Put Option Selling: Strategies and Techniques for Profitable Trading

  1. Trading for professionals: Options trading
    1. Call Option Basics learn the basic Definition with Examples
    2. Call option and put option understanding types of options
    3. What Is Call Option and How to Use It With Example
    4. Options Terminology The Master List of Options Trading Terminology
    5. Options Terms Key Options Trading Definitions
    6. Buy call option A Beginner’s Guide to Call Buying
    7. How to Calculate Profit on Call Option
    8. Selling Call Option What is Writing/Sell Call Options in Share Market?
    9. Call Option Payoff Exploring the Seller’s Perspective
    10. American vs European Options What is the Difference?
    11. Put Option A Guide for Traders
    12. put option example: Analysis of Bank Nifty and the Bearish Outlook
    13. Put option profit formula: P&L Analysis and Break-Even Point
    14. Put Option Selling strategies and Techniques for Profitable Trading
    15. Call and put option Summary Guide
    16. Option premium Understanding Fluctuations and Profit Potential in Options Trading
    17. Option Contract moneyness What It Is and How It Works
    18. option moneyness Understanding itm and otm
    19. option delta in option trading strategies
    20. delta in call and put Option Trading Strategies
    21. Option Greeks Delta vs spot price
    22. Delta Acceleration in option trading strategies
    23. Secrets of Option Greeks Delta in option trading strategies
    24. Delta as a Probability Tool: Assessing Option Profitability
    25. Gamma in option trading What Is Gamma in Investing and How Is It Used
    26. Derivatives: Exploring Delta and Gamma in Options Trading
    27. Option Gamma in options Greek
    28. Managing Risk in Options Trading: Exploring Delta, Gamma, and Position Sizing
    29. Understanding Gamma in Options Trading: Reactivity to Underlying Shifts and Strike Prices
    30. Mastering Option Greeks
    31. Time decay in options: Observing the Effect of Theta
    32. Put Option Selling: Strategies and Techniques for Profitable Trading
    33. How To Calculate Volatility on Excel
    34. Normal distribution in share market
    35. Volatility for practical trading applications
    36. Types of Volatility
    37. Vega in Option Greeks: The 4th Factors to Measure Risk
    38. Options Trading Greek Interactions
    39. Mastering Options Trading with the Greek Calculator
    40. Call and Put Option Guide
    41. Option Trading Strategies with example
    42. Physical Settlement in Option Trading
    43. Mark to Market (MTM) and Profit/Loss Calculation
Marketopedia / Trading for professionals: Options trading / Put Option Selling: Strategies and Techniques for Profitable Trading


Vega measures the rate at which option premiums change with respect to volatility. Many traders express the concept of volatility as being synonymous with the “up-down movement of the stock market“. Consequently, it is time we gain a better understanding of this variable.

– Moneyball

Have you seen the Hollywood movie ‘Moneyball’? It is a depiction of a real- life story centred around the manager of an American baseball team, Billy Beane, and his colleague. To acquire the most skilled players for their team, they implemented a revolutionary strategy that was unheard of in their day – using statistics to pick lower profile but highly talented baseball players. This approach was both innovative and disruptive at the time.

To give an example, I can draw some inspiration from the Moneyball approach to illustrate volatility.

This topic may appear to be unrelated to stock markets, but try not to be disheartened. Rest assured that it is pertinent, and will assist you in understanding the concept of ‘Volatility’ better.

Let’s consider two batsmen and the runs they have scored in six consecutive matches.

As the captain, you face a decision between Billy and Mike for the upcoming game. It is crucial to select a batsman who can consistently score at least 20 runs. In such situations, there are typically two methods used:

  1. Calculate the combined score (referred to as ‘Sigma’) of both batsmen and choose the one with the highest score for the game.
  2. Determine the mean (also known as the ‘average’) of the number of scores per game and select the batsman with a superior average.


Let’s perform the calculations using the following numbers:

Billy’s Sigma = 25 + 28 + 22 + 24 + 21 + 26 = 146

Mike’s Sigma = 38 + 15 + 20 + 14 + 30 + 21 = 138

Based on Sigma, Mike appears to be the more likely choice. Now, let’s calculate the mean or average for both players:

Billy = 146 / 6 = 24.33

Mike = 138 / 6 = 23

From both the mean and Sigma perspectives, it seems that Mike has the advantage. However, it is important not to jump to conclusions just yet. Remember, the goal is to select a batsman capable of consistently scoring at least 20 runs. This cannot be determined solely based on mean and Sigma data. Let’s continue our analysis.

Next, we will find the deviation from the mean for each match. Taking Billy as an example, his average is 24.33, and in the first match, he scored 25 runs, resulting in a deviation of +0.67 (0.67 runs above his average). In the second match, there was a deviation of -3.33 (-3.33 runs below his average) with a score of 28.

The average score of Billy is represented by the middle black line, and the double-arrowed vertical line indicates the deviation from the mean in each match. Moving forward, we will compute another statistical measure called ‘Variance’.

Calculating variance may seem daunting, but it’s not that complex. We simply sum up the squares of the deviations from the average result and divide by the number of matches played, which is 6 in this case.

For Billy, the variance can be calculated as follows:

Variance = [(0.67)^2 + (-3.33)^2 + (-1.33)^2 + (+0.67)^2 + (-3.33)^2 + (+1.67)^2] / 6

= 35.33 / 6

= 5.89

We will also calculate another factor called ‘Standard Deviation’ (SD), which is obtained by taking the square root of the variance:

Standard Deviation = √Variance


For Billy, the standard deviation is:

Standard Deviation = √5.89

≈ 2.43

Similarly, the standard deviation for Mike is calculated to be 3.46. Now, let’s gather and compare all the numbers and statistics: 

While we are familiar with the terms ‘mean’ and ‘Sigma’, what is SD? This statistic quantifies the extent to which values deviate from the average. Standard deviation (SD), often represented by the Greek letter sigma (σ), is a measure in statistics that indicates the spread of a set of data values.

It is important not to confuse the two sigmas. The total is denoted by the Greek symbol ∑, while standard deviation is sometimes represented by σ.

To predict the likely number of runs Billy and Mike will score in their next game, we can utilize the standard deviation (SD). By adding and subtracting the SD from their mean, we can obtain a range of predictions.

The figures indicate that in the upcoming 7th match, Billy is likely to score between 21.9 and 26.76 runs, while Mike’s potential range is wider, spanning from 19.54 to 26.46 runs. With Mike’s scoring range being more diverse, it becomes challenging to determine if he will reach or exceed 20 runs. He could end up scoring anywhere between 19 and 27 runs.

Considering Billy’s consistent performance and narrower range, he is a safer choice for the 7th match. His scores are likely to fall between 21 and 27, while Mike’s performance can be more unpredictable. Choosing Mike may involve a higher level of risk.

In conclusion, when considering who has a better chance of scoring 20 or more runs, Billy is undoubtedly the clear choice. He is reliable and less prone to taking unnecessary risks, unlike Mike.

We have assessed the risk associated with these players through “Standard Deviation.” In the realm of the stock market, the riskiness of stocks or indices is referred to as volatility, which is represented as a percentage calculated using standard deviation.

Here’s Investopedia’s definition: 

 “A statistical measure of the dispersion of returns for a given security or market index. Volatility can either be measured by using the standard deviation or variance between returns from that same security or market index. Commonly higher the standard deviation, higher is the risk”.

According to the given definition, if the volatility of Infosys is 25% and TCS is 45%, it is evident that Infosys demonstrates more stable price fluctuations in comparison to TCS.

Before concluding this chapter, let’s make some predictions based on the following information:

Today’s Date: 1st June 2023

Nifty Spot: 12,500

Nifty Volatility: 18%

TCS Spot: 3,000

TCS Volatility: 30%

Given this information, can we predict the likely trading range for Nifty and TCS one year from now? Let’s put these numbers to good use.

According to calculations, in the next 1 year, Nifty is expected to trade within the range of 10,250 and 14,750, with varying probabilities for values in between. For example, the probability of Nifty being around 11,000 could be 25%, while around 13,000 it could be 40% by 1st June 2024.

This leads us to an exciting platform.

  1. We have estimated the range for Nifty over 1 year. 
  2. Now, can we estimate the range within which Nifty is likely to trade over the next few days or until the series expiry? 
  3. If we can do this, we will be in a better position to identify options that are likely to expire worthless, allowing us to sell them today and pocket the premiums.
  4. While we have determined the range for Nifty based on a volatility estimate of 16.5%, we should consider what happens if the volatility changes.

In the upcoming chapters, we will answer these questions and explore easier methods, such as using MS Excel, to calculate volatility.