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A Markov Chain Model for Vegetable Price Movement in Jaffna

Authors:

P. Jasinthan,

University of Jaffna, Jaffna, LK
About P.
Department of Mathematics and Statistics
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A. Laheetharan ,

University of Jaffna, Jaffna, LK
About A.
Department of Mathematics and Statistics
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N. Satkunanathan

University of Jaffna, Jaffna, LK
About N.
Department of Mathematics and Statistics
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Abstract

The modeling of random movement events is an important aspect of stochastic analysis. Developing a stochastic model to represent the price movement at various time intervals has long been a nature of interest in stock market, business processes and others. The daily price data of vegetables at Thirunelvelly market for 1704 days (Jan 2009-Sept 2013) have been collected from Vallampuri paper corporation, Jaffna, Sri Lanka. The data were analyzed to classify the price movement change on two successive days. Many scholars showed that modeling a market as a random walk was applicable and that a market may be viewed as having the Markov property. The objective of this paper is to construct the Markov chain model for daily vegetable price movement in Jaffna. Two models are highlighted, where the price movement is considered as being in a state of (1) gain or loss and (2) large gain, or small gain or loss, or large loss. Twelve different types of vegetables are considered and above two models are used to analyze the price movement of each vegetable. These models were used to obtain transitional probabilities, steady state probabilities and mean recurrence times. Our results indicate that the pattern of price movement of brinjal (eggplant) is similar to price movements of other vegetables, both in models (1) and (2). The investor is encouraged to invest in the vegetable market at any time in a way which leads to a greater chance of getting more gain than loss.

 

Sri Lankan Journal of Applied Statistics 2015;16(2): 93-105

How to Cite: Jasinthan, P., Laheetharan, A. & Satkunanathan, N., (2015). A Markov Chain Model for Vegetable Price Movement in Jaffna. Sri Lankan Journal of Applied Statistics. 16(2), pp.93–105. DOI: http://doi.org/10.4038/sljastats.v16i2.7825
Published on 19 Oct 2015.
Peer Reviewed

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