Calculating Parkinson’s Volatility in Python

How to Code Parkinson’s Volatility For Time Series Analysis

Parkinson’s volatility is a measure of historical volatility that utilizes the high and low prices of a financial instrument over a given period. It is considered to be more efficient than the standard close-to-close volatility estimator because it incorporates the intraday price range, capturing more information about price movements within the day.

This article presents this volatility measure in detail and shows how to code a rolling calculation on time series using Python.

Understanding Parkinson’s Volatility

Before discussing complex volatility models, it is always recommended to have a thorough understanding of the most basic volatility model (or calculation), that is the historical standard deviation. The standard deviation using the historical method is a common way to measure the volatility of a financial instrument based on past price data.

It quantifies the amount of variation or dispersion of a set of values. In finance, it typically measures the dispersion of daily returns around their mean. Follow these steps to calculate the standard deviation:

• Calculate the returns using either the differencing (first…