CHING'S MARKOV CHAIN MODELS
FUEL PRICE PREDICTION
R. Sasikumar and A. S. Abdullah (2017). An application of multivariate markov chain model for fuel price changes in India. Global and Stochastic Analysis,4: 49-61
In Sasikumar et al. (2017), the authors considered the problem of analyzing the relationship between the price of petrol and diesel and the price of crude oil. They proposed to use a multivariate Markov chain model to analyze to what extent these categorical these two prices may be affected by each other.
The monthly changes of the Petrol and Diesel prices and the monthly changes of the crude oil price are taken into consideration as two categorical data sequences. There are five states which are the values of increase over the mean value of increase, the values of increase below the mean value of increase, the values of decrease over the mean value of decrease, the values of decrease below the mean value of decrease and the no change. The authors then follow the parsimonious multivariate Markov chain model along with the parameter estimation method in Ching et al. (2002) to analyze those categorical data sequences. With the help of the parsimonious higher-order multivariate Markov chain model along with the parameter estimation method in Ching et al (2004, 2008), the required parameters are significantly less than the conventional one. Also, the parameter estimation problem can be reduced to linear programming problem which is easier to handle.
The authors in Sasikumar et al. (2017) implement the multivariate Markov chain model to analyze the monthly price changes of Crude oil, Petrol and Diesel price data for the period of October 2005 to September 2016 were obtained from the yahoofinance.com. They then conclude that it is possible to predict the probability values of moving from one state to another states for Petrol and Diesel prices.
W. Ching, E. Fung, M. Ng, A multivariate Markov chain model for categorical data
sequences and its applications in demand predictions, IMA Journal of Management
Mathematics 13 (2002) 87–199.