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Dating Common Commodity Price And Inflation Shocks With Alternative Approaches

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  • Roberto Esposti

    (Department of Economics and Social Sciences, Marche Polytechnic University)

Abstract

This paper investigates the occurrence of common price shocks (coexceedance) across different commodities. Instead of looking for a common Data Generating Process (DGP), the analysis considers two possible alternatives. The first looks for common volatility clusters trough individual GARCH models then detecting whether and when respective clusters overlap. The second alternative looks for a common Bubble Generating Process (BGP) by firstly looking for individual explosive roots and then dating them to identify the possible overlaps and early movers. Evidence emerging about these shocks' generating processes are linked to the analogous behaviour of the Consumer Price Index (CPI) to assess to what extent inflation shocks can be associated to the observed commodity price spikes. Results show that the detection of temporary bubbles and of volatility clusters only partially agrees on the episodes of exuberance, on the early-moving commodities and on the involvement of the CPI.

Suggested Citation

  • Roberto Esposti, 2022. "Dating Common Commodity Price And Inflation Shocks With Alternative Approaches," Working Papers 469, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    • Handle: RePEc:anc:wpaper:469
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    References listed on IDEAS

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    Cited by:

    1. Roberto Esposti, 2022. "Who Moves First? Commodity Price Interdependence Through Time-Varying Granger Causality," Working Papers 471, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

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    More about this item

    Keywords

    Commodity Prices; Price Volatility; Explosive Roots;
    All these keywords.

    JEL classification:

    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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