Options rules basic concepts

In this way, delta and gamma of an option changes with the change in the stock price. We should note that Gamma is the highest for a stock call option when the delta of an option is at the money. Since a slight change in the underlying stock leads to a dramatic increase in the delta. Similarly, the gamma is low for options which are either out of the money or in the money as the delta of stock changes marginally with changes in the stock option.

You can watch this video to understand it in more detail. Theta measures the exposure of the options price to the passage of time.

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It options rules basic concepts the rate at which options price, especially in terms of the time value, changes or decreases as the time to expiry is approached. Vega measures the exposure of the option price to changes in the volatility of the underlying.

Generally, options are more expensive for higher volatility. So, if the volatility goes up, the price of the option might go up to and vice-versa. Vega increases or decreases with respect to the time to expiry? What do you think? You can confirm your answer by watching this video. One of the popular options pricing model is Black Scholes, which helps us to understand the options greeks of an option.

Although some option contracts are over the counter, meaning they are between two parties without going through an exchange, standardized contracts known as listed options trade on exchanges. Option contracts give the owner rights and the seller obligations. Here are the key definitions and details: Call option: A call option gives the owner seller the right obligation to buy sell a specific number of shares of the underlying stock at a specific price by a predetermined date.

Black-Scholes options pricing model The formula for the Black-Scholes options pricing model is given as: where, C is the price of the call option P represents the price of a put option. N x is the standard normal cumulative distribution function.

The formulas for d1 and d2 are given as: To calculate the Greeks in options we use the Black-Scholes options pricing model.

Delta and Gamma are calculated as: In the example below, we have used the determinants of the BS model to compute the Greeks in options. At an underlying price of If we were to increase the price of the underlying by Rs. As can be observed, the Delta of the call option in the first table was 0.

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Hence, given the definition of the delta, we can expect the price of the call option to increase approximately by this value when the price of the underlying increases by Rs. The new price of the call option is If you observe the value of Gamma in both the tables, it is the same for both call and put options rules basic concepts contracts since it has options rules basic concepts same formula for both options types. If you are going long on the options, then you would prefer having a higher gamma and if you are short, then you would be looking for a low gamma.

Thus, if an options trader is having a net-long options position then he will aim to maximize the gamma, whereas in case of a net-short position he will try to minimize the gamma value. The third Greek, Theta has different formulas for both call and put options. These are given below: In the first table on options rules basic concepts LHS, there are 30 days remaining for the options contract to expire. We have a negative theta value of He has to be sure about his analysis in order to profit from trade as time decay will affect this position.

This impact of time decay is evident in the table on the RHS where the time left to expiry is now 21 days with other factors remaining the same. As a result, the value of the call option has fallen from If an options trader wants to profit from the time decay property, long- term trading on binary options can sell options instead of going long which will result in a positive theta.

We have just discussed how some of the individual Greeks in options impact option pricing. However, it is very essential to understand the combined behaviour of Greeks in an options position to truly profit from your options position.

Let us now look at a Python package which is used to implement the Black Scholes Model. Python Library - Mibian What is Mibian? Mibian is an options pricing Python library implementing the Black-Scholes along with a couple other models for European options on currencies and stocks.

In the context of this article, we are going to look at the Black-Scholes part of this library. Mibian is compatible with python 2.

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This library requires scipy to work properly. How to use Mibian for BS Model?

The function which builds the Black-Scholes model in this library is the BS function. This list has to be specified each time the function is being called. Next, we input the volatility, if we are interested in computing the price of options and the option greeks. The BS function will only contain two arguments. If we are interested in computing the implied volatilitywe will not input the volatility but instead will input either the call price or the put price.

In case we are interested in computing the put-call parity, we will enter both the put price and call price after the list. BS [1.

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We will learn more about this as we move to the next pricing model. Derman Kani Model The Derman Kani model was developed to overcome the long-standing issue with the Black Scholes modelwhich is the volatility smile. One of the underlying assumptions of Black Scholes model is that the underlying follows a random walk with constant volatility. However, on calculating the implied volatility for different strikes, it is seen that the volatility curve is not a constant straight line as we would expect, but instead has the shape of a smile.

This curve of implied volatility against the strike price is known as the volatility smile. If the Black Scholes model is correct, it would mean that the underlying follows a lognormal distribution and the implied volatility curve would have been flat, but a volatility smile indicates that traders are implicitly attributing a unique non-lognormal distribution to the underlying.

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