undefinedOne of the most beautiful characteristics of Options Trading is the multiple dimensions in which the trade data can be analysed.

Implied Volatility (IV) is one such example of data derived from Options Trading. While in itself the IV is tradable, there is a tremendous analytical value to it as well. Let us first understand: What is IV?

Options Premium (Call/Put) is made up of five variables viz. Underlying Price, Strike Price, Time to Expiry, Interest Rate and Volatility.

Information about four of the five factors are available publically so there would be no debate on its input value. Last but not least is the ‘volatility’ figure which could be debated over.

Now the volatility that we are talking about is a forward-looking figure because we are valuing an option to exercise or let go of a transaction on a future date.

So, while the volatility figure has its roots into historical behaviour, the expectation of future behaviour of the underlying would have equal bearing on the volatility figure.

So instead of us trying to come at a correct number why not derive it from the current option premium . Investors should plug in the current price, strike price, time to expiry and interest rate figures, and compare it with the resultant Option Premium to get the volatility figure implied by the Option Premium and arrive at IV.

As far as the analytical value of this number is concerned, one thing about IV is that it has a mean-reverting characteristic. Meaning, it would always be in a range. The range might keep moving up and down but it still would remain in a range.

While this is an observation and not a fact. It is statistically proven observation by running a test called Co-Integration. I would encourage the readers to Google this concept and try it on the readily available proxy for IV of Nifty Options i.e. India VIX.

Now let us understand how IV correlates with the underlying. The relationship between the underlying and the IV is negative or inverse. Why?

Well, it is because of the simple of the law of nature. Building something takes time thereby lower expected volatility associated with rising underlying (we are referring to equity and Indices).

While, breaking something can be much faster, hence higher expected volatility associated with falling underlying.

Now that we know enough about IV let us focus on how to monetise this knowledge, which brings us to two questions:1. How do we find extremes?

2. How do we trade them?

To find the extreme just plot implied volatility (can be found using much free software on the web) of nearest strike Call/Put of any underlying for at least 20 preceding days (an approximation for an expiry).

Find its average and add two standard deviations to the average to find the upper extreme and reduce two standard deviations from the average to find lower extreme. (Average and Standard Deviations are ready functions in MS Excel).

There could be more sophisticated and accurate ways to come at a statistical extreme but I am trying to keep it simple. For tryouts, one can even use historical India VIX as a proxy for Nifty Options IV.

Answer to the second question, once we know that the IV is at the extreme, it gives you perfect ground to take a contra trade in the Underlying.

Meaning with IV at upper extreme typically we would have falling underlying but we would be nearing a bottom. Similarly, a top would more than not coincide with the lower extreme of the IV.

Should you be using this indicator in isolation? Preferably not. But, in my experience IV has one of the highest success rates in catching tops and bottoms.

Hence, keep track of IV to figure out when to say enough is enough and sense being close to a reversal.

Learn and read more about hedging strategy from Quantsapp classroom which has been curated for understanding of Long Straddle from scratch, to enable option traders grasp the concepts practically and apply them in a data-driven trading approach.

SHUBHAM AGARWAL is a CEO & Head of Research at Quantsapp Pvt. Ltd. He has been into many major kinds of market research and has been a programmer himself in Tens of programming languages. Earlier to the current position, Shubham has served for Motilal Oswal as Head of Quantitative, Technical & Derivatives Research and as a Technical Analyst at JM Financial.

source - moneycontrol.com

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