatility is not Risk.  Volatility is what happens every day.  Risk is what ends my career.”  For effect, I even stopped and said “it’s worth repeating slowly.  V-o-l-a-t-i-l-i-t-y    i-s    n-o-t    R-i-s-k.

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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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