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Commodity Markets through the business cycle

Author

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  • Mathieu Gatumel

    (IREGE - Institut de Recherche en Gestion et en Economie - USMB [Université de Savoie] [Université de Chambéry] - Université Savoie Mont Blanc)

  • Florian Ielpo

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

We introduce a new meaure of risk appetite in financial markets, based on the cross sectional behavior of excess returns. Turning them into probabilities through a Markov Switching model, we define one global risk appetite measure as the cross-sectional average of the individual probabilities for each asset to be in a " risk appetite " regime. Given the probabilistic approach that comes naturally with this Markov Switching framework, we present various tests to gauge the interest of the risk appetite measure that is presented here. Using these tests we show that our index behaves well vs. various competitors, especially in out-of-sample results. We test for the information content of various assets and find that a core of asset allocation-related assets provide the best possible choice over various competing specifications.

Suggested Citation

  • Mathieu Gatumel & Florian Ielpo, 2014. "Commodity Markets through the business cycle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01302479, HAL.
    • Handle: RePEc:hal:cesptp:hal-01302479
    DOI: 10.2139/ssrn.2334180
    Note: View the original document on HAL open archive server: https://hal.science/hal-01302479
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    3. Wei Yang & Ai Han & Yongmiao Hong & Shouyang Wang, 2016. "Analysis of crisis impact on crude oil prices: a new approach with interval time series modelling," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1917-1928, December.
    4. Charalampos Stasinakis & Georgios Sermpinis & Ioannis Psaradellis & Thanos Verousis, 2016. "Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1901-1915, December.
    5. Lof, Matthijs & Nyberg, Henri, 2017. "Noncausality and the commodity currency hypothesis," Energy Economics, Elsevier, vol. 65(C), pages 424-433.
    6. Zhang, Yongmin & Ding, Shusheng, 2021. "Liquidity effects on price and return co-movements in commodity futures markets," International Review of Financial Analysis, Elsevier, vol. 76(C).
    7. Chevallier, Julien & Ielpo, Florian, 2017. "Investigating the leverage effect in commodity markets with a recursive estimation approach," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 763-778.

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