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Monetise options beyond options trading

Any significant change in Premium without any significant change in underlying price and time to expiry can now be attributed to change in Implied Volatility.

SHUBHAM AGARWAL | 15-Feb-20
Reading Time: 3 minutes

Options are agile instruments with their non-linear pay-offs and favourable risk profiles (at least for buyers). While it is important to trade them using single Options (Buy or Sell) or various combination of them, it is also important to realise a unique utility of the Options: that of analysing trade data.

Options being financial derivatives are traded with a perspective on the underlying asset. While the available instruments (Option types, Expiries, Strike Prices) remain more or less standard. The level of activity and the intensity of trading (Price) keeps changing.

The variations into these trade data points give away a lot in terms of overall market consensus. This data analytics opens a whole new window of trade improvisation.

Trade data analytics being a vast subject in itself we may not be able to discuss all of it but let us pick two of the most important ones along with their utilities.

Implied Volatility

This fancy element in plain English is the expensiveness of the option. It results in the inflation in premium just because the expected volatility is high just like ahead of an event and deflation just because an event has passed by and calmer times are expected ahead.

As the four factors used in the calculation of option premium viz. Underlying Price, Strike Price, Time to Expiry & Risk-Free Rate of Interest do not have any different answer, given these four known factors and the premium traded in the market the volatility figure back-calculated is called Implied Volatility.

Any significant change in premium without any significant change in underlying price and time to expiry can now be attributed to change in Implied Volatility. Higher premiums are attributed to higher implied volatility and lower premiums are attributed to lower implied volatility.

Calculating Implied Volatility is difficult but most of the markets now have Index of Implied Volatility of Index Options like we have India VIX for Nifty, which can be used as a proxy. Like OIPCR, Implied Volatility is also mean reverting in nature but is inversely correlated to the underlying.

The utility of Implied Volatility Analytics: More often than not the Underlying tops and Implied Volatility Bottom coincide. So similar to OIPCR if we see Implied Volatility at recent high while the index has been falling to fresh lows, there could be a breather in place and so is true for another way around also, when Implied Volatility I at the recent low.

Option Interest Distribution

This one is the easiest. Carrying forward few thoughts, as we learnt the Open Interest figure is readily available. Look for the heaviest Call Open Interest in the entire set of strikes. One would find Heaviest Call strike higher than current market price and heaviest Put strike lower than the current market price.

Two utilities of Open Interest Analytics

1. Look at those strikes and one would get an idea what is the ballpark range expected by the market consensus for the underlying to trade into in the expiry those options belong to.

2. In case the stock moves above any one of these strikes, there would be a break in consensus and could trigger a serious move, so if a Call strike is surpassed there could be a sharp up move and another way around for Put.

There we have it, these are just tips of big icebergs mentioned above. There is a lot more to it. I would sincerely recommend reading into these along with your existing trade finding techniques and benefit from it.

The author CEO and Head of Research at Quantsapp Private

Disclaimer: The views and investment tips expressed by investment experts on Moneycontrol.com are their own and not that of the website or its management. Moneycontrol.com advises users to check with certified experts before taking any investment decisions.

Learn and read more about short starngle 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.

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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.

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