Global commodity prices and inflation expectations

Abstract

PurposeIn this paper, we study the effect of global energy and food inflation on household inflation expectations during the period 1988M01–2020M03 for a set of European economies.Design/methodology/approachWe use multifractal de-trended cross-correlation analysis to estimate the non-linear and time-varying cross-correlation. We provide additional robustness tests using the Autoregressive-Distributed Lag method.FindingsWe find that household inflation expectations, global energy inflation and global food inflation are all multifractal. We also find that the household inflation expectations, global energy inflation and global food inflation are positively correlated (i.e., they are persistent). However, household inflation expectations respond more when the volatility of the global energy inflation is lower than when the volatility is higher. The correlation between household inflation expectations and global food inflation does not depend on the level of volatility.Research limitations/implicationsFirst, paying attention to the global commodity inflation might help anchor inflation expectations better. It is so because Central Bank's efficacy in achieving price stability may be weakened if there is a relationship between commodity inflation and inflation expectation. This task would become even more difficult in the average inflation targeting regime than inflation targeting regime if actual inflation is persistently different from the target inflation. Second, our results also emphasize the importance of effective strategy for communicating to households about actual inflation, inflation target and keep them updated about how monetary policy functions.Originality/valueWe contribute to the literature by estimating the cross-correlation between household inflation expectations with the global commodity inflation, conditional to the volatility of the commodity inflation under consideration.

Navigating Choppy Waters: Interplay between Financial Stress and Commodity Market Indices

“…Since then, DCCA and MFDCCA have been extensively utilized in fields such as finance, chemistry and geophysics [88][89][90][91]. Regarding empirical investigation, several studies applied the MFDCCA approach to explore the cross-correlations between two financial time series, for instance efficiency of stock prices, Cryptocurrency prices and economic policy uncertainty, commodity prices and energy market, tourism and supply chain management [92][93][94][95][96][97][98][99].…”