Portfolio Immunization and Cash Flow Matching: Techniques in Python for Liability-Driven Investing

In the world of finance, managing a portfolio to meet future liabilities is a critical task. Two primary techniques used for this purpose are portfolio immunization and cash flow matching. These techniques ensure that the portfolio is structured in such a way that it can meet future liabilities regardless of interest rate movements. In this tutorial, we will delve into these techniques using Python, focusing on real-world applications and data.

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Introduction

Portfolio immunization and cash flow matching are essential strategies in liability-driven investing (LDI). Immunization involves structuring a portfolio such that its duration matches the duration of the liabilities, thereby minimizing the impact of interest rate changes. On the other hand, cash flow matching involves creating a portfolio where the cash flows from the assets match the timing and amount of the liabilities.

In this tutorial, we will use Python to implement these techniques. We will download real financial data using the yfinance library, perform calculations to match durations and cash flows and visualize the results using various plotting libraries. By the end of this tutorial, you will have a comprehensive understanding of how to implement these strategies in Python.

Setting Up the Environment

First, let’s install the necessary libraries. We will use yfinance for downloading financial data, numpy for numerical calculations, pandas for data manipulation and matplotlib and plotly for plotting.

pip install yfinance numpy pandas matplotlib plotly

Now, let’s import these libraries in our Python script.

import yfinance as yf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objects as go

Downloading Financial Data

We will start by downloading historical data for a few bonds and stocks. For this tutorial, we will use bonds from different sectors and stocks from various industries to diversify our portfolio. Let’s download the data until the end of June 2024.