Copula Models for Multivariate Financial Data: Correlation and Dependency Analysis in Python
Understanding the relationships and dependencies between different assets is crucial for making informed investment decisions. Copula models provide a powerful framework for analyzing the correlation structure of multivariate financial data, allowing us to capture complex dependencies that traditional methods may overlook.
In this tutorial, we will explore how to use copula models in Python to analyze and visualize the correlation and dependency structure of financial assets. We will download real financial data using the yfinance library, build a multivariate dataset, fit copula models and generate stylish plots to visualize the results.
Downloading Financial Data
To begin our analysis, we need to download real financial data for multiple assets. We will use the yfinance library to fetch historical price data for a diverse set of securities listed on Yahoo Finance. Let's start by importing the necessary libraries and downloading the data.
import yfinance as yf
# Define a list of securities to download data for
securities = ['GOOG', 'TSLA', 'NFLX', 'MSFT', 'AAPL']
# Download historical price data for the securities
data = yf.download(securities, start='2020-01-01', end='2024-02-29')['Adj Close']