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Educational Article

The Debit Straddle II

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This week, we continue our discussion about the debit (long) straddle; a conservative, easy-to-manage strategy for situations in which one believes the price of the underlying issue will move substantially but does not know in which direction it will go. A debit straddle is best suited to situations where implied volatility in a particular option series is low and is expected to increase, but it can also be a viable technique to speculate on the outcome of key events or important announcements. Corporate earnings reports, new drug approvals, merger or takeover speculation and annual board meetings (splits/spin-offs) are prime examples of situations in which straddles can potentially profit from uncertain information that will be released on a specific date. At the same time, volatility in itself will not guarantee a profitable straddle position. Why? Because the uncertainty associated with the above examples is known by everyone, thus the options may already be priced according to a higher stock volatility. Indeed, the most attractive straddles will be those in which the trader is confident that the stock will be more volatile than everyone else expects.

There are many useful sources of information on the Internet and one of the best ways to help find debit straddles, especially "event-driven" plays, is to follow the mainstream activity. News articles on extremes in option trading volume and volatility are listed at many of the popular financial websites and the major exchanges; CBOE, PHLX, and AMEX are excellent resources for historical and statistical option pricing. Most of the candidates for professional traders come from reports on options activity, volume, and volatility as well as proprietary software programs that provide premium disparity algorithms, and research from specialty data providers. In contrast, retail participants generally opt for mathematical scan/sort tools that compare basic volatility data (implied versus historical, etc.) and these products can be found on a number of option research sites and in various software programs. Probability calculators and technical analysis/charting programs are also helpful in sorting through the large number of possible candidates to identify potentially undervalued options and help make assumptions about future movements in the underlying security.

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Before you can use these tools effectively, it necessary to understand all the components that affect option pricing. We have discussed intrinsic and time value at length, yet relatively little has been said about the complex subject of volatility. Unfortunately, that is one topic we cannot overlook because traders without knowledge of the way volatility (and probability) affect option pricing and potential risk-reward have little chance of surviving in the derivatives market.

Volatility & Probability

Volatility is one of the most important factors in option trading because it measures the amount by which the underlying asset fluctuates in a given period of time. In order to estimate the future volatility of an issue, or the probability of the issue reaching a certain price, you must understand how a stock's movement is quantified from a statistical standpoint. Historical volatility is calculated using the standard deviation (SD) of an underlying asset's price changes (from close of trading each day) for a certain time period. By definition, standard deviation of a collection of numbers is the square root of the difference between the mean of the squares of the numbers and the square of the mean of the numbers. In simpler terms, the standard deviation is basically the "mean of the mean," and it can often help you find the story behind the historical data. To better
appreciate this unique concept, you must learn more about what statisticians call "normal" distribution. A normal distribution of data means that most of the examples in a set of data are close to the "average," while relatively few examples tend to one extreme or the other. If you depicted normally distributed data on a graph, it would look something like this (the infamous bell-curve):

Normal Distribution Chart

The x-axis (the horizontal one) is the value in question, and the y-axis (the vertical one) is the number of data points for each value on the x-axis. Not all sets of data will have graphs that look this perfect. Some will have relatively flat curves, while others will be steeper. Sometimes the mean will lean a bit to one side or the other. However, all normally distributed data will look something like this same bell-curve shape. The standard deviation is a statistic that tells you how tightly all the various examples are clustered around the mean in a set of data. When the examples are fairly tightly bunched together and the bell-shaped curve is steep, the standard deviation is small. When the examples are spread apart and the curve is relatively flat, that suggests a relatively large standard deviation in the data. Computing the value of a standard deviation is complicated but here is what a standard deviation represents graphically:

Standard Deviations Chart

One standard deviation away from the mean in either direction on the horizontal axis (the red area on the above graph) accounts for somewhere around 68% of the possible outcomes in this group. Two standard deviations away from the mean (the red and green areas) account for roughly 95% of the outcomes. Three standard deviations (the red, green, and blue areas) account for about 99% of all the possible outcomes. If this curve were flatter and more spread out, the standard deviation would have to be larger in order to account for the number of possible outcomes. That is why the standard deviation can tell you how dispersed the results in a set are from the mean.

As mentioned earlier, historical volatility calculations generally use past closing prices to determine a stock's annualized standard deviation. Thus, a historical volatility of 50 means that the stock has a 68% probability (one sigma) of trading within 50% of its average targeted move within one year. In contrast, an option's implied volatility is not based on prior movement but rather an estimate or assumption produced by an option pricing model. This calculation starts with the current option price and works backward to determine the theoretical value of volatility that is equal to the market price minus intrinsic value. Although it is directly affected by factors such as relation to strike price, time to expiration, the risk-free interest rate, and the dividend rate, implied volatility has more to do with an option's current market value than the historical volatility of the underlying asset.
 

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