Ironic Lesson

Today I gave two back-to-back presentations at a conference on Financial Risk Management.  In preparation for this day, the conference organizers required me to send them my slides months ago.  I explained to them that I use PowerPoint on the Mac and that the advanced features I use don’t always translate well if they’re putting the slides on a PC.  So after they put the slides into the official template with the right colors and all that, they sent the file back to me to make sure everything still worked.  All this happened two months before I got here.

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Beta to the Max

Last week, I was having a really hard time explaining myself until I realized that I was trying to explain in words how to take advantage of the fact that humans perceive images faster and better than we perceive words or numbers.  This has a lot to do with our evolutionary history and with behavioral finance.  My example was the interpretation of a fund’s beta – and my point was that it’s really really hard to get the right interpretation from the numbers without the right picture.  In this case, the right picture makes a BIG difference.  It was really frustrating trying to give an example of what I meant until I realized that I was suffering from the same thing I was trying to explain!  Wow, I guess I really can be that thick.  I should have been showing her the pictures rather than talking about them.  So that’s exactly what I then did. Read more of this post

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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Gauss’s Fat Tails

The term “fat tails” is thrown around with what I consider reckless abandon.  Most times I find that people use it without having an appreciation for what it really means and then they make the wrong conclusion.  So, I’m going to take a stab at explaining what I think a correct interpretation is.  The first and most prevalent wrong conclusion is that all quants and financial models underestimate the risk of extreme events.  Wrong – there are plenty of ways to reasonably and accurately model tails.  The second is that all models use the Normal Distribution.  Wrong – there’s a host of distributions that can be and are used.  A corollary to that wrong assumption is that VaR (Value-at-Risk), in particular, always uses the Normal Distribution, which is also wrong.  VaR makes no assumptions about what distribution is used, but I’ll have an entirely different set of posts about VaR.  This post will be part one of what may be a series of posts on the topic of fat tails in particular, and this one will be limited to discuss actual data and comparing it to the most commonly used distribution, the Normal curve.

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Opportunity Risk

If you don’t want to read about September 11, I suggest you skip ahead to the alternative introduction.  My brother’s Facebook post today, that he’s glad I wasn’t hurt in the attacks, got me thinking about how different things would have been for my family had I died that day.  My first thought was that my youngest child would not have been born and I was deeply saddened that we (or rather they) would have never gotten to know that wonderful person (after I did the math, it turns out she had already been conceived but I ran with the notion that she had not, just to see where it took me).  In this case, the risk was not of losing something valuable (the father), but rather the risk of never getting something good (the future child). We don’t usually think of risk this way – risk is almost always cast as the risk of losing something you already have.  It’s not cast as a chance of not getting something beneficial that you don’t yet have.

Alternative introduction: One of the most amazing things I was taught by a college physics professor was to think of time flowing “backwards.”  When I first heard of this, it was just bizarre to me.  But she was persistent: “nothing in the equation forces time to flow in a particular direction, so it may help your understanding, if you think of time flowing backwards,” she told me.  When I entered financial services several years later, that’s exactly what helped me understand short selling.  Instead of buying low and selling high, a short seller is first selling high and then at a later time buying low. Conceptually, it’s no different than traditional investing except with the arrow of time flowing in the opposite direction (of course, in practice, it’s very different because of borrowing requirements and other realities).  I decided to apply this “time reversal” concept to risk management: instead of thinking about risk as the risk of losing something you have (today), why not think about a different kind of risk: the risk of not getting something you want in the future.  We don’t usually think of risk this way – risk is almost always cast as the risk of losing something you already have.  It’s not cast as a chance of not getting something beneficial that you don’t yet have.

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Risk Reading List

I’m often asked for my suggested reading.  Here are some of the books that I’ve found especially insightful, listed by sub-topic.  I also include a favorite authors section – basically, I read everything by these people whenever they publish a new work.  This post will be updated from time to time as new books or authors come across my desk.

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Bayesian Learning

We know that probabilities are hard for us humans to understand and estimate.  Even seasoned professionals are fooled by seemingly easy problems (take, for example, Marylyn vos Savant’s Parade Magazine fiasco with the Monte Hall Puzzle), and sports fans around the world are fooled whenever they subscribe to the notion that their favorite player is “due” or is on a streak.  I hope I didn’t just alienate too many readers.

Anyway, today I learned about research that investigates some of the evolutionary origins of our troubles with probabilities, and seems to indicate that even purely rational thinkers would come to the same conclusions if their world view was similarly shaped (skewed?) as ours.  In other words, human decision making should be viewed as rational within a model-based framework, and that our decision making is not consistent with blank-slate / model-free learning.

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