Modelling The Joint Dynamics of Rail Vikas And Indian Railway Finance Corporation (IRFC) Using A Multivariate Markov Process

Abstract

In time series analysis, Markov models are frequently employed methods. A stochastic
method for modeling systems in which the future state solely depends on the present state is
the Markov Chain (MC). By adding more than one variable, the Multivariate Markov Model
(MMC) expands on the classic Markov model to examine the likelihood of future states.
The relationship between the stock prices of IRFC and Rail Vikas is investigated in this study,
along with the ways in which each stock price affects the other and the likelihood of price
movements. The MMC model is used to determine whether these stocks tend to move in
tandem and to predict whether they will rise, fall, or stay the same. The study emphasizes how
the stock prices of Rail Vikas and IRFC, two companies with major roles in the transportation
and infrastructure industries, are actively correlated. The MMC model successfully captures
non-linear relationships and regime shifts that conventional models like linear regression and
VAR might not adequately account for when using historical stock data. The results show that
there are times when the two stocks are correlated, especially when the market is moving. The
study concludes that MMC offers insightful information about the short- and long-term
relationships between the stock prices of IRFC and Rail Vikas, with useful ramifications for
investors and decision-makers who want to comprehend market dynamics.