INDIA'S WHOLESALE INFLATION RATE: AN EXAMINATION OF VOLATILITY CLUSTERING AND LEVERAGE EFFECTS

Abstract

The Volatility in inflation rates is a significant concern for policymakers, economists, and financial analysts due to its profound impact on economic planning, investment decisions, and price stability. This study aims to investigate the existence of volatility clustering and leverage effects in India's Wholesale Inflation rate over a span of eighteen years, from January 2005 to June 2022. The research employs the monthly Wholesale Price Index (WPI) inflation data, a crucial indicator for wholesale-level price movements in the Indian economy, sourced from the Government of India's official data repository. Volatility clustering refers to the phenomenon where high-volatility events tend to cluster together, followed by periods of relative calm, indicating persistence in volatility. Leverage effects, on the other hand, reflect the asymmetric relationship between past returns and future volatility, typically suggesting that negative shocks have a greater impact on volatility than positive ones of similar magnitude. Identifying these characteristics in inflation dynamics is essential for designing accurate predictive models and developing informed policy responses. The study begins by testing the stationarity of the WPI inflation series using the Augmented Dickey-Fuller (ADF) test. The findings confirm that the data series is non- stationary at level but achieves stationarity at the first difference, implying that the statistical properties of the series such as mean and variance become constant after differencing. This transformation is a necessary precondition for applying volatility modelling techniques effectively. Following the stationarity check, the Autoregressive Conditional Heteroskedasticity (ARCH) model is employed to detect the presence of heteroskedasticity and to examine leverage effects. The ARCH model is particularly useful for modelling time- varying volatility by capturing the influence of past forecast errors on current volatility. In the context of this study, it was used to analyse whether past inflation shocks, both positive and negative, influence the future volatility of wholesale inflation rates. The results from the ARCH model suggest that while volatility clustering is present, the leverage effect is minimal. This indicates a relatively symmetric response of volatility to inflation shocks, suggesting that positive and negative shocks have a comparable impact on future volatility. To understand more about the the asymmetry and persistence of volatility, the study further applies the Exponential GARCH (EGARCH) model, an advanced variant of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family that explicitly accounts for asymmetric effects. The EGARCH model allows for a more refined understanding of volatility patterns by incorporating the possibility that volatility may respond differently to positive and negative shocks, and also allows for logarithmic specification to ensure non-negativity of variance without constraints. However, the application of the EGARCH model yielded results that support the earlier findings—although there is evidence of volatility clustering, the impact of past volatility on current wholesale inflation is statistically insignificant. This suggests that past high-volatility episodes in India’s WPI inflation do not necessarily result in increased volatility in the current period, highlighting a weaker persistence of volatility and an absence of strong leverage effects in the dataset studied. Overall, the findings of this research provide valuable insights into the nature of inflation volatility in India. The limited presence of leverage effects and insignificant influence of lagged volatility suggest that India’s wholesale inflation rate is more likely influenced by real-time economic shocks and external macroeconomic factors rather than its own past behaviour. These insights can be crucial for central banks and policymakers in refining inflation targeting strategies and ensuring economic stability. This study also contributes to the growing literature on inflation volatility modelling in emerging economies by demonstrating the usefulness and limitations of ARCH-type models in capturing the nuanced dynamics of inflation volatility. Further research may explore structural models or incorporate external macroeconomic variables to enhance the predictability of wholesale inflation trends in India.