The Asymetry of Death

I’ve been waiting to post this for Halloween, when most of us get dressed in fun costumes or walk around with our children in a light-hearted celebration of candy.  Not too long ago the day was All-Hallows’ Eve, the day before All Souls’ Day, when the spirits of departed loved ones were honestly believed to come back and share a day (and a night) with the living.  The rite goes back to pagan celebrations of the harvest and spirits of the dead.  For thousands of years, this was an attempt to deal with the inevitable reality of death and the harshness of non-existence that awaits us all.  That reality, though, has some very real consequences for how we deal with risks. Read more of this post

How Do You Know It Works (part 3)?

In this 3rd part of  ‘How Do You Know It Works” I’m going to cover three different popular ways to measure risks and show how to tell if they work or not (I know, I know, that part 2 cartoon is a cheap way of making this a trilogy-post but I’ll take what I can get).

What we’re talking about is much wider than financial risk, but it’s also at the very heard of financial risk management:  How do you know if your risk measure is worth the paper it’s printed on or the computer it runs on?  Here are some things to look for in evaluating a particular forward-looking risk analytic:

  1. It can be calculated from available information and isn’t tied to a proprietary piece of data.
  2. The analytics’s accuracy can be quantitatively determined in some way.
  3. The assumptions going into the analytic can be clearly communicated.
  4. Users of the risk analytic can have an indication of when it is applicable and when it is not.

I’m just going to concentrate on the second feature: evaluating the quantitative accuracy of the analytic.  Let’s consider three different analytics:

Worst Case Loss
Investor Analytics
is sometimes asked to calculate a firm’s proprietary risk model which falls into the category of “worst case loss.”  I’ve heard it described many different ways, using many different formulas / techniques / methodologies, and it usually starts with a set of reasonable inputs but boils down to a series of alchemy-like adjustments that render the thing useless.

Read more of this post

How Do You Know It Works (part 2)?

xkcd is a great cartoon

This pretty well sums up what I meant by the previous post.  It’s really easy to be fooled by seemingly ‘real’ things.  This is another good test of whether or not something actually works:  If something works, chances are good (but not perfect) that someone will be taking advantage of it.

Here’s a link to xkcd, where this is from.  Enjoy.

How Do You Know It Works?

I participated in a panel yesterday at a hedge fund conference in NYC.  What made this panel a little different was that I wasn’t the first person to talk about how important it is to know the limitations of risk models.  In fact, I was the THIRD person.  The question was posed “what makes an outstanding risk manager?”  The first person to respond included in his answer something along the lines of “an outstanding risk manager asks the question – how do I know the model works?”  Bingo!

How do you know if any prediction works?  Over the ages, people have tried all sorts of methods.  Let’s take a look a few of them.

Method #1: you believe it if an authority figure tells you it’s true. Like the village elder.  Or the shaman.  Or a celebrity.  Or Congress.  Assessment: inconsistent results at best.  Let’s try something else…

Read more of this post

I’ll Gladly Pay You Tuesday for a Hamburger Today

Yesterday’s Wall Street Journal article about Facebook’s privacy issues and this morning’s NPR story about people giving up private information in order to play games got me thinking about the risks people face by agreeing to something pleasureful today, only to pay for it some time later.

Wimpy, the man who will gladly pay you Tuesday for a Hamburger today!

Pay Tuesday, Play Today.

Just like poor old Wimpy, Facebook users are being tempted by something they want immediately – in this case, it’s not about a moist delicious burger but about playing one of the popular games that a friend just invited them to play, like MafiaWars or Farmville.  Never mind if you like these games or not – the point is that many people play them and that means we can learn something about risky behavior from them.  The price for access to the game is giving up some personal information – birthday, location, friend list, possibly income info, etc.  The risk, of course, is that the information is used in ways the person doesn’t approve, such as targeted advertising or examining friends’ credit ratings to estimate your likelihood of defaulting (after all, we are judged by the company we keep).  In order to be allowed into the game, you have to agree to share the information.  And given how many people play these games, it’s quite clear that many feel it’s worth the price.

Read more of this post

What Went Right?

Note: This article was originally published in IPE (Investments & Pensions Europe) on 1 October, 2010 under the title “Never mind what went wrong in the financial crisis, what went right?

A lot has been written about all the things that have gone wrong leading up to and during the financial crisis, and for good reason. After all, a lot did go wrong, and we are still reeling from it. Over the same period, though, many things – largely unnoticed – went right. Let’s remember that not everyone who considered Madoff actually invested with him, not every bank or mortgage broker made undocumented loans, and not every fund lost terrible sums of money. It’s worth taking a look at what these people and institutions did to prevent catastrophic loss, especially because their tactics turn out to be both practical and widely applicable.

Read more of this post

Risk at the Limit

You have a problem.  A big problem.  And it’s unfortunately a common problem for a Risk Officer.  What do you do when your risk measures are all in the red zone except for one of them, which happens to be your boss’s favorite?  And he just happens to be the Head Trader of your fund.  It’s like an engineer telling the captain of the ship that the engines can’t go any faster – “Captain (in good Scottish brogue), I’m givin’ it all she’s got.”  To which the Captain replies “No, Scotty, look at this other gauge – it says we’re only at half capacity.”  Which gauge is right – the captain’s or the engineer’s?  Fortunately, there is a way to tell.  Unfortunately, captains don’t like to accept it when they’re wrong.

Read more of this post