Volatility | Options trading strategy in Python: Basic | Quantra Course

Part of the course on Options trading strategy in Python: Basic. Link: https://quantra.quantinsti.com/course/options-trading-strategies-python-basic

In this video lecture we will understand what is meant by volatility in the context of finance and trading. We will also look at different types of volatility.

In finance, volatility is the degree of variation in a price time series, such as the closing prices of a particular stock. This is measured by the standard deviation of the time series, a statistical measure of dispersion that we have looked at in the previous reading material.
Volatility refers to the amount of uncertainty or risk about the amount of changes in a security's value. Volatility is not concerned with the direction of change, but with the amount of change. In the context of options, volatility is a variable in the option pricing formula indicating the extent to which the return of the underlying asset may fluctuate between now and the expiration date of the option.
Now that we have understood the essence of volatility, let us look at its different types. Historical Volatility gauges the fluctuations of underlying securities by measuring the price changes over a predetermined period of time in the past. The calculation can also be done on an intra-day basis, but this process is generally done over the closing prices of securities. Based on the intended duration of the options trade, one can calculate the historical volatility.
Another type of volatility that is extensively used in options trading is Implied Volatility. By understanding imbalances in the supply and demand, implied volatility represents the expected fluctuations of the underlying security till the expiration of the option contract. Implied volatility is a result of people buying and selling in the market, and is a forward-looking imperfect metric, whereas historical volatility looks at a particular time frame in past and measures the movement in that duration.
Realised volatility is also sometimes referred to as the historical volatility. Unlike implied volatility which tells us the expected variance in a future time period, realised volatility tells us the actual or historical volatility for that future period of time.
Let us understand these three types of volatilities through a game of football. Assume that you and your friends are about to watch the El Clasico, the fierce game between FC Barcelona and Real Madrid. Based on the historical goals of the two clubs in previous games, one would say that this game would result in a 2-2 draw.
But looking at the current form of players, everyone predicts the game to be won by Barcelona with a scorelines of 3 goals to 1. After the game is over, it is seen that Barcelona wins the game by 4 goals to 2. These three scoreline can be categorised as historical, implied and realized.
For calculating the historical volatility, the first step is to compute the log returns for the time series. Once this is done, the next step is to compute the standard deviation of the log returns.
You will be taken through a Python code for computing the historical volatility of a security in an
upcoming unit.
The price of the option changes with volatility. To understand this better, let us look back at our insurance analogy for the premiums of options. The life insurance premium of a skydiver will be considerably higher than the life insurance premium of a librarian, because the skydiver takes more risks on a daily basis. Similarly, the option premium for those securities which are more risky or have higher volatility will be higher than those with lower volatility.

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