What Is Our Edge In Options Trading? - Option Posts

# What Is Our Edge In Options Trading?

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Every successful business has some advantage over its competitors, and the options trading business is no exception. In this post, we’ll explain our edge in options trading; understanding our strategy is one of the most important steps you can take towards becoming a successful options trader.

Since successful options traders operate a lot like insurance companies, we’ll start by taking an in-depth look at how insurance companies make their money.

## How Insurance Companies Make Money

From the perspective of insurance companies, insurance is a pure probability game. Using actuaries and complex data analysis, insurance companies calculate the probability that they will have to pay out a claim on an insurance policy and price their plans accordingly.

For example, car insurance companies know from historical data that certain variables—age, gender, number of miles driven per year, type of car, etc.—affect the likelihood that an individual will get in an accident. For each customer, they collect this data and estimate how likely they are to get in an accident. Then, they use the number they come up with to determine the premium they will need to charge for the insurance policy to be profitable.

## Example: Car Insurance Policy Pricing

For all insured motorists, there are only two possible scenarios: they either get into an accident, or they do not. Let’s say each person has a 5% probability of getting into a car accident, and if this occurs, the insurance company will have to pay \$50,000 to cover the damages. Multiplying the probability of this happening (5%) times the cost if it does (\$50,000) gives us the expected value of this scenario, which is -\$2,500. This number is negative because the insurance company is paying out, or losing, that money.

If there is 5% probability of getting into a car accident, then there is a 95% probability of not getting into a car accident. If there is no car accident, the insurance company does not have to pay out anything, so the expected value of this scenario is \$0. Adding the expected values of the two scenarios together, we arrive at an expected value of -\$2,500 per policy.

Insurance companies insure many people at one time; in this scenario, if they were to charge \$2500 for each policy, they could use the money from the 95% of people who pay, but don’t get in an accident to cover the 5% who do get in an accident and come out even, neither losing nor gaining money.

## Probability, Profit, and Scale

Insurance companies don’t want to come out even, though; they want to make a profit. So, instead of charging \$2,500, they actually charge a little extra on top of the expected value—say, a total of \$3,000 per policy, or \$500 more than the expected value—to turn a profit on these insurance policies. In this way, they can pay out money in 5% of the cases and still make a profit. This is like betting on a coin flip with more money on one side; if you made a bet in which you would lose \$1 on heads and win \$1.30 on tails, you would lose on half of the flips, but still come out ahead on average.

Because they rely on probability, insurance companies want to scale their business up as fast as they can. Larger sample sizes are more likely to resemble the theoretical probabilities. If the insurance company concentrates their risk by only insuring five people, it’s possible (though unlikely) that all five of them could get in an accident, resulting in losses for the company. Instead, insurance companies want to insure as many people as possible to dilute their risk. If they insure over a million customers, they are far more likely to reach theoretical probabilities, and are less likely to be susceptible to a fluke string of randomly unprofitable policies.

## How does this relate to options trading?

As options traders, we need to know the probability of winning, how much we stand to make when we win, and how much we can potentially lose. If we can calculate all of these things, we can make informed trading decisions in which we come out ahead on average.

Let’s first talk about Implied Volatility (IV). IV measures how volatile a stock price is likely to be in the future. It is the projected, or forward looking, annualized standard deviation of the stock price in the form of a percentage. If the IV is 20%, this means that 68% of the time, the stock price will remain within +/-20% of its current price. This equates to the green zone in the bell curve below, where -1 SD is equal to the stock price going down 20%, while +1 SD is equal to the stock price rising 20%.

In fact, the stock market as a whole follows a similar distribution. Here’s the actual distribution of the S&P 500 index daily returns.

On some days it goes up a lot, and on others it goes down a lot. But most of the time, it falls within one standard deviation of the average. Knowing this distribution pattern allows us to predict what the market is likely to do in the future.

Option contract prices are controlled by five factors: stock price, strike price, time to expiration, volatility, and interest rates. Volatility is the only unknown factor in this model. However, because we know all the other factors and the price, we can use this information to work backwards to solve for the volatility which is implied by the option price.

Buyers and sellers of options try to predict what the range of the stock will be in the future by increasing and decreasing the price of the options. Option contracts are a form of insurance for stock holders, so the more volatile, or uncertain a stock is, the more people will want insurance on that asset. If the option prices are relatively high (IV also will be high), then the options market is predicting a wide range (SD=2), while a stock with cheap option prices (IV will also be low) is predicting a narrow range (SD=0.5).

If any of these terms used seem unfamiliar, be sure to visit our list of options trading terms defined.

## Implied Volatility vs Historical Volatility

Implied volatility is the stock range that people expect will occur in the future, while historical volatility is the volatility that actually occurred, looking backwards. For example, option buyers and sellers could be predicting a plus or minus 20% move in the next year for a stock. Fast forward one year, we can compare this prediction with what actually happened: say, 15%. In this case, the actual move, or historical volatility, was less than the implied move/predicted move.

This is exactly where our edge is in options trading: implied volatility overstates historical volatility.

As you can see in the graph below, the blue line represents implied (future) volatility, while the orange line represents historical volatility going all the way back to the year 2000 for the S&P 500 index. Implied volatility overstated historical volatility 87% of the time (theoretically only supposed to be 68%), with an average overstatement of 4%. This means that the options market on average predicted a 4% higher range than actually occurred in the market.

## What does implied volatility overstatement mean?

Implied volatility overstatement of actual volatility is our edge in trading. If on average the expected range is 4% greater than what actually occurs, then the options price is theoretically over-priced by 4%.

Just like the insurance company that charges a premium on top of the theoretical value of an insurance policy, effectively gaming the system, options prices are overvalued relative to the actual move that occurs in the stock market, giving us our edge in options trading. This is also why we choose to be options sellers, taking advantage of the over-priced nature of options.

Like insurance companies, who insure as many people as they can to diversify their risk, options traders need to trade small and often to realize our theoretical probabilities. You do not want to bet the farm on one trade, because that one trade might be the unlucky trade that would put you out of business. However, if you stay small, consistent, and persistent to raise the number of occurrences, the numbers will eventually play out and you will reach the theoretical probabilities.

## Summary

Implied volatility overstates the actual move of a stock. This gives us a proven mathematical advantage over stock traders, whose investments are only a 50/50 proposition. This is also why we choose to be options sellers to take advantage of the over-priced nature of options contracts. Options trading is just a pure probability game based on math. Insurance companies use the exact same strategy to give them a theoretical edge, leading one of the most profitable industries. Options traders can use the same concept to gain an edge in trading options.