This article aims to provide a comprehensive guide on developing volatility forecasting models using Python. We will leverage the yfinance library to retrieve historical volatility data and implement a GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model to estimate and forecast volatility.
Volatility is an important aspect of financial markets because it measures how much the price of a financial instrument changes over time. Accurate volatility forecasts can help traders and investors make informed decisions and manage risk effectively.
We will discuss the following topics:
Introduction to Volatility Forecasting
Retrieve Historical Volatility Data
Exploratory Data Analysis (EDA) for yfinance Volatility Data
Implement GARCH Models
Estimate and Forecast Volatility
Evaluate Model Performance
Before we dive into implementation, make sure you have the following Python libraries installed:
yfinance
numpy
pandas
matplotlib
statsmodels
You can install these libraries with pip:
pip install yfinance numpy pandas matplotlib statsmodels
1. Introduction to Volatility Forecasting
Volatility is a statistical measure of the dispersion of returns for a given financial instrument. It quantifies how much an asset's price has changed over a specific period of time. Volatility is an important concept in finance because it helps investors and traders assess the risks associated with investing.
Volatility forecasting involves forecasting the future volatility of financial instruments based on historical data.
Accurate volatility forecasts can assist various financial applications such as portfolio optimization, risk management, and option pricing.
There are various approaches to volatility forecasting, including historical volatility, implied volatility, and model-based approaches. In this tutorial, we will focus on the GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model, which is widely used for volatility forecasting.
2. Retrieve historical volatility data yfinance
In order to develop our volatility