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İklim Politikası Belirsizliği ile Tarım ve Gıda Piyasası Endeksleri Arasındaki İlişki: TVP VAR Yaklaşımı

Yıl 2025, Cilt: 13 Sayı: 1, 46 - 64, 30.06.2025
https://doi.org/10.52122/nisantasisbd.1626552

Öz

Climate change significantly affects the availability, accessibility, quality and stability of food in the world. Climate change has the power to affect relevant companies, investors and policy-makers by putting pressure on agricultural production and practices. In this regard, the main purpose of this paper examines the dynamic connectivity nexus between the Climate Policy Uncertainty Index (CPU), FTSE 350 Food Producers Index (FTSE 350), S&P Commodity Producers Agriculture Net Return Index (S&P Commodity), FAO Food Price Index (FAO) and DAX Global Agricultural Index (DAX). In the paper time-varying parameter vector autoregressive (TVP-VAR) model was used in period of July 2007 to July 2022. It was observed that the FTSE 350 index spreads strong volatility to the CPU, S&P Commodity index and DAX index. In addition, it has been determined that S&P Commodity and DAX index emit weak volatility due to climate policy uncertainty.

Kaynakça

  • Abbas, S., Kousar, S., & Khan, M. S. (2022). The role of climate change in food security; empirical evidence over Punjab regions, Pakistan. Environmental Science and Pollution Research, 29(35), 53718-53736.
  • Adekoya, O. B., & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. Resources Policy, 70, 101898.
  • Adnan, S., Ullah, K., Gao, S., Khosa, A. H., & Wang, Z. (2017). Shifting of agro‐climatic zones, their drought vulnerability, and precipitation and temperature trends in Pakistan. International Journal of Climatology, 37, 529-543.
  • Agyei, S. K., Isshaq, Z., Frimpong, S., Adam, A. M., Bossman, A., & Asiamah, O. (2021). COVID‐19 and food prices in sub‐Saharan Africa. African Development Review, 33, S102-S113.
  • Algieri, B., & Leccadito, A. (2017). Assessing contagion risk from energy and non-energy commodity markets. Energy Economics, 62, 312-322.
  • Allen, D. E., Chang, C., McAleer, M., & Singh, A. K. (2018). A cointegration analysis of agricultural, energy and bio-fuel spot, and futures prices. Applied Economics, 50(7), 804-823.
  • Al-Maadid, A., Caporale, G. M., Spagnolo, F., & Spagnolo, N. (2017). Spillovers between food and energy prices and structural breaks. International Economics, 150, 1-18.
  • Amrouk, E. M., Grosche, S. C., & Heckelei, T. (2020). Interdependence between cash crop and staple food international prices across periods of varying financial market stress. Applied Economics, 52(4), 345-360.
  • Anscombe, Francis J., & William J. Glynn. (1983). Distribution of the Kurtosis Statistic B2 For Normal Samples. Biometrika 70: 227–34.
  • Antonakakis, N., & Gabauer, D. (2017). Refined Measures of Dynamic Connectedness based on TVP-VAR. In MPRA Paper (No. 78282; MPRA Paper). University Library of Munich, Germany.
  • Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/JRFM13040084
  • Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The quarterly journal of economics, 131(4), 1593-1636.
  • Bradbear, C., & Friel, S. (2013). Integrating climate change, food prices and population health. Food Policy, 43, 56-66.
  • Brown, M. E., & Kshirsagar, V. (2015). Weather and international price shocks on food prices in the developing world. Global Environmental Change, 35, 31-40.
  • Chandio AA, Gokmenoglu KK, Ahmad F (2021a) Addressing the long-and short-run efects of climate change on major food crops production in Turkey. Environ Sci Pollut Res 1–17.
  • Chandio AA, Gokmenoglu KK, Ahmad M, Jiang Y (2021b) Towards sustainable rice production in Asia: the role of climatic factors. Earth Syst Environ 1–14.
  • Chandio AA, Magsi H, Ozturk I (2020) Examining the efects of climate change on rice production: case study of Pakistan. Environ Sci Pollut Res 27(8):7812–7822.
  • D’Agostino, Ralph B. (1970). Transformation To Normality of the Null Distribution of G1. Biometrika 57: 679–81.
  • Deaton, B. J., & Deaton, B. J. (2020). Food security and Canada's agricultural system challenged by COVID‐19. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 68(2), 143-149.
  • Diebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/J.1468-0297.2008.02208.X
  • Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/J.IJFORECAST.2011.02.006
  • Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/J.JECONOM.2014.04.012
  • Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient Tests for An Autoregresive Unit Root. Econometrica, 64(4), 813–836.
  • FAO (2008). Climate change: Implications for food safety. http://www.fao.org/3/i0195e/i0195e00.htm.
  • Fisher, T. J., & Gallagher, C. M. (2012). New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing. Journal of the American Statistical Association, 107, 777–787. https://doi.org/10.1080/01621459.2012.688465
  • Frimpong, S., Gyamfi, E. N., Ishaq, Z., Kwaku Agyei, S., Agyapong, D., & Adam, A. M. (2021). Can global economic policy uncertainty drive the interdependence of agricultural commodity prices? Evidence from partial wavelet coherence analysis. Complexity, 2021, 1-13.
  • Gabauer, D., & Gupta, R. (2018). On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters, 171, 63–71. https://doi.org/10.1016/J.ECONLET.2018.07.007
  • Gavriilidis, K. (2021). Measuring climate policy uncertainty. Available at SSRN 3847388.
  • Gomez-Zavaglia, A., Mejuto, J. C., & Simal-Gandara, J. (2020). Mitigation of emerging implications of climate change on food production systems. Food Research International, 134, 109256.
  • Haq, Z. U., Nazli, H., & Meilke, K. (2008). Implications of high food prices for poverty in Pakistan. Agricultural Economics, 39, 477-484.
  • He, L. Y., & Chen, S. P. (2011). Multifractal detrended cross-correlation analysis of agricultural futures markets. Chaos, Solitons & Fractals, 44(6), 355-361.
  • IPCC (2018) 18. Summary for policymakers. In: Masson-Delmotte, V., Zhai, P., P ̈ortner, H.- O., Roberts, D., Skea, J., Shukla, P.R., Pirani, A., Moufouma-Okia, W., P ́ean, C., Pidcock, R., Connors, S., Matthews, J.B.R., Chen, Y., Zhou, X., Gomis, M.I., Lonnoy, E., Maycock, T., Tignor, M., Waterfield, T. (Eds.), Global Warming of 1.5◦ C. An IPCC Special Report on the Impacts of Global Warming of 1.5◦ C Above Pre- industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and efforts to eradicate poverty. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 3–24.
  • IPCC, (2022). In: P ̈ortner, H.-O., Roberts, D.C., Tignor, M., Poloczanska, E.S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., L ̈oschke, S., M ̈oller, V., Okem, A., Rama, B. (Eds.), Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
  • Islam, M. S., Okubo, K., Islam, A. H. M. S., & Sato, M. (2022). Investigating the effect of climate change on food loss and food security in Bangladesh. SN Business & Economics, 2, 1-24.
  • Janetos, A., Justice, C., Jahn, M., Obersteiner, M., Glauber, J., & Mulhern, W. (2017). The risks of multiple breadbasket failures in the 21st century: a science research agenda.
  • Jarque, Carlos M., and Anil K. Bera. 1980. Efficient Tests For Normality, Homoscedasticity and Serial Independence of Regression Residuals. Economics Letters 6: 255–59.
  • Ke, Y., Li, C., McKenzie, A. M., & Liu, P. (2019). Risk Transmission between Chinese and US agricultural commodity futures markets—A CoVaR approach. Sustainability, 11(1), 239.
  • Kim, W., Iizumi, T., & Nishimori, M. (2019). Global patterns of crop production losses associated with droughts from 1983 to 2009. Journal of Applied Meteorology and Climatology, 58(6), 1233-1244.
  • Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101–116. https://doi.org/10.1016/J.EUROECOREV.2014.07.002
  • Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4
  • Korobilis, D., & Yilmaz, K. (2018). Measuring Dynamic Connectedness with Large Bayesian VAR Models. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3099725
  • Laborde, D., Mamun, A., Martin, W., Piñeiro, V., & Vos, R. (2021). Agricultural subsidies and global greenhouse gas emissions. Nature communications, 12(1), 2601
  • Lobell, D. B., Hammer, G. L., McLean, G., Messina, C., Roberts, M. J., & Schlenker, W. (2013). The critical role of extreme heat for maize production in the United States. Nature climate change, 3(5), 497-501.
  • Misiou, O., & Koutsoumanis, K. (2022). Climate change and its implications for food safety and spoilage. Trends in Food Science & Technology, 126, 142-152.
  • Mittenzwei, K., Persson, T., Höglind, M., & Kværnø, S. (2017). Combined effects of climate change and policy uncertainty on the agricultural sector in Norway. Agricultural Systems, 153, 118-126.
  • Nawaz Z, Li X, Chen Y, Guo Y, Wang X, Nawaz N (2019) Temporal and spatial characteristics of precipitation and temperature in Punjab, Pakistan. Water 11(9):1–23.
  • Nazlioglu, S., & Soytas, U. (2012). Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics, 34(4), 1098-1104.
  • Nelson, G. C., Valin, H., Sands, R. D., Havlík, P., Ahammad, H., Deryng, D., ... & Willenbockel, D. (2014). Climate change effects on agriculture: Economic responses to biophysical shocks. Proceedings of the National Academy of Sciences, 111(9), 3274-3279.
  • Oecd, F. A. O. (2018). OECD-FAO Agricultural Outlook 2022-2031.
  • Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0
  • Ren, F. R., Tian, Z., Chen, H. S., & Shen, Y. T. (2021). Energy consumption, CO2 emissions, and agricultural disaster efficiency evaluation of China based on the two-stage dynamic DEA method. Environmental Science and Pollution Research, 28, 1901-1918.
  • Stephens, E. C., & Barrett, C. B. (2011). Incomplete credit markets and commodity marketing behaviour. Journal of agricultural economics, 62(1), 1-24.
  • Tang, K., & Xiong, W. (2012). Index investment and the financialization of commodities. Financial Analysts Journal, 68(6), 54-74.
  • Trostle, R. (2011). Why have food commodity prices risen again?. Diane Publishing.
  • Ullah S (2017) Climate change impact on agriculture of Pakistan-a leading agent to food security. Int J Environ Sci Nat Res 6(3):1–4.
  • Ullah, A., Khan, D., Khan, I., & Zheng, S. (2018). Does agricultural ecosystem cause environmental pollution in Pakistan? Promise and menace. Environmental Science and Pollution Research, 25, 13938-13955.
  • Umar, Z., Jareño, F., & Escribano, A. (2022). Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era. Applied Economics, 54(9), 1030-1054.
  • Wang, K. H., Kan, J. M., Qiu, L., & Xu, S. (2023). Climate policy uncertainty, oil price and agricultural commodity: From quantile and time perspective. Economic Analysis and Policy.
  • Webb, P. (2010). Medium-to long-run implications of high food prices for global nutrition. The Journal of nutrition, 140(1), 143S-147S.
  • World Bank,. (2007). "World Development Report 2008: Agriculture for Development Washington."
  • Yahya, M., Oglend, A., & Dahl, R. E. (2019). Temporal and spectral dependence between crude oil and agricultural commodities: A wavelet-based copula approach. Energy Economics, 80, 277-296.
  • Yip, P. S., Brooks, R., Do, H. X., & Nguyen, D. K. (2020). Dynamic volatility spillover effects between oil and agricultural products. International Review of Financial Analysis, 69, 101465.

RELATIONSHIP BETWEEN CLIMATE POLICY UNCERTAINTY AND AGRICULTURE AND FOOD MARKET INDICES: TVP VAR APPROACH

Yıl 2025, Cilt: 13 Sayı: 1, 46 - 64, 30.06.2025
https://doi.org/10.52122/nisantasisbd.1626552

Öz

Climate change significantly affects the availability, accessibility, quality and stability of food in the world. Climate change has the power to affect relevant companies, investors and policy-makers by putting pressure on agricultural production and practices. In this regard, the main purpose of this paper examines the dynamic connectivity nexus between the Climate Policy Uncertainty Index (CPU), FTSE 350 Food Producers Index (FTSE 350), S&P Commodity Producers Agriculture Net Return Index (S&P Commodity), FAO Food Price Index (FAO) and DAX Global Agricultural Index (DAX). In the paper time-varying parameter vector autoregressive (TVP-VAR) model was used in period of July 2007 to July 2022. It was observed that the FTSE 350 index spreads strong volatility to the CPU, S&P Commodity index and DAX index. In addition, it has been determined that S&P Commodity and DAX index emit weak volatility due to climate policy uncertainty.

Kaynakça

  • Abbas, S., Kousar, S., & Khan, M. S. (2022). The role of climate change in food security; empirical evidence over Punjab regions, Pakistan. Environmental Science and Pollution Research, 29(35), 53718-53736.
  • Adekoya, O. B., & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. Resources Policy, 70, 101898.
  • Adnan, S., Ullah, K., Gao, S., Khosa, A. H., & Wang, Z. (2017). Shifting of agro‐climatic zones, their drought vulnerability, and precipitation and temperature trends in Pakistan. International Journal of Climatology, 37, 529-543.
  • Agyei, S. K., Isshaq, Z., Frimpong, S., Adam, A. M., Bossman, A., & Asiamah, O. (2021). COVID‐19 and food prices in sub‐Saharan Africa. African Development Review, 33, S102-S113.
  • Algieri, B., & Leccadito, A. (2017). Assessing contagion risk from energy and non-energy commodity markets. Energy Economics, 62, 312-322.
  • Allen, D. E., Chang, C., McAleer, M., & Singh, A. K. (2018). A cointegration analysis of agricultural, energy and bio-fuel spot, and futures prices. Applied Economics, 50(7), 804-823.
  • Al-Maadid, A., Caporale, G. M., Spagnolo, F., & Spagnolo, N. (2017). Spillovers between food and energy prices and structural breaks. International Economics, 150, 1-18.
  • Amrouk, E. M., Grosche, S. C., & Heckelei, T. (2020). Interdependence between cash crop and staple food international prices across periods of varying financial market stress. Applied Economics, 52(4), 345-360.
  • Anscombe, Francis J., & William J. Glynn. (1983). Distribution of the Kurtosis Statistic B2 For Normal Samples. Biometrika 70: 227–34.
  • Antonakakis, N., & Gabauer, D. (2017). Refined Measures of Dynamic Connectedness based on TVP-VAR. In MPRA Paper (No. 78282; MPRA Paper). University Library of Munich, Germany.
  • Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/JRFM13040084
  • Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The quarterly journal of economics, 131(4), 1593-1636.
  • Bradbear, C., & Friel, S. (2013). Integrating climate change, food prices and population health. Food Policy, 43, 56-66.
  • Brown, M. E., & Kshirsagar, V. (2015). Weather and international price shocks on food prices in the developing world. Global Environmental Change, 35, 31-40.
  • Chandio AA, Gokmenoglu KK, Ahmad F (2021a) Addressing the long-and short-run efects of climate change on major food crops production in Turkey. Environ Sci Pollut Res 1–17.
  • Chandio AA, Gokmenoglu KK, Ahmad M, Jiang Y (2021b) Towards sustainable rice production in Asia: the role of climatic factors. Earth Syst Environ 1–14.
  • Chandio AA, Magsi H, Ozturk I (2020) Examining the efects of climate change on rice production: case study of Pakistan. Environ Sci Pollut Res 27(8):7812–7822.
  • D’Agostino, Ralph B. (1970). Transformation To Normality of the Null Distribution of G1. Biometrika 57: 679–81.
  • Deaton, B. J., & Deaton, B. J. (2020). Food security and Canada's agricultural system challenged by COVID‐19. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 68(2), 143-149.
  • Diebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/J.1468-0297.2008.02208.X
  • Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/J.IJFORECAST.2011.02.006
  • Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/J.JECONOM.2014.04.012
  • Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient Tests for An Autoregresive Unit Root. Econometrica, 64(4), 813–836.
  • FAO (2008). Climate change: Implications for food safety. http://www.fao.org/3/i0195e/i0195e00.htm.
  • Fisher, T. J., & Gallagher, C. M. (2012). New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing. Journal of the American Statistical Association, 107, 777–787. https://doi.org/10.1080/01621459.2012.688465
  • Frimpong, S., Gyamfi, E. N., Ishaq, Z., Kwaku Agyei, S., Agyapong, D., & Adam, A. M. (2021). Can global economic policy uncertainty drive the interdependence of agricultural commodity prices? Evidence from partial wavelet coherence analysis. Complexity, 2021, 1-13.
  • Gabauer, D., & Gupta, R. (2018). On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters, 171, 63–71. https://doi.org/10.1016/J.ECONLET.2018.07.007
  • Gavriilidis, K. (2021). Measuring climate policy uncertainty. Available at SSRN 3847388.
  • Gomez-Zavaglia, A., Mejuto, J. C., & Simal-Gandara, J. (2020). Mitigation of emerging implications of climate change on food production systems. Food Research International, 134, 109256.
  • Haq, Z. U., Nazli, H., & Meilke, K. (2008). Implications of high food prices for poverty in Pakistan. Agricultural Economics, 39, 477-484.
  • He, L. Y., & Chen, S. P. (2011). Multifractal detrended cross-correlation analysis of agricultural futures markets. Chaos, Solitons & Fractals, 44(6), 355-361.
  • IPCC (2018) 18. Summary for policymakers. In: Masson-Delmotte, V., Zhai, P., P ̈ortner, H.- O., Roberts, D., Skea, J., Shukla, P.R., Pirani, A., Moufouma-Okia, W., P ́ean, C., Pidcock, R., Connors, S., Matthews, J.B.R., Chen, Y., Zhou, X., Gomis, M.I., Lonnoy, E., Maycock, T., Tignor, M., Waterfield, T. (Eds.), Global Warming of 1.5◦ C. An IPCC Special Report on the Impacts of Global Warming of 1.5◦ C Above Pre- industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and efforts to eradicate poverty. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 3–24.
  • IPCC, (2022). In: P ̈ortner, H.-O., Roberts, D.C., Tignor, M., Poloczanska, E.S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., L ̈oschke, S., M ̈oller, V., Okem, A., Rama, B. (Eds.), Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
  • Islam, M. S., Okubo, K., Islam, A. H. M. S., & Sato, M. (2022). Investigating the effect of climate change on food loss and food security in Bangladesh. SN Business & Economics, 2, 1-24.
  • Janetos, A., Justice, C., Jahn, M., Obersteiner, M., Glauber, J., & Mulhern, W. (2017). The risks of multiple breadbasket failures in the 21st century: a science research agenda.
  • Jarque, Carlos M., and Anil K. Bera. 1980. Efficient Tests For Normality, Homoscedasticity and Serial Independence of Regression Residuals. Economics Letters 6: 255–59.
  • Ke, Y., Li, C., McKenzie, A. M., & Liu, P. (2019). Risk Transmission between Chinese and US agricultural commodity futures markets—A CoVaR approach. Sustainability, 11(1), 239.
  • Kim, W., Iizumi, T., & Nishimori, M. (2019). Global patterns of crop production losses associated with droughts from 1983 to 2009. Journal of Applied Meteorology and Climatology, 58(6), 1233-1244.
  • Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101–116. https://doi.org/10.1016/J.EUROECOREV.2014.07.002
  • Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4
  • Korobilis, D., & Yilmaz, K. (2018). Measuring Dynamic Connectedness with Large Bayesian VAR Models. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3099725
  • Laborde, D., Mamun, A., Martin, W., Piñeiro, V., & Vos, R. (2021). Agricultural subsidies and global greenhouse gas emissions. Nature communications, 12(1), 2601
  • Lobell, D. B., Hammer, G. L., McLean, G., Messina, C., Roberts, M. J., & Schlenker, W. (2013). The critical role of extreme heat for maize production in the United States. Nature climate change, 3(5), 497-501.
  • Misiou, O., & Koutsoumanis, K. (2022). Climate change and its implications for food safety and spoilage. Trends in Food Science & Technology, 126, 142-152.
  • Mittenzwei, K., Persson, T., Höglind, M., & Kværnø, S. (2017). Combined effects of climate change and policy uncertainty on the agricultural sector in Norway. Agricultural Systems, 153, 118-126.
  • Nawaz Z, Li X, Chen Y, Guo Y, Wang X, Nawaz N (2019) Temporal and spatial characteristics of precipitation and temperature in Punjab, Pakistan. Water 11(9):1–23.
  • Nazlioglu, S., & Soytas, U. (2012). Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics, 34(4), 1098-1104.
  • Nelson, G. C., Valin, H., Sands, R. D., Havlík, P., Ahammad, H., Deryng, D., ... & Willenbockel, D. (2014). Climate change effects on agriculture: Economic responses to biophysical shocks. Proceedings of the National Academy of Sciences, 111(9), 3274-3279.
  • Oecd, F. A. O. (2018). OECD-FAO Agricultural Outlook 2022-2031.
  • Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0
  • Ren, F. R., Tian, Z., Chen, H. S., & Shen, Y. T. (2021). Energy consumption, CO2 emissions, and agricultural disaster efficiency evaluation of China based on the two-stage dynamic DEA method. Environmental Science and Pollution Research, 28, 1901-1918.
  • Stephens, E. C., & Barrett, C. B. (2011). Incomplete credit markets and commodity marketing behaviour. Journal of agricultural economics, 62(1), 1-24.
  • Tang, K., & Xiong, W. (2012). Index investment and the financialization of commodities. Financial Analysts Journal, 68(6), 54-74.
  • Trostle, R. (2011). Why have food commodity prices risen again?. Diane Publishing.
  • Ullah S (2017) Climate change impact on agriculture of Pakistan-a leading agent to food security. Int J Environ Sci Nat Res 6(3):1–4.
  • Ullah, A., Khan, D., Khan, I., & Zheng, S. (2018). Does agricultural ecosystem cause environmental pollution in Pakistan? Promise and menace. Environmental Science and Pollution Research, 25, 13938-13955.
  • Umar, Z., Jareño, F., & Escribano, A. (2022). Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era. Applied Economics, 54(9), 1030-1054.
  • Wang, K. H., Kan, J. M., Qiu, L., & Xu, S. (2023). Climate policy uncertainty, oil price and agricultural commodity: From quantile and time perspective. Economic Analysis and Policy.
  • Webb, P. (2010). Medium-to long-run implications of high food prices for global nutrition. The Journal of nutrition, 140(1), 143S-147S.
  • World Bank,. (2007). "World Development Report 2008: Agriculture for Development Washington."
  • Yahya, M., Oglend, A., & Dahl, R. E. (2019). Temporal and spectral dependence between crude oil and agricultural commodities: A wavelet-based copula approach. Energy Economics, 80, 277-296.
  • Yip, P. S., Brooks, R., Do, H. X., & Nguyen, D. K. (2020). Dynamic volatility spillover effects between oil and agricultural products. International Review of Financial Analysis, 69, 101465.
Toplam 62 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Uluslararası Finans
Bölüm Makaleler
Yazarlar

Samet Gürsoy 0000-0003-1020-7438

Mesut Doğan 0000-0001-6879-1361

Feyyaz Zeren 0000-0003-0163-5916

İbrahim Ekşi 0009-0002-4129-8168

Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 24 Ocak 2025
Kabul Tarihi 10 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 13 Sayı: 1

Kaynak Göster

APA Gürsoy, S., Doğan, M., Zeren, F., Ekşi, İ. (2025). RELATIONSHIP BETWEEN CLIMATE POLICY UNCERTAINTY AND AGRICULTURE AND FOOD MARKET INDICES: TVP VAR APPROACH. Nişantaşı Üniversitesi Sosyal Bilimler Dergisi, 13(1), 46-64. https://doi.org/10.52122/nisantasisbd.1626552

Nişantaşı Üniversitesi kurumsal yayınıdır.