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A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements

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  • Bernardina Algieri

    (Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, 87030 Rende, Italy
    Department of Economic and Technological Change, Zentrum für Entwicklungsforschung (ZEF), Universität Bonn, Walter-Flex-Straße 3, 53113 Bonn, Germany
    These authors contributed equally to this work.)

  • Arturo Leccadito

    (Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, 87030 Rende, Italy
    These authors contributed equally to this work.)

  • Pietro Toscano

    (Wellington Management Company LLP, 280 Congress Street, Boston, MA 02210, USA
    These authors contributed equally to this work.)

Abstract

This study investigates the daily co-movements in commodity prices over the period 2006–2020 using a novel approach based on a time-varying Gerber correlation. The statistic is computed considering a set of probabilities estimated via non-traditional models that give a time-varying structure to the measure. The results indicate that there are several co-movements across commodities, that these co-movements change over time, and that they are tendentially positive. Conditional auto-regressive multithreshold logit models show higher forecasting accuracy for agricultural returns, while dynamic conditional correlation models are more accurate for energy products and metals. The proposed models are shown to be superior in terms of forecasting power to the benchmark method which is based on estimating the Gerber correlation moving a rolling window.

Suggested Citation

  • Bernardina Algieri & Arturo Leccadito & Pietro Toscano, 2021. "A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:2:p:22-354:d:555557
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    1. Ali, Sajid & Bouri, Elie & Czudaj, Robert Lukas & Shahzad, Syed Jawad Hussain, 2020. "Revisiting the valuable roles of commodities for international stock markets," Resources Policy, Elsevier, vol. 66(C).
    2. Mensi, Walid & Beljid, Makram & Boubaker, Adel & Managi, Shunsuke, 2013. "Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold," Economic Modelling, Elsevier, vol. 32(C), pages 15-22.
    3. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, September.
    4. Zaremba, Adam & Umar, Zaghum & Mikutowski, Mateusz, 2021. "Commodity financialisation and price co-movement: Lessons from two centuries of evidence," Finance Research Letters, Elsevier, vol. 38(C).
    5. Xinyu Yuan & Jiechen Tang & Wing-Keung Wong & Songsak Sriboonchitta, 2020. "Modeling Co-Movement among Different Agricultural Commodity Markets: A Copula-GARCH Approach," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    6. Byrne, Joseph P. & Fazio, Giorgio & Fiess, Norbert, 2013. "Primary commodity prices: Co-movements, common factors and fundamentals," Journal of Development Economics, Elsevier, vol. 101(C), pages 16-26.
    7. Taylor, James W., 2017. "Probabilistic forecasting of wind power ramp events using autoregressive logit models," European Journal of Operational Research, Elsevier, vol. 259(2), pages 703-712.
    8. Harvey, Andrew & Snyder, Ralph D., 1990. "Structural time series models in inventory control," International Journal of Forecasting, Elsevier, vol. 6(2), pages 187-198, July.
    9. Caldara, Dario & Cavallo, Michele & Iacoviello, Matteo, 2019. "Oil price elasticities and oil price fluctuations," Journal of Monetary Economics, Elsevier, vol. 103(C), pages 1-20.
    10. James W. Taylor & Keming Yu, 2016. "Using auto-regressive logit models to forecast the exceedance probability for financial risk management," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 1069-1092, October.
    11. Touhami Abdelkhalek & Jean-Marie Dufour, 1998. "Statistical Inference For Computable General Equilibrium Models, With Application To A Model Of The Moroccan Economy," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 520-534, November.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    13. O'Connor, Fergal A. & Lucey, Brian M. & Batten, Jonathan A. & Baur, Dirk G., 2015. "The financial economics of gold — A survey," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 186-205.
    14. Dufour, Jean-Marie & Valéry, Pascale, 2009. "Exact and asymptotic tests for possibly non-regular hypotheses on stochastic volatility models," Journal of Econometrics, Elsevier, vol. 150(2), pages 193-206, June.
    15. Bernardina Algieri, 2021. "Fast & furious: Do psychological and legal factors affect commodity price volatility?," The World Economy, Wiley Blackwell, vol. 44(4), pages 980-1017, April.
    16. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    17. Kalkuhl, Matthias & von Braun, Joachim & Torero, Maximo, 2016. "Food Price Volatility and Its Implications for Food Security and Policy," MPRA Paper 72164, University Library of Munich, Germany.
    18. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    19. Matthias Kalkuhl & Joachim von Braun & Maximo Torero (ed.), 2016. "Food Price Volatility and Its Implications for Food Security and Policy," Springer Books, Springer, number 978-3-319-28201-5, December.
    20. Luger, Richard, 2012. "Finite-sample bootstrap inference in GARCH models with heavy-tailed innovations," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3198-3211.
    21. Marc F. Bellemare, 2015. "Rising Food Prices, Food Price Volatility, and Social Unrest," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(1), pages 1-21.
    22. Angelo Algieri & Pietropaolo Morrone & Sergio Bova, 2020. "Techno-Economic Analysis of Biofuel, Solar and Wind Multi-Source Small-Scale CHP Systems," Energies, MDPI, vol. 13(11), pages 1-21, June.
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    Cited by:

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    2. Arturo Leccadito & Alessandro Staino & Pietro Toscano, 2024. "A novel robust method for estimating the covariance matrix of financial returns with applications to risk management," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-28, December.
    3. Ko, Hyungjin & Son, Bumho & Lee, Yunyoung & Jang, Huisu & Lee, Jaewook, 2022. "The economic value of NFT: Evidence from a portfolio analysis using mean–variance framework," Finance Research Letters, Elsevier, vol. 47(PA).

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