You are a volatility trader and would like to classify the VIX index of future volatility (http://www.cboe.com/vix) in periods of low, middle, and high volatility. As you have access to the historical VIX values (file ^Vix.csv), train a Hidden Markov Model (HMM) to obtain three states of the economy (high, medium, and low volatility). You can use the GaussianHMM function from the hmmlearn (hmmlearn.hmm) Python package. Instructions to install the package hmmlearn: - Open an Anaconda terminal selecting "Run as Administrator" - Submit the following code in this terminal: conda install -c conda-forge hmmlearn You can also explore the package and other ways to install this package at: https://github.com/hmmlearn/hmmlearn

You can also explore the tutorial of hmmlearn and examples at: https://hmmlearn.readthedocs.io/en/latest/tutorial.html

1). With the complete dataset, create the variable Xp that includes the following periods (you will not use Xp for your model):

0. Low volatility: observations less or equal than the 15% percentile. 1. Medium volatility 2. High volatility: observations greater or equal than the 85% percentile.