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Reminiscences of an Option Trader: In the Beginning



Editor's Note: Prof. Succo's introduction to the ROT column.

Volatility is movement; we can measure past movement for average moves and maximum moves, but at best it only gives us a general idea of potential volatility.

We measure volatility linearly, a crude measure called "standard deviation" which gives us a normalized average movement of an asset over a certain period of time. When we say a stock has a 20% volatility, we are saying the stock "should" move about 20% up or down over a year's time. We can then infer that the stock will then move on average 1.35% per day (20% divided by the square root of 220, which is approximately the number of trading days in a year). People normally only look at the 66% of "observations closest to the mean price of the asset when calculating this measure."

Notice this measure does not describe anything about the potential for large moves. Assets of different types have very different profiles for large moves if something unexpected happens: stocks can move much more on a percentage basis than bonds if something unknown becomes known. This is called "kurtosis" or "tail risk" and is calculated by looking at those really outlier moves. But again, these observations are only "what has happened in the past" and describe nothing of what could happen in the future. This is the risk most people ignore and is what I refer to when I say "people are taking too much risk in buying stocks."

Only the first measure is used directly by most practitioners in valuing derivatives like options, although the CDO market does incorporate Monte Carlo (and we do as well in our largest positions), which is a simulation model, to adjust prices for "tail risk."

Can we predict volatility? Many have tried to develop models in an attempt to, just as they have developed them in an attempt to predict the direction of asset prices, but I have to say that I don't think it is possible. I think there are just too many inter-dependent variables at work to develop a linear model to do so. I think there is some promise in non-linear models to help understand potential volatility better, but nothing to build a business on. So why do I trade it?

Only because the vehicles whose value is a function of the level of volatility, these derivatives, are most often mispriced relative to each other. It is always difficult to say that an option is mispriced on an absolute level, for who knows; they are often at various times, however, mispriced on a relative level. For example, when markets get volatile, the demand for out of the money puts goes up while the supply of out of the money calls also goes up (for a time at least). This would cause us under the right circumstances to enter into the market and sell that put, buy the call, and "delta" hedge it with stock.

We create portfolios of derivatives where our long volatility has some level of correlation to our short volatility (we often go through a legging process by buying volatility first, but I will save that for another day); our risk is that correlation. If our short volatility trades exhibit a higher volatility than our long ones, we lose money.

So we look for trades where there is an adequate discrepancy between prices of derivatives and the correlation risk is quantifiable. Of course, there is no correlation risk at all when buying and selling options on the same security (although there is tail risk, which I will discuss). Because of this, they don't occur very often; they occur almost exclusively either because there is great distress in the stock (this could be from market distress) that creates a large imbalance between the supply and demand of various options of different strikes and/or expirations or because there exists a special situation such as an event that creates a non-normal distribution of prices, i.e. the stock trading up or down dramatically instantly.

Next week I will describe a trade like the last mentioned: the Guidant (GDT)/Johnson & Johnson (JNJ)/Boston Scientific (BSX) saga. Because of the binomial distribution of stock prices created by a fluid merger situation, option prices in these three names became skewed to the point where we entered the market.

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Positions in JNJ, GDT

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