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Asymmetries in Inflation Volatility:Evidence from India


Affiliations
1 Department of Economics, University of Rajasthan, JLN Marg, Jaipur - 302004, Rajasthan, India
 

This paper records the volatility and asymmetry present in Indian inflation. The study uses monthly data from January 1991 to December 2016 of Wholesale Price Index (WPI) and Consumer Price Index (CPI) inflation to examine and model the volatility in the backdrop of changes in the monthly Crude Oil and Gold Prices. The methodology uses a generalized autoregressive conditional heteroskedasticity (GARCH) model along with exponential GARCH (EGARCH) and threshold GARCH (TGARCH). The analysis reveals that crude oil and gold price affect WPI and CPI differently. While crude oil price seems to be an insignificant factor contributing to CPI volatility, gold price emerges as a consequential factor influencing WPI inflation volatility.

 


Keywords

EGARCH, Inflation Uncertainty, TGARCH.
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  • Azimi, M. N. (2016). Modeling the Clustering Volatility of India’s Wholesale Price Index and the Factors Affecting It. J. Mgmt. & Sustainability, 6, 141.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  • Chowdhury, A. (2014). Inflation and inflation-uncertainty in India: the policy implications of the relationship. Journal of Economic Studies, 41(1), 71-86.
  • Emara, N. (2012). Inflation volatility, institutions, and economic growth. Global Journal of Emerging Market Economies, 4(1), 29-53.
  • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4), 987-1007.
  • Friedman, M. (1977). Nobel lecture: inflation and unemployment. Journal of political economy, 85(3), 451-472.
  • Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), 347-370.
  • Rabemananjara, R. & Zakoian, J. M. (1993). Threshold ARCH models and asymmetries in volatility. Journal of Applied Econometrics, 8(1), 31-49.
  • Rother, P. (2004). Fiscal policy and inflation volatility. ECB Working Paper No. 317. Available at SSRN: https://ssrn.com/abstract=515081 .
  • Wu, J. (2010). Threshold GARCH model: Theory and application. The University of Western Ontario.

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  • Asymmetries in Inflation Volatility:Evidence from India

Abstract Views: 237  |  PDF Views: 88

Authors

Swapnil Bhardwaj
Department of Economics, University of Rajasthan, JLN Marg, Jaipur - 302004, Rajasthan, India

Abstract


This paper records the volatility and asymmetry present in Indian inflation. The study uses monthly data from January 1991 to December 2016 of Wholesale Price Index (WPI) and Consumer Price Index (CPI) inflation to examine and model the volatility in the backdrop of changes in the monthly Crude Oil and Gold Prices. The methodology uses a generalized autoregressive conditional heteroskedasticity (GARCH) model along with exponential GARCH (EGARCH) and threshold GARCH (TGARCH). The analysis reveals that crude oil and gold price affect WPI and CPI differently. While crude oil price seems to be an insignificant factor contributing to CPI volatility, gold price emerges as a consequential factor influencing WPI inflation volatility.

 


Keywords


EGARCH, Inflation Uncertainty, TGARCH.

References