Modelling the Clustering Volatility of India's Wholesales Price Index and the Factors Affecting it

Mohammad Naim Azimi ()

MPRA Paper from University Library of Munich, Germany

Abstract: This paper proposes to examine the clustering volatility of India’s Wholesale Price Index throughout the period 1960 to 2014 by applying the ARCH(1) and GARCH(1) model. The pre-conditional requirement for the computation of ARCH (1,1) required us to perform several other tests i.e. Dickey Fuller, Ordinary Least Squared Regression and post OLS tests for investigating the ARCH effect in the first difference of WPI. The statistical analysis reveals a p-value of the GARCH mean model by 0.569 which is not significant at α 0.05 to explain that the previous period’s volatility can influence the WPI and the coefficient of WPI at first difference exhibits a value of less than 1 which is nice in magnitude with a p-value of ARCH by 0.005 at ∂ 0.05 which is significant to explain the volatility of WPI. The diagnostic test of autocorrelation in the residuals reveals that the residuals are white noise by exhibiting a corresponding probability value of 0.3757. Since, the overarching objective of this paper is to examine the clustering volatility of the aforementioned variable with regards to internal shocks, there might have been other factors of external shocks on WPI that are deliberately overlooked in this paper.

Keywords: Clustering Volatility; ARCH model; GARCH model; WPI; Gaussian distribution (search for similar items in EconPapers)
JEL-codes: C1 C15 D4 E2 (search for similar items in EconPapers)
Date: 2015-11-29
New Economics Papers: this item is included in nep-mac
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Published in Journal of Management and Sustainability 1.6(2016): pp. 141-148

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