Do commodity prices matter for global systemic risk? Evidence from ML variable selection

We identify robust predictors of global systemic risk proxied by conditional capital shortfall (SRISK) among a comprehensive set of commodity prices for the period between January 2004 and December 2021. The search is based on a battery of ML variable selection algorithms which apply both to price levels and price shocks in the presence of control variables, including the first lag of SRISK, world industrial production, global economic policy uncertainty, geopolitical risk as well as the global stance of monetary and macroprudential policies. We find that these controls outweigh commodity prices as the predictors of global systemic risk. Of the commodities themselves, the prices for agricultural commodities, including food, e.g. chicken, bananas, beef, tea, cocoa, are more important predictors of global systemic risk than the prices for energy commodities, e.g. natural gas and oil prices. The financialization of agricultural commodities, bio-energy expansion as well as commodity-specific dependence of the major economies contributing to global systemic risk, e.g. China, account for our main finding. We also document the positive linkage between commodity prices and systemic risk for the majority of commodities. Thus, monitoring commodity prices to avoid their unbalanced growth is of vast importance to curb global systemic financial risk.

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Publication Details

Journal
The Journal of Finance and Data Science
Published
2024-10-22
DOI
https://doi.org/10.1016/j.jfds.2024.100144
Citations
1
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
1.13
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article

Do commodity prices matter for global systemic risk? Evidence from ML variable selection

Maria Shchepeleva, Mikhail Stolbov
1 citations
The Journal of Finance and Data Science
Market Dynamics and Volatility
1.13
article

Do commodity prices matter for global systemic risk? Evidence from ML variable selection

Maria Shchepeleva, Mikhail Stolbov
article en
1 citations

Abstract

We identify robust predictors of global systemic risk proxied by conditional capital shortfall (SRISK) among a comprehensive set of commodity prices for the period between January 2004 and December 2021. The search is based on a battery of ML variable selection algorithms which apply both to price levels and price shocks in the presence of control variables, including the first lag of SRISK, world industrial production, global economic policy uncertainty, geopolitical risk as well as the global stance of monetary and macroprudential policies. We find that these controls outweigh commodity prices as the predictors of global systemic risk. Of the commodities themselves, the prices for agricultural commodities, including food, e.g. chicken, bananas, beef, tea, cocoa, are more important predictors of global systemic risk than the prices for energy commodities, e.g. natural gas and oil prices. The financialization of agricultural commodities, bio-energy expansion as well as commodity-specific dependence of the major economies contributing to global systemic risk, e.g. China, account for our main finding. We also document the positive linkage between commodity prices and systemic risk for the majority of commodities. Thus, monitoring commodity prices to avoid their unbalanced growth is of vast importance to curb global systemic financial risk.

The Journal of Finance and Data ScienceVol. 10
National Research University Higher School of Economics (RU), Moscow State Institute of International Relations (RU)
Decent work and economic growth
Openalex Percentile: Top 11%
Market Dynamics and Volatility
1.13
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Do commodity prices matter for global systemic risk? Evidence from ML variable selection — Maria Shchepeleva, Mikhail Stolbov · The Journal of Finance and Data Science (2024) | TGRS Research Map | TGRS