how to calculate volatility in python
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volatility is a key measure of risk and uncertainty in financial markets. it quantifies the degree of variation of a trading price series over time. in this tutorial, we will explore how to calculate volatility using python. we'll cover both historical volatility (based on past price data) and implied volatility (derived from option prices).
historical volatility is computed from historical price data. one common method to calculate historical volatility is by using the standard deviation of the logarithmic returns of a stock over a specified period. here's how you can do it in python:
implied volatility is derived from option prices and reflects the market's expectation of future volatility. one common method to calculate implied volatility is using option pricing models like the black-scholes model or numerical methods like the newton-raphson method. here, we'll demonstrate using the py_vollib library, which provides tools for option pricing and implied volatility calculation.
adjust the parameters (s, k, r, t, option_price) according to your specific option contract.
in this tutorial, we've covered how to calculate both historical and implied volatility in python. these calculations are essential for risk management and pricing of financial instruments in quantitative finance. make sure to explore further and adapt these methods to suit your specific requirements and data sources. happy coding!
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