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.”

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.