Estimating the rolling three-factor model on a portfolio of assets
In this recipe, we learn how to estimate the three-factor model in a rolling fashion. What we mean by rolling is that we always consider an estimation window of a constant size (60 months, in this case) and roll it through the entire dataset, one period at a time. A potential reason for doing such an experiment is to test the stability of the results. Alternatively, we could also use an expanding window for this exercise.
In contrast to the previous recipes, this time, we use portfolio returns instead of a single asset. To keep things simple, we assume that our allocation strategy is to have an equal share of the total portfolio's value in each of the following stocks: Amazon, Google, Apple, and Microsoft. For this experiment, we use stock prices from the years 2010-2020.
How to do it...
Follow these steps to implement the rolling three-factor model in Python.
- Import the libraries:
import pandas as pd
import numpy...
