I’m Long for a “Big Short” Oscar

Big Short

[This post was originally published in Risk.net’s Hedge Funds Review]

How would you have reacted if I told you, several years ago, that a Hollywood studio was making a movie about the role hedge funds played in the global financial crisis? That it portrayed hedge funds as the heroes? That within the first few minutes every moviegoer would be paying close attention to a detailed explanation of short-selling? What would an option to invest in such a film have been worth? Simply put: not enough. Today, The Big Short is up for five Academy Awards including best picture, best director and best supporting actor. It does an incredible job of taking a rather dry subject and turning it into an engaging story about a few eclectic characters that millions of people continue to pay good money to see. The two best reasons to encourage others to see The Big Short are straightforward: it entertainingly educates about the complexities of the financial system and, more importantly, it accurately portrays the positive role that hedge funds can play.

The Big Short gets the high-level story right and uses accurate details to support its tale. Hedge funds – indeed all successful investment managers – profit from access to scarcely known information. The Big Short tells the story of a number of persistent investors who didn’t accept the party-line explanation of inconsistencies in the mortgage markets. They dug deeper, asked uncomfortable questions and found opportunity. This is not just the story of a handful of investors who profited from the collapse of the housing market – this is the epic story of how to be successful in life: be different, be bold, find a niche, stick to your convictions and see the job through. Each of the subplots in this movie follow that inspiring David vs Goliath storyline.

Christian Bale’s character, Michael Burry, goes through a meticulous analysis of underlying home loans before deciding to bet against the mortgage-backed securities (MBS) in which they’re collateralised. The movie shows him struggling with his preliminary findings, gathering more data, performing more analysis and finally deciding to bet heavily against the US housing market. Day after day as he waits for the collapse, Blurry records his mounting loses by writing his fund’s negative returns in big numerals on a massive white board for all his employees to see. Chutzpah, writ large. His obsessive-like behaviour endears him to the audience. His lack of experience with MBSs strains his credibility with his mentor and investors. We love an underdog and this part of the story delivers.

Meanwhile, Mark Baum, played by a convincing Steve Carell, discovers the hollowness of the same market as he tours empty Florida housing developments and interviews mortgage brokers who brag about putting modest-income earners into palaces through interest-only loans. Following the very skeptical Baum as he pieces together the underlying fragility in the housing market, you can’t help but root for him. This is great acting and great direction coupled with great writing. It’s one thing to hear Steve Carell’s dripping sarcasm when he repeats what he just learned about “CDO”s but to hear him snide as he slowly pronounces “synthetic…” CDO is what makes movie going worthwhile.

The moral aspect of the entire topic is all too briefly – but quite neatly – addressed by Brad Pitt’s character, reclusive Ben Rickert, who left Wall Street to the comfort of his Rocky Mountain sanctuary. Brought back into investing to help two naïve hedge fund entrepreneurs, he admonishes them, “You just bet against the American economy. And if you win, hardworking people will suffer, so try not to celebrate.” Wall Street could use more conversations like that.

The use of cameos to explain complex financial concepts works rather well. The dialogue between Selena Gomez and Nobel Prize-winning behavioural economist Richard Thaler at a Las Vegas blackjack table is inspired, as is celebrity chef’s Anthony Bourdain’s explanation that subprime mortgages can be made to look appealing through collateralisation, just like last week’s fish can be made into seafood stew. Director and screenplay adaptor/writer Adam McKay makes it clear that these topics deserve to be understood and helps the audience by making the lectures entertaining. He seems to tell us “no shortcuts here – I’m going to force you to pay attention to otherwise boring but important stuff by having celebrities teach you. So there!”

To be sure, The Big Short won’t make everyone in the alternatives industry happy, nor will it change the perceptions of those who believe the solution is a radical reform of Wall Street. But it will explain some of the intricacies of the investment management world and how it operates. And it will allow more people to have an informed dialogue about the proper roles of regulation, free markets, finance and opportunity. And whatever side of the debate you’re on, that’s a good thing.

All in all, this is a movie worth seeing. This is a story worth telling. For those of us in the financial investment industry, this is a movie worth encouraging others to see. It’s entertaining, informative and provocative, just like a good movie should be.

Correlation Doesn’t Measure What You Think!

This article originally appears in my risk.net column in February, 2015, which you can find here.

Take a quick look at the two panels of Figure 1 and estimate the correlation for the two funds in both panels. Really, please do it now. What’s your gut feel of the correlation of each set? If you are like virtually everyone I asked, it is quite obvious that the two funds in the left panel are uncorrelated or possibly negatively correlated while those in the right panel are highly correlated with each other. Estimates for the left range from zero to -0.7, and estimates for the right panel are often above 0.7. In reality, though, the funds in the left pane have a return correlation of +0.95 and the correlation for the set on the right is -0.92. That’s right: the funds on the left are positively correlation, and quite highly, while the funds on the right are negatively correlated.

In the left panel, over this simulated three-year time period, Fund 1 shows a 49% total return corresponding to a 14% annualized return. Fund 2 suffers a 29.6% total loss, or an annualized loss of 11%. Now take another look at that left panel and estimate which of these two funds is more volatile. Some people interpret Fund 2 as more volatile because it suffers a loss while Fund 1 has stellar returns, equating “risk” or “loss” with volatility, but most people recognize that Fund 1’s volatility is at least somewhat greater than the volatility of Fund 2. In fact, the volatility of Fund 1 is 2.5 times greater than the volatility of Fund 2.

Left Pane: who funds diverging in value. The blue fund's total return is 86% while the red fund's total loss is 22%. Right pane: Two funds both increasing in value.

Figure 1. Left Pane: two funds diverging in value. The blue fund’s total return is 86% while the red fund’s total loss is 22%. Right pane: Two funds both increasing in value.

Russian Risk Rising

Sanctions' Effects on Russian Markets. Source: WSJ

Sanctions’ Effects on Russian Markets. Source: WSJ

There are between 25,000 and 35,000 Russian troops amassed on the border with Ukraine, just a few hours’ unhindered drive from Kyiv, Ukraine’s capital. Last week, Putin sent a very strong signal about his intentions to invade the rest of Ukraine, but only to those who could hear it. There are two ways to say ‘Russian’ in their language: one way, “Rossisskii,” is used to describe any citizen of Russia regardless of their ethnicity. Mongols, Chechnyans, Russians and Ukrainians can all be “Rossisskii” if they carry a Russian passport.  And that’s how Putin always referred to citizens of his country, until last week. He suddenly switched to using “Russki,” the form that means ethnic Russian. He called Crimea primordial ‘Russki’ land and its main seaport, Sevastopol, a ‘Russki’ city. Most telling, he actually said Ukraine’s capital, Kyiv, is “the mother of Russki cities.” As this Washington Post article points out, this must have grated on the ears of any Ukrainian listening, as it was a revisionist reference to Kyiv’s role as the capital of the ancient Rus’ civilization. Peter the Great, the ruler of what was then the country of Muskovy, wanted to improve his country’s reputation by bolstering its historic credentials. He decided to rename his country by starting with Ukraine’s ancient name of Rus’ and adding a few letters. It caused an uproar among Muskovites at the time, but Peter prevailed and claimed his neighboring country’s richer and far more ancient heritage for his own. This Identify Theft would prove decisive in helping Peter establish his newly renamed country on the European stage. Peter stole Ukraine’s history and name; Putin now aims to take her cities.

Just a few days ago, NATO’s Supreme Allied Commander Europe said “the (Russian) force that is at the Ukrainian border now to the east is very, very sizable and very, very ready.” For their part, Ukrainian troops have begun digging anti-tank ditches and have placed giant jax-like concrete tank-blockers near the border. Today, the Ukrainian government started broadcasting messages preparing people for the likelihood of a massive invasion in the coming days.

The US and the EU have already levied sanctions for Russia’s actions in Crimea, and have threatened more if Russia pushes further. So far, the sanctions target those in Putin’s inner circle and include visa bans and asset freezes that have even impacted credit card transactions. Because significant equity in Bank Rossiya and Sobinbank is owned by those on the US sanctions list, MasterCard and Visa have stopped authorizing transactions for credit cards issued by those banks. In Brussels today President Obama said that Moscow must consider “the potential for additional, deeper sanctions” if it pushes further into Ukraine. He went on to say “we recognize that in order for Russia to feel the impact of these sanctions, it will have some impact on the global economy as well as on all the countries represented here today.” Just a few days ago US Energy Department said it would permit exports of liquefied natural gas from Oregon to help European nations struggling with supporting further sanctions because of their reliance on Russian energy.

The market has not been kind to Russia in the past few weeks, as this Twitter post from the Wall Street Journal shows: equity markets down 13%, interest rates up about 150 bps across the curves, and the Ruble continuing to fall. In addition, Russia has admitted that it expects between $65B and $70B of foreign capital to leave Russia before the end of March!

If Russia does indeed invade more of Ukraine, we expect significantly more in terms of sanctions and capital flight. As I pointed out in an earlier post, it’s easy to be fooled into thinking that since a portfolio has no direct exposure to Russia that it won’t be affected by these events. In reality, many countries, including Germany, are significant trading partners of Russia’s and their equity and fixed income markets would certainly suffer from contagion.

Stress Testing is a good way to simulate possible market effects of materially increased sanctions or even open warfare. As I pointed out in the last post, modeling would include 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. “Flight to Safety” of capital out of Russia in excess of the $70B already expected in the coming weeks would also be reasonable. In a full military escalation to open warfare coupled with seriously increased sanctions, prudent managers would also simulate a collapse of the Ruble, hyper-inflation and a modern Russian default.

Late on March 26, CNN reported that US Intelligence analyst say there’s a greater likelihood of a Russian invasion than previously believed. The House Armed Service Committee, when it learned of the report, sent a classified letter to the White House expressing concern. An unclassified version of this report said members feel ‘urgency and alarm’ about the information now in their possession.

 

 

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?

LinearRegression-S&P500INDEX-0%Percentile

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.

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.

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.

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.

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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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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The Risk of Success

Switzerland has a problem – they’ve been so incredibly successful at financial risk controls while the rest of Europe falters, that the Swiss Franc now costs too much for most Swiss to afford their own country’s products!  The Swiss Franc is now so expensive compared to the Euro and the US Dollar that Swiss exports are out of reach for foreigners and the Swiss themselves find much better bargains by crossing the border to any one of their Euro-denominated neighbors.  According to this NPR article, Swiss shopping centers are empty and the Swiss now regularly go shopping in foreign countries.

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When does AA+ = F?

I’m not going to add to the litany of voices predicting how much interest rates will rise in the  days and weeks following the the US downgrade.  Nor am I going to write about whether or not S&P was justified in downgrading the US (I think they were).  But I am going to write about a more insidious issue resulting from this downgrade: the fact that AA+ is simply not good enough to pass many funds’ investment governance requirements.

Many different institutions – from Mutual Funds to Pension Funds to Money Market Funds (and even some Hedge Funds) – publish guidelines and restrictions on what types of securities can be held in the portfolio.  The US government’s various authorities limit what kinds of investments can be made by different types of funds – for example, mutual funds have to go through a detailed process to check their concentration limits in a number of different ways.  One of the most common ways for an investment fund to project safety, high quality and an image of low-risk is to limit fixed income investments to ‘AAA’ credit rated securities.  Even if they allow lower rated securities into their portfolio, they often limit their exposure to fractions of what is permitted for AAA bonds.

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Markets Signal US Default Likely

This dangerous game that the US congress and the White House are playing just got very real.  How real?  The Credit Default Swap market just gave a strong signal that a US Government default is likely.

Today's curve (red) is 'inverted' compared to all previous curves. This usually signals that the issuer (the US Government in this case) is about to default. Source: Bloomberg.

The figure tells the story: the red curve (today, Tuesday July 26) is ‘inverted’ as compared to all previous curves (in other colors).  When this happens to a company, it’s a signal that the company is likely to default on its loans.  In this case, it applies to the US Government.  In other words, market participants are putting their money on a higher probability of default.

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Safe Ride

Micro-post.  I’m heading to Chicago for a major hedge fund conference today and because of airline merger monopoly on hub-to-hub routes, I’m flying out of LaGuardia instead of nearby Newark Airport.  As the car arrived to take me to the airport, my wife said “Have a safe ride to the airport” and I immediately smiled.  “That’s exactly right!” I let out.  “You got it!  Going from New Jersey to Queens by car is much riskier than flying from New York to Chicago.  Instead of wishing me a ‘safe flight,’ you wished me a ‘safe car ride’.  Thank You.”  Has my wife been reading my blog?

Making Sense of Risk Reports

We’re doing more and more business with firms that are less and less familiar with absolute risk measures.  Many institutional investors are familiar with Relative risk measures, like Tracking Error and Beta, but they are much less familiar with things like VaR, Stress Tests, Correlations, and all the other analytics that are standard fare for risk managers.  As Investor Analytics works with more of these institutional investors, its become clear to me that they could use a hand in interpreting their risk reports.  Most of our competitors (which my marketing department has told me it’s never a good idea to mention by name — see, I can learn something) don’t offer interpretation or consulting services.  They just produce the reports and send them out.  But I think it’s much more valuable to have a guide on how to make sense of them.

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Volcanic Ash Redux

My very first blog post – Volcanic Ash is Financially Risky – pointed out that the European authorities did a foolish thing by shutting down European air space without even bothering to test if the ash had made it dangerous to fly.  They assumed that it was dangerous because of only one other known incident of a jet flying through a different ash cloud years ago. The point of my blog entry was not that they should have kept the skies open, as some have interpreted it.  Rather, the point is that they should have closed the skies and immediately ordered tests to verify their assumption that it’s dangerous to fly.  Instead, they waited two or three days until the Dutch had had enough of this ridiculous approach and flew tests themselves.  Lo and behold – it was safe!

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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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Visual Correlations Part 1

I like pictures.  In my copious spare time I’m a black-and-white photographer.  I use film.  I have my own darkroom.  Maybe that’s why I think of correlations in terms of diagrams instead of just as numbers.

Correlations are the key to understanding how diversification lowers financial risk, and diversification is one of the simplest and oldest ways that humans have managed risk.  By spreading around the chances of bad things happening, you lower your risk.  “Don’t put all your eggs in one basket” is obvious, even to children. Most importantly – it works.  Financial portfolio management implements this advice by applying investment guidelines like “no more than 5% invested in any one company.”  This automatically forces the investments to be spread over at least 20 different companies.

But what happens if those 20 companies’ values/stock prices start rising and falling together – the equivalent of putting your eggs into 20 baskets and then carrying them all down a steep hill?  The relevant question then becomes ‘How much do you reduce your risk by spreading the investments across many companies if their stock prices are related?’  The short answer is that it all depends on the correlation between the assets.  If two stocks are highly correlated, then putting money into the second stock doesn’t lower the overall risk at all.  But if the two stocks are uncorrelated, then you can reduce your risk substantially by investing in the second stock. Read more of this post

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