Abstract
<p>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 <em>p-value</em> of 0.569 for the GARCH mean model which is not significant at ∂ 0.05 to explain that the previous period’s volatility can influence the WPI. The coefficient of WPI at first difference exhibits a value of less than 1 which is nice in magnitude with a <em>p</em>-value of 0.005 for ARCH at ∂ 0.05 which is significant to explain the volatility of the 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 the internal shocks, there might have been other factors of external shocks on WPI that have deliberately been overlooked in this paper.</p>
Highlights
Wholesale Price Index (WPI) as a macroeconomic variable has always been the central theme of many research papers studying to investigate the reason of movement in the values overtime, given rise to critics in which clustering volatility is a significant economic phenomenon to be debated
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 overarching objective of this paper is to examine the clustering volatility of the aforementioned variable with regards to the internal shocks, there might have been other factors of external shocks on WPI that have deliberately been overlooked in this paper
Summary
Wholesale Price Index (WPI) as a macroeconomic variable has always been the central theme of many research papers studying to investigate the reason of movement in the values overtime, given rise to critics in which clustering volatility is a significant economic phenomenon to be debated. It is assumed that the computation of ARCH and GARCH model is only a decade ago, but its initiation goes far as back as to Bachelier (1900) who had initially observed and tested for the price speculation. The extensive literature on economic and financial time series analysis suggest that such volatile and gradually changing variables computation is based on ARCH and GARCH models (Guo, 2006; Nelson, 1991; Zakoian, 1994; Higgins et al, 1992; Ding et al, 1993; Engle, 1982). The remainder of this paper is organized as follow: Section 2, illustrates the data and testing procedures plus the pre-conditional requirements for the application of ARCH and GARCH model, Section 3, presents the data analysis and research findings, Section 4, concludes the paper and is followed by acknowledgement of the author and the list of references
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
