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Option seller looking for a safe strike in extreme conditions

An options seller commits to buying or selling an underlying asset (for example, 100 Apple shares - AAPL) if the price reaches a specified value within a predetermined time. This commitment is formalized through an agreement, which provides for an immediate receipt called a "premium" (or the option's price). The option loses its value once the defined time period has elapsed, allowing the seller to realize a profit corresponding to the "premium" received minus the commissions. If, however, the strike price is exceeded, the seller will be obligated to buy or sell those shares at a value higher or lower than the current price, generating a loss.
Context and Challenge
Prepare the Analysis
How to Use MetricAlgo
Results and Comparison
Conclusions
Video

Context and Challenge

Like an auto insurer, the main challenge of an options seller is to identify the best strike price, based on the probability that the price will not reach a certain deviation within a defined time. Accurately assessing this probability is crucial in determining whether the risk taken is offset by the premium received. In fact, these probabilities influence the risk that the seller is willing to take. Therefore, it is crucial to know historical behaviors to compare the data obtained with the current market context and, consequently, to evaluate the most appropriate strike price.

In this case study, the aim is to sell a PUT or a CALL with a daily expiration, maintaining a historical probability of success between 95% and 100%. This means that, historically, the deviation from our strike price has never occurred.

It goes without saying that under “normal” conditions, options of this kind are not frequently quoted by brokers and, therefore, are not always viable. However, in the presence of high volatility or sudden price movements within the same day, situations arise where the price deviates significantly from the opening price. In these contexts, even options with an initial probability of 100% begin to be quoted by brokers.

The right balance between premium, historical deviation, and historical probability of price reversion offers advantages to astute traders, who could exploit such moments if adequately informed.

Prepare the Analysis

Starting from the direction in which the price has moved rapidly, in this case in a bullish manner, I decide to sell a CALL, should favorable conditions exist. Normally, the evaluation phase of this specific situation begins when the price has moved between 68% and 95% of the historical highs.

The speed at which the price reaches areas far from the opening price demonstrates the exceptionality of the event and causes the price of options to increase. The peak of values occurs at the current price of the underlying asset, in this case, APPLE, and since this represents an extreme historical point, the historical probability that the price will revert increases (Note: we are talking about historical probability, which does not mean that we can predict the future; every analysis must be contextualized to the current market momentum, which the trader must always consider). At this moment, we come into play with analysis and attempt to determine the likelihood that the trend will continue, compared to the probability of a reversion.

The goal in this case is to sell a CALL, thus assuming a “non-bullish” stance, which means I start to profit if the price drops and, over time, even if the price rises slightly (for this reason, it is termed “non-bullish” and not “short”). Since each filter produces a dataset of variable size and more or less similar to the current market conditions, several research initiatives are launched with the series filter, all aimed at obtaining information that helps to understand past behavior. Since each modified filter changes the probabilities and the number of similar historical cases, it is crucial to conduct various research and extrapolate a definitive “personal” figure, possibly by combining multiple analyses.

For each analysis, we will take a look and note these values in addition to the number of records extracted from each search (note that the searches are based only on the highs):

  1. Peak of the period. (For example, for a CALL, we are interested in this)
  2. Moving averages uptrend
  3. Moving averages of closing prices
  4. Number of times closed down
  5. Number of times closed up
  6. Prob. Historical 68,X
  7. Prob. Historical 80%
  8. Prob. Historical 90 %
  9. Prob. Historical 95%
  10. Prob. Historical 99,7%

How to Use MetricAlgo

The MetricAlgo tool to be used in this case is the limit analysis found within the Historical Stats tool package.

Select the APPLE stock from the Dashboard and enter the tool where we will start to set the filters to perform the analysis. The first thing to set is the timeframe, which in this case will be set to “daily” and we recommend selecting only the last 20 years of the series.
The various combinations of filters and the corresponding searches that we will carry out could be:

  • Last 20 years
  • Last 20 years in the same month
  • Last 20 years on the same day of the week
  • Last 20 years with the same type of GAP (Pos/Neg)
  • Last 20 years with the same type of GAP between two openings
  • Last 20 years in the same month and day of the week
  • Last 20 years in the same month with the same type of GAP
  • Last 20 years in the same month with the same type of GAP between two openings
  • Last 20 years on the same day with the same type of GAP
  • Last 20 years on the same day with the same type of GAP between two openings
  • Last 20 years with the same type of GAP (opening and previous close)
  • Last 20 years with the same month, same day, and the same type of GAP
  • Last 20 years with the same month, same day, and same type of GAP between two openings
  • Last 20 years with the same month, same day, and both the same type of GAP

to which another set of identical analyses should be added, but with the addition of a volatility filter, for example in a range of +/-5% from the current volatility value. In this way, you can compare historical results with others more specific to the important context of “the same volatility.

In this way, from the previous 14 analyses, we would obtain 28, with two sets differing by volatility. Here’s an example in the image:

It goes without saying that an analysis of the last 15 or 10 years will each yield another set of 28 results. Furthermore, it’s important to understand that choosing volatility as the final element of comparison is an arbitrary decision by the trader or historical data analyst, who can decide the order they prefer in their filtered investigation of the series.

Not forgetting to take a look at the overlay chart, this chart has a strong visual impact and can make you realize at a glance the exceptionality or normality of the daily trading session.

Results and Comparison

Once all the analyses have been completed, we can compare the data and immediately notice that adding the volatility filter is essential in a context of this type, as it needs to be monitored. If the volatility is high, it will be an exceptional day because of it. However, if the volatility is low, it will be an extremely exceptional day, as the price is moving despite the volatility.

Regardless, what we are interested in observing is whether the probabilities beyond 95% are quoted enough by the broker to take advantage of and make a sale in a field of historical probability of even 100%.

To be smart enough to find interesting prizes in favorable probability fields and decide based on a careful analysis of the reason why the extreme event was created.

Conclusions

Once the data has been analyzed, the most important thing is to be aware that the risk in this case is that the exceptionality is “that case that has never happened” and continues the race towards that goal.

It is possible.

This is where the risk of this situation begins, to guess if it will return to normality or register a rare extreme case. It is a balance between the low probability that it will continue in its direction and the increasingly high probability that it will return, which is why we define someone who operates in this context as “clever”

Context and Challenge
Prepare the Analysis
How to Use MetricAlgo
Results and Comparison
Conclusions

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