Markov chains - the stocks regime switching model

In the last few decades, a lot of studies have been conducted on the analysis and forecasting of volatility. Volatility is the degree of variation of a trading price series over time as measured by the standard deviation of returns. Models of stock returns assume that the returns follow a geometric Brownian motion. This implies that over any discrete time interval, the return on stocks is log normally distributed and that returns in non-overlapping intervals are independent. Studies have found that this model fails to capture extreme price movements and stochastic variability in the volatility parameter. Stochastic volatility takes discrete values, switching between these values randomly. This is the basis of the regime-switching lognormal process (RSLN).

Getting ready

In order to perform the Markov chains regime switching model we shall be using data collected from the Stock's dataset.

Step 1 - collecting and describing the data

The dataset...