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Do International Agricultural Commodity Prices Have an Effect on the Stock Market Index? A Comparative Analysis Between Poland and Turkey

Year 2022, Volume: 30 Issue: 52, 87 - 107, 28.04.2022
https://doi.org/10.17233/sosyoekonomi.2022.02.06

Abstract

This study analysed the effects of international wheat, rice, sugar, and beef prices on Turkish and Polish stock exchange markets through the quantile regression and cointegration regressions methods from December 2008-November 2020. As a result of the analysis, it cannot be said that agricultural commodities do not affect stock market indices. Also, empirical evidence suggests that the impact of agricultural commodities on the Turkish stock market is more significant than on the Polish stock market. This may be because Poland’s economic ecosystem is more industrialised than Turkey’s. Further, these findings indicate that agricultural commodities have both similar and different effects on the stock market indices of these two countries.

References

  • Al-Maadid, A. et al. (2017), “Spillovers between food and energy prices and structural breaks”, International Economics, 150, 1-18.
  • Azimli, A. (2020), “The impact of COVID-19 on the degree of dependence and structure of risk-return relationship: A quantile regression approach”, Finance Research Letters, 36, 101648.
  • Bahloul, S. & I. Khemakhem (2021), “Dynamic return and volatility connectedness between commodities and Islamic stock market indices”, Resources Policy, 71, 101993.
  • Bandumula, N. (2018), “Rice Production in Asia: Key to global food security”, Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 88, 1323-1328.
  • Black, A.J. et al. (2014), “Forecasting stock returns: Do commodity prices help?”, Journal of Forecasting, 33(8), 627-639.
  • Bohl, M.T. & C. Sulewski (2019), “The impact of long-short speculators on the volatility of agricultural commodity futures prices”, Journal of Commodity Markets, 16, 100085, 1-11.
  • Candila, V. & S. Farace (2018), “On the volatility spillover between agricultural commodities and Latin American stock markets”, Risks, 6, 1-16.
  • Chen, Y.K. (2016), “Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory”, Economic Modelling, 52, 772-789.
  • Creti, A. et al. (2013), “On the links between stock and commodity markets' volatility”, Energy Economics, 37, 16-28.
  • Dawar, I. et al. (2021), “Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression”, Renewable Energy, 163, 288-299.
  • Dawe, D. (2010), The Rice Crisis: Markets, Policies and Food Security, Earthscan: London and Washington, DC.
  • El-Beltagy, A. & M. Madkou (2012), “Impact of climate change on arid lands agriculture”, Agriculture & Food Security, 1(3), 1-12.
  • Et ve Süt Kurumu (2018), Sektör Değerlendirme Raporu, Ankara.
  • FAO (2017), The future of food and agriculture - Trends and challenges, Rome.
  • Gadal, N. et al. (2019), “A review on production status and growing environments of rice in Nepal and the world”, Archives of Agriculture and Environmental Science, 4(1), 83-87.
  • Giraldo, P. et al. (2019), “Worldwide research trends on wheat and barley: A bibliometric comparative analysis”, Agronomy, 9(7), 1-18.
  • Girardi, D. (2015), “Financialization of food. Modeling the time-varying relation between agricultural prices and stock market dynamics”, International Review of Applied Economics, 29(4), 482-505.
  • Gomiero, T. (2016), “Soil degradation, land scarcity and food security: Reviewing a complex challenge”, Sustainability, 8(281), 1-41.
  • Greene, W.H. (2019), Econometric analysis, Pearson Education: Essex.
  • Gujarati, D. (2011), Econometrics by example, Palgrave Macmillan: London.
  • Hashemi, H. (2015), “Climate change and the future of water management in Iran”, Middle East Critique, 24(3), 307-323.
  • Hoang, T.V.H. et al. (2019), “Determinants influencing financial performance of listed firms: Quantile regression approach”, Asian Economic and Financial Review, 9(1), 78-90.
  • Hor, C. (2015), “Modeling international tourism demand in Cambodia: ARDL model”, Review of Integrative Business and Economics Research, 4(4), 106-120.
  • Hryszko, K. & P. Szajner (2017), “Polish sugar sector after abolishing sugar production quotas”, 34th International Academic Conference, Florence.
  • Huang, Y. et al. (2020), “The heterogeneous effect of driving factors on carbon emission intensity in the Chinese transport sector: Evidence from dynamic panel quantile regression”, Science of the Total Environment, 727, 138578.
  • İstiklal, D. (2020), “Dünya buğday ekonomisinde Türkiye”, Kriter Dergisi, (Eylül), 88-89.
  • Iwanska, M. et al. (2020), “Adaptation of winter wheat cultivars to different environments: A case study in Poland”, Agronomy, 10, 632.
  • Iyke, B.N. & S.Y. Ho (2021), “Stock return predictability over four centuries: The role of commodity returns”, Finance Research Letter, 40(May), 101711.
  • Jiang, Y. et al. (2020), “Impacts of global warming on the cropping systems of China under technical improvements from 1961 to 2016”, Agronomy Journal, 113, 187-199.
  • Jordan, S.J. et al. (2016), “Can commodity returns forecast Canadian sector stock returns?”, International Review of Economics and Finance, 41, 172-188.
  • Kaur, G. & B. Dhiman (2019), “Agricultural commodities and FMCG stock prices in India: Evidence from the ARDL bound test and the Toda and Yamamoto causality analysis”, Global Business Review, 1-12.
  • Koenker, R. & J.G. Bassett (1978), “Regression quantiles”, Econometrica, 46(1), 33-50.
  • Kotyza, P. et al. (2021), “Sugar prices vs. financial market uncertainty in the time of crisis: Does COVID-19 induce structural changes in the relationship?”, Agriculture, 11(93), 1-16.
  • Liang, C. et al. (2020), “Which types of commodity price information are more useful for predicting U.S. stock market volatility?”, Economic Modelling, 93, 1-9.
  • Lin, B. & B. Xu (2018), “Factors affecting CO2 emissions in China's agriculture sector: A quantile regression”, Renewable and Sustainable Energy Reviews, 94, 15-27.
  • Main, S. et al. (2018), “Financialization and the returns to commodity investments”, Journal of Commodity Markets, 10, 22-28.
  • Mensi, W. et al. (2013), “Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold”, Economic Modelling, 32, 15-22.
  • Misecka, T. et al. (2019), “In search of attention in agricultural commodity markets”, Economics Letters, 184, 108668, 1-6.
  • Mishra, A.K. & C.B. Moss (2013), “Modeling the effect of off-farm income on farmland values: A quantile regression approach”, Economic Modelling, 32, 361-368.
  • Mohanty, S.K. & S. Mishra (2020), “Regulatory reform and market efficiency: The case of Indian agricultural commodity futures markets”, Research in International Business and Finance, 52, 1-18.
  • Nguyen, D.C. et al. (2020), “U.S. equity and commodity futures markets: Hedging of financialization?”, Energy Economics, 86, 104660.
  • Nicola, F. et al. (2016), “Co-movement of major energy, agricultural, and food commodity price returns: A time-series assessment”, Energy Economics, 57, 28-41.
  • Ouyang, R. & X. Zhang (2020), “Financialization of agricultural commodities: Evidence from China”, Economic Modelling, 85, 381-389.
  • Öztek, M.F. & N. Öcal (2017), “Financial crises and the nature of correlation between commodity and stock markets”, International Review of Economics and Finance, 48, 56-68.
  • Park, J. (1992), “Canonical cointegrating regressions”, Econometrica, 60, 119-143.
  • Phillips, P. & B. Hansen (1990), “Statistical inference in instrumental variables regression with I(1) processes”, Review of Economic Studies, 57, 99-125.
  • Sadorsky, P. (2014), “Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat”, Energy Economics, 43, 72-81.
  • Sahu, P.K. et al. (2015), “Modelling and forecasting of area, production, yield and total seeds of rice and wheat in SAARC countries and the world towards food security”, American Journal of Applied Mathematics and Statistics, 3(1), 34-48.
  • Saikkonen, P. (1991), “Asymptotically efficient estimation of cointegration regressions”, Econometric Theory, 7, 1-21.
  • Satterthwaite, D. et al. (2010), “Urbanization and its implications for food and farming”, Philosophical Transactions of the Royal Society B, 365, 2809-2820.
  • Sevillano, M.C. & F. Jareno (2018), “The impact of international factors on Spanish company returns: a quantile regression approach”, Risk Management, 20, 51-76.
  • Sirin, S.M. & B.N. Yilmaz (2020), “Variable renewable energy technologies in the Turkish electricity market: Quantile regression analysis of the merit-order effect”, Energy Policy, 144, 1-15.
  • Siwar, C. et al. (2014), “Issues and challenges facing rice production and food security in the granary areas in the east coast economic region (ECER), Malaysia”, Research Journal of Applied Sciences, Engineering and Technology, 7(4), 711-722.
  • Stock, J. & M. Watson (1993), “A simple estimator of cointegrating vectors in higher order integrated systems”, Econometrica, 61(4), 783-820.
  • U.N. (2019), World Population Prospects 2019: Highlights, United Nations, Department of Economic and Social Affairs, Population Division, ST/ESA/SER.A/423.
  • USDA (2019), “Turkey sugar annual report”, USDA Foreign Agricultural Service Gain Report, 1-14.
  • Vandone, D. et al. (2018), “The impact of energy and agriculture prices on the stock performance of the water industry”, Water Resources and Economics, 23, 14-27.
  • Vladu, M. et al. (2021). “Study on the production and valorization of sugar beet in the European Union”, Romanian Agricultural Research, 38, 447-455.
  • Xu, B. & B. Lin (2020), “Investigating drivers of CO2 emission in China’s heavy industry: A quantile regression analysis”, Energy, 206, 1-13.
  • You, W. et al. (2017), “Oil price shocks, economic uncertainty and industry stock returns in China: Asymmetric effects with quantile regression”, Energy Economics, 68, 1-18.
  • Youssef, M. & K. Mokni (2020), “Modeling the relationship between oil and USD exchange rates: Evidence from a regime-switching-quantile regression approach”, Journal of Multinational Financial Management, 55, 1-19.
  • Zhang, F. et al. (2021), “Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection”, Information Sciences, 547, 244-254.
  • Zivkov, D. et al. (2020), “Short and long-term volatility transmission from oil to agricultural commodities - The robust quantile regression approach”, Borsa Istanbul Review, 20-S1, S11- S25.

Uluslararası Tarımsal Emtia Fiyatlarının Borsa Endeksi Üzerinde Etkisi Var Mıdır? Polonya ve Türkiye Arasında Karşılaştırmalı Bir Analiz

Year 2022, Volume: 30 Issue: 52, 87 - 107, 28.04.2022
https://doi.org/10.17233/sosyoekonomi.2022.02.06

Abstract

Bu çalışmada, uluslarası buğday, pirinç, şeker ve sığır eti fiyatlarının Türkiye ve Polonya borsalarına etkileri kantil regresyon ve eşbütünleşme regresyonları yöntemleri ile Aralık 2008-Kasım 2020 dönemi için analiz edilmiştir. Analizler sonucunda tarımsal emtiaların borsa endeksleri üzerinde hiçbir etkisinin olmadığı söylenemez. Ayrıca amprik kanıtlar, tarımsal emtiaların Türkiye borsası üzerindeki etkisinin Polonya borsasına göre daha fazla olduğunu göstermektedir. Bunun nedeni Polonya’nın ekonomik ekosisteminin Türkiye’den daha fazla sanayileşmiş olması olabilir. Ayrıca, bu bulgular tarımsal emtiaların bu iki ülkenin borsa endeksleri üzerinde hem benzer hem de farklı etkileri olduğunu göstermektedir.

References

  • Al-Maadid, A. et al. (2017), “Spillovers between food and energy prices and structural breaks”, International Economics, 150, 1-18.
  • Azimli, A. (2020), “The impact of COVID-19 on the degree of dependence and structure of risk-return relationship: A quantile regression approach”, Finance Research Letters, 36, 101648.
  • Bahloul, S. & I. Khemakhem (2021), “Dynamic return and volatility connectedness between commodities and Islamic stock market indices”, Resources Policy, 71, 101993.
  • Bandumula, N. (2018), “Rice Production in Asia: Key to global food security”, Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 88, 1323-1328.
  • Black, A.J. et al. (2014), “Forecasting stock returns: Do commodity prices help?”, Journal of Forecasting, 33(8), 627-639.
  • Bohl, M.T. & C. Sulewski (2019), “The impact of long-short speculators on the volatility of agricultural commodity futures prices”, Journal of Commodity Markets, 16, 100085, 1-11.
  • Candila, V. & S. Farace (2018), “On the volatility spillover between agricultural commodities and Latin American stock markets”, Risks, 6, 1-16.
  • Chen, Y.K. (2016), “Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory”, Economic Modelling, 52, 772-789.
  • Creti, A. et al. (2013), “On the links between stock and commodity markets' volatility”, Energy Economics, 37, 16-28.
  • Dawar, I. et al. (2021), “Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression”, Renewable Energy, 163, 288-299.
  • Dawe, D. (2010), The Rice Crisis: Markets, Policies and Food Security, Earthscan: London and Washington, DC.
  • El-Beltagy, A. & M. Madkou (2012), “Impact of climate change on arid lands agriculture”, Agriculture & Food Security, 1(3), 1-12.
  • Et ve Süt Kurumu (2018), Sektör Değerlendirme Raporu, Ankara.
  • FAO (2017), The future of food and agriculture - Trends and challenges, Rome.
  • Gadal, N. et al. (2019), “A review on production status and growing environments of rice in Nepal and the world”, Archives of Agriculture and Environmental Science, 4(1), 83-87.
  • Giraldo, P. et al. (2019), “Worldwide research trends on wheat and barley: A bibliometric comparative analysis”, Agronomy, 9(7), 1-18.
  • Girardi, D. (2015), “Financialization of food. Modeling the time-varying relation between agricultural prices and stock market dynamics”, International Review of Applied Economics, 29(4), 482-505.
  • Gomiero, T. (2016), “Soil degradation, land scarcity and food security: Reviewing a complex challenge”, Sustainability, 8(281), 1-41.
  • Greene, W.H. (2019), Econometric analysis, Pearson Education: Essex.
  • Gujarati, D. (2011), Econometrics by example, Palgrave Macmillan: London.
  • Hashemi, H. (2015), “Climate change and the future of water management in Iran”, Middle East Critique, 24(3), 307-323.
  • Hoang, T.V.H. et al. (2019), “Determinants influencing financial performance of listed firms: Quantile regression approach”, Asian Economic and Financial Review, 9(1), 78-90.
  • Hor, C. (2015), “Modeling international tourism demand in Cambodia: ARDL model”, Review of Integrative Business and Economics Research, 4(4), 106-120.
  • Hryszko, K. & P. Szajner (2017), “Polish sugar sector after abolishing sugar production quotas”, 34th International Academic Conference, Florence.
  • Huang, Y. et al. (2020), “The heterogeneous effect of driving factors on carbon emission intensity in the Chinese transport sector: Evidence from dynamic panel quantile regression”, Science of the Total Environment, 727, 138578.
  • İstiklal, D. (2020), “Dünya buğday ekonomisinde Türkiye”, Kriter Dergisi, (Eylül), 88-89.
  • Iwanska, M. et al. (2020), “Adaptation of winter wheat cultivars to different environments: A case study in Poland”, Agronomy, 10, 632.
  • Iyke, B.N. & S.Y. Ho (2021), “Stock return predictability over four centuries: The role of commodity returns”, Finance Research Letter, 40(May), 101711.
  • Jiang, Y. et al. (2020), “Impacts of global warming on the cropping systems of China under technical improvements from 1961 to 2016”, Agronomy Journal, 113, 187-199.
  • Jordan, S.J. et al. (2016), “Can commodity returns forecast Canadian sector stock returns?”, International Review of Economics and Finance, 41, 172-188.
  • Kaur, G. & B. Dhiman (2019), “Agricultural commodities and FMCG stock prices in India: Evidence from the ARDL bound test and the Toda and Yamamoto causality analysis”, Global Business Review, 1-12.
  • Koenker, R. & J.G. Bassett (1978), “Regression quantiles”, Econometrica, 46(1), 33-50.
  • Kotyza, P. et al. (2021), “Sugar prices vs. financial market uncertainty in the time of crisis: Does COVID-19 induce structural changes in the relationship?”, Agriculture, 11(93), 1-16.
  • Liang, C. et al. (2020), “Which types of commodity price information are more useful for predicting U.S. stock market volatility?”, Economic Modelling, 93, 1-9.
  • Lin, B. & B. Xu (2018), “Factors affecting CO2 emissions in China's agriculture sector: A quantile regression”, Renewable and Sustainable Energy Reviews, 94, 15-27.
  • Main, S. et al. (2018), “Financialization and the returns to commodity investments”, Journal of Commodity Markets, 10, 22-28.
  • Mensi, W. et al. (2013), “Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold”, Economic Modelling, 32, 15-22.
  • Misecka, T. et al. (2019), “In search of attention in agricultural commodity markets”, Economics Letters, 184, 108668, 1-6.
  • Mishra, A.K. & C.B. Moss (2013), “Modeling the effect of off-farm income on farmland values: A quantile regression approach”, Economic Modelling, 32, 361-368.
  • Mohanty, S.K. & S. Mishra (2020), “Regulatory reform and market efficiency: The case of Indian agricultural commodity futures markets”, Research in International Business and Finance, 52, 1-18.
  • Nguyen, D.C. et al. (2020), “U.S. equity and commodity futures markets: Hedging of financialization?”, Energy Economics, 86, 104660.
  • Nicola, F. et al. (2016), “Co-movement of major energy, agricultural, and food commodity price returns: A time-series assessment”, Energy Economics, 57, 28-41.
  • Ouyang, R. & X. Zhang (2020), “Financialization of agricultural commodities: Evidence from China”, Economic Modelling, 85, 381-389.
  • Öztek, M.F. & N. Öcal (2017), “Financial crises and the nature of correlation between commodity and stock markets”, International Review of Economics and Finance, 48, 56-68.
  • Park, J. (1992), “Canonical cointegrating regressions”, Econometrica, 60, 119-143.
  • Phillips, P. & B. Hansen (1990), “Statistical inference in instrumental variables regression with I(1) processes”, Review of Economic Studies, 57, 99-125.
  • Sadorsky, P. (2014), “Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat”, Energy Economics, 43, 72-81.
  • Sahu, P.K. et al. (2015), “Modelling and forecasting of area, production, yield and total seeds of rice and wheat in SAARC countries and the world towards food security”, American Journal of Applied Mathematics and Statistics, 3(1), 34-48.
  • Saikkonen, P. (1991), “Asymptotically efficient estimation of cointegration regressions”, Econometric Theory, 7, 1-21.
  • Satterthwaite, D. et al. (2010), “Urbanization and its implications for food and farming”, Philosophical Transactions of the Royal Society B, 365, 2809-2820.
  • Sevillano, M.C. & F. Jareno (2018), “The impact of international factors on Spanish company returns: a quantile regression approach”, Risk Management, 20, 51-76.
  • Sirin, S.M. & B.N. Yilmaz (2020), “Variable renewable energy technologies in the Turkish electricity market: Quantile regression analysis of the merit-order effect”, Energy Policy, 144, 1-15.
  • Siwar, C. et al. (2014), “Issues and challenges facing rice production and food security in the granary areas in the east coast economic region (ECER), Malaysia”, Research Journal of Applied Sciences, Engineering and Technology, 7(4), 711-722.
  • Stock, J. & M. Watson (1993), “A simple estimator of cointegrating vectors in higher order integrated systems”, Econometrica, 61(4), 783-820.
  • U.N. (2019), World Population Prospects 2019: Highlights, United Nations, Department of Economic and Social Affairs, Population Division, ST/ESA/SER.A/423.
  • USDA (2019), “Turkey sugar annual report”, USDA Foreign Agricultural Service Gain Report, 1-14.
  • Vandone, D. et al. (2018), “The impact of energy and agriculture prices on the stock performance of the water industry”, Water Resources and Economics, 23, 14-27.
  • Vladu, M. et al. (2021). “Study on the production and valorization of sugar beet in the European Union”, Romanian Agricultural Research, 38, 447-455.
  • Xu, B. & B. Lin (2020), “Investigating drivers of CO2 emission in China’s heavy industry: A quantile regression analysis”, Energy, 206, 1-13.
  • You, W. et al. (2017), “Oil price shocks, economic uncertainty and industry stock returns in China: Asymmetric effects with quantile regression”, Energy Economics, 68, 1-18.
  • Youssef, M. & K. Mokni (2020), “Modeling the relationship between oil and USD exchange rates: Evidence from a regime-switching-quantile regression approach”, Journal of Multinational Financial Management, 55, 1-19.
  • Zhang, F. et al. (2021), “Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection”, Information Sciences, 547, 244-254.
  • Zivkov, D. et al. (2020), “Short and long-term volatility transmission from oil to agricultural commodities - The robust quantile regression approach”, Borsa Istanbul Review, 20-S1, S11- S25.
There are 63 citations in total.

Details

Primary Language English
Subjects Economics
Journal Section Articles
Authors

Kenan İlarslan 0000-0002-5097-7552

Münevvere Yıldız 0000-0001-9541-2603

Publication Date April 28, 2022
Submission Date July 6, 2021
Published in Issue Year 2022 Volume: 30 Issue: 52

Cite

APA İlarslan, K., & Yıldız, M. (2022). Do International Agricultural Commodity Prices Have an Effect on the Stock Market Index? A Comparative Analysis Between Poland and Turkey. Sosyoekonomi, 30(52), 87-107. https://doi.org/10.17233/sosyoekonomi.2022.02.06
AMA İlarslan K, Yıldız M. Do International Agricultural Commodity Prices Have an Effect on the Stock Market Index? A Comparative Analysis Between Poland and Turkey. Sosyoekonomi. April 2022;30(52):87-107. doi:10.17233/sosyoekonomi.2022.02.06
Chicago İlarslan, Kenan, and Münevvere Yıldız. “Do International Agricultural Commodity Prices Have an Effect on the Stock Market Index? A Comparative Analysis Between Poland and Turkey”. Sosyoekonomi 30, no. 52 (April 2022): 87-107. https://doi.org/10.17233/sosyoekonomi.2022.02.06.
EndNote İlarslan K, Yıldız M (April 1, 2022) Do International Agricultural Commodity Prices Have an Effect on the Stock Market Index? A Comparative Analysis Between Poland and Turkey. Sosyoekonomi 30 52 87–107.
IEEE K. İlarslan and M. Yıldız, “Do International Agricultural Commodity Prices Have an Effect on the Stock Market Index? A Comparative Analysis Between Poland and Turkey”, Sosyoekonomi, vol. 30, no. 52, pp. 87–107, 2022, doi: 10.17233/sosyoekonomi.2022.02.06.
ISNAD İlarslan, Kenan - Yıldız, Münevvere. “Do International Agricultural Commodity Prices Have an Effect on the Stock Market Index? A Comparative Analysis Between Poland and Turkey”. Sosyoekonomi 30/52 (April 2022), 87-107. https://doi.org/10.17233/sosyoekonomi.2022.02.06.
JAMA İlarslan K, Yıldız M. Do International Agricultural Commodity Prices Have an Effect on the Stock Market Index? A Comparative Analysis Between Poland and Turkey. Sosyoekonomi. 2022;30:87–107.
MLA İlarslan, Kenan and Münevvere Yıldız. “Do International Agricultural Commodity Prices Have an Effect on the Stock Market Index? A Comparative Analysis Between Poland and Turkey”. Sosyoekonomi, vol. 30, no. 52, 2022, pp. 87-107, doi:10.17233/sosyoekonomi.2022.02.06.
Vancouver İlarslan K, Yıldız M. Do International Agricultural Commodity Prices Have an Effect on the Stock Market Index? A Comparative Analysis Between Poland and Turkey. Sosyoekonomi. 2022;30(52):87-107.