The impact of geopolitical risk on the international agricultural market: Empirical analysis based on the GJR-GARCH-MIDAS model
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- Christian Conrad & Melanie Schienle, 2020.
"Testing for an Omitted Multiplicative Long-Term Component in GARCH Models,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 229-242, April.
- Conrad, Christian & Schienle, Melanie, 2019. "Testing for an omitted multiplicative long-term component in GARCH models," Working Paper Series in Economics 121, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
- Yue-Hua Dai & Wen-Jie Xie & Zhi-Qiang Jiang & George J. Jiang & Wei-Xing Zhou, 2016.
"Correlation structure and principal components in the global crude oil market,"
Empirical Economics, Springer, vol. 51(4), pages 1501-1519, December.
- Yue-Hua Dai & Wen-Jie Xie & Zhi-Qiang Jiang & George J. Jiang & Wei-Xing Zhou, 2014. "Correlation structure and principal components in global crude oil market," Papers 1405.5000, arXiv.org.
- Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
- Dai, Peng-Fei & Xiong, Xiong & Zhou, Wei-Xing, 2021.
"A global economic policy uncertainty index from principal component analysis,"
Finance Research Letters, Elsevier, vol. 40(C).
- Peng-Fei Dai & Xiong Xiong & Wei-Xing Zhou, 2019. "A global economic policy uncertainty index from principal component analysis," Papers 1907.05049, arXiv.org, revised Jul 2019.
- Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020.
"Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection,"
Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
- Tong Fang & Tae-Hwy Lee & Zhi Su, 2020. "Predicting the Long-term Stock Market Volatility: A GARCH-MIDAS Model with Variable Selection," Working Papers 202009, University of California at Riverside, Department of Economics.
- Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
- Zhou, Wei-Xing & Dai, Yun-Shi & Duong, Kiet Tuan & Dai, Peng-Fei, 2024.
"The impact of the Russia-Ukraine conflict on the extreme risk spillovers between agricultural futures and spots,"
Journal of Economic Behavior & Organization, Elsevier, vol. 217(C), pages 91-111.
- Wei-Xing Zhou & Yun-Shi Dai & Kiet Tuan Duong & Peng-Fei Dai, 2023. "The impact of the Russia-Ukraine conflict on the extreme risk spillovers between agricultural futures and spots," Papers 2310.16850, arXiv.org.
- Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006.
"Predicting volatility: getting the most out of return data sampled at different frequencies,"
Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," NBER Working Papers 10914, National Bureau of Economic Research, Inc.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
- Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
- Wang, Lu & Wu, Rui & Ma, WeiChun & Xu, Weiju, 2023. "Examining the volatility of soybean market in the MIDAS framework: The importance of bagging-based weather information," International Review of Financial Analysis, Elsevier, vol. 89(C).
- Walther, Thomas & Klein, Tony & Bouri, Elie, 2019.
"Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting,"
Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
- Walther, Thomas & Klein, Tony & Bouri, Elie, 2018. "Exogenous Drivers of Bitcoin and Cryptocurrency Volatility – A Mixed Data Sampling Approach to Forecasting," QBS Working Paper Series 2018/02, Queen's University Belfast, Queen's Business School.
- Zhang, Jiaming & Xiang, Yitian & Zou, Yang & Guo, Songlin, 2024. "Volatility forecasting of Chinese energy market: Which uncertainty have better performance?," International Review of Financial Analysis, Elsevier, vol. 91(C).
- Benoit Mandelbrot, 1967. "The Variation of Some Other Speculative Prices," The Journal of Business, University of Chicago Press, vol. 40, pages 393-393.
- Christian Conrad & Karin Loch, 2015.
"Anticipating Long‐Term Stock Market Volatility,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1090-1114, November.
- Conrad, Christian & Loch, Karin, 2012. "Anticipating Long-Term Stock Market Volatility," Working Papers 0535, University of Heidelberg, Department of Economics.
- Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022.
"Energy Markets and Global Economic Conditions,"
The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
- Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2020. "Energy Markets and Global Economic Conditions," Working Papers 2020_08, Business School - Economics, University of Glasgow.
- Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2020. "Energy Markets and Global Economic Conditions," NBER Working Papers 27001, National Bureau of Economic Research, Inc.
- Baumeister, Christiane & Korobilis, Dimitris & Lee, Thomas K., 2020. "Energy Markets and Global Economic Conditions," CEPR Discussion Papers 14580, C.E.P.R. Discussion Papers.
- Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2020. "Energy Markets and Global Economic Conditions," CESifo Working Paper Series 8282, CESifo.
- Zhiyuan Pan & Ruijun Bu & Li Liu & Yudong Wang, 2020. "Macroeconomic fundamentals, jump dynamics and expected volatility," Quantitative Finance, Taylor & Francis Journals, vol. 20(8), pages 1345-1371, August.
- Libing Fang & Baizhu Chen & Honghai Yu & Yichuo Qian, 2018. "The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 413-422, March.
- Su, Zhi & Fang, Tong & Yin, Libo, 2017. "The role of news-based implied volatility among US financial markets," Economics Letters, Elsevier, vol. 157(C), pages 24-27.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005.
"There is a risk-return trade-off after all,"
Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2003. "There is a Risk-Return Tradeoff After All," CIRANO Working Papers 2003s-26, CIRANO.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "There is a Risk-Return Tradeoff After All," CIRANO Working Papers 2004s-24, CIRANO.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "There is a Risk-Return Tradeoff After All," NBER Working Papers 10913, National Bureau of Economic Research, Inc.
- Chao Liang & Yaojie Zhang & Xiafei Li & Feng Ma, 2022. "Which predictor is more predictive for Bitcoin volatility? And why?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1947-1961, April.
- Lorente, Daniel Balsalobre & Mohammed, Kamel Si & Cifuentes-Faura, Javier & Shahzad, Umer, 2023. "Dynamic connectedness among climate change index, green financial assets and renewable energy markets: Novel evidence from sustainable development perspective," Renewable Energy, Elsevier, vol. 204(C), pages 94-105.
- Segnon, Mawuli & Gupta, Rangan & Wilfling, Bernd, 2024.
"Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks,"
International Journal of Forecasting, Elsevier, vol. 40(1), pages 29-43.
- Mawuli Segnon & Rangan Gupta & Bernd Wilfling, 2022. "Forecasting Stock Market Volatility with Regime-Switching GARCH-MIDAS: The Role of Geopolitical Risks," Working Papers 202203, University of Pretoria, Department of Economics.
- Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993.
"On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,"
Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
- Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis.
- Mo, Di & Gupta, Rakesh & Li, Bin & Singh, Tarlok, 2018. "The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets," Economic Modelling, Elsevier, vol. 70(C), pages 543-560.
- Dai, Yun-Shi & Dai, Peng-Fei & Zhou, Wei-Xing, 2023.
"Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets,"
Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
- Yun-Shi Dai & Peng-Fei Dai & Wei-Xing Zhou, 2023. "Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets," Papers 2303.11030, arXiv.org.
- Li, Dongxin & Zhang, Li & Li, Lihong, 2023. "Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model," International Review of Financial Analysis, Elsevier, vol. 88(C).
- Raza, Syed Ali & Masood, Amna & Benkraiem, Ramzi & Urom, Christian, 2023.
"Forecasting the volatility of precious metals prices with global economic policy uncertainty in pre and during the COVID-19 period: Novel evidence from the GARCH-MIDAS approach,"
Energy Economics, Elsevier, vol. 120(C).
- Syed Ali Raza & Amna Masood & Ramzi Benkraiem & Christian Urom, 2023. "Forecasting the volatility of precious metals prices with global economic policy uncertainty in pre and during the COVID-19 period: Novel evidence from the GARCH-MIDAS approach," Post-Print hal-04080872, HAL.
- Haohua Li & Elie Bouri & Rangan Gupta & Libing Fang, 2023. "Return Volatility, Correlation, and Hedging of Green and Brown Stocks: Is there a Role for Climate Risk Factors?," Working Papers 202301, University of Pretoria, Department of Economics.
- Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
- Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
- Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
- Benoit Mandelbrot, 2015.
"The Variation of Certain Speculative Prices,"
World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78,
World Scientific Publishing Co. Pte. Ltd..
- Benoit Mandelbrot, 1963. "The Variation of Certain Speculative Prices," The Journal of Business, University of Chicago Press, vol. 36, pages 394-394.
- Dai, Peng-Fei & Xiong, Xiong & Zhang, Jin & Zhou, Wei-Xing, 2022.
"The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model,"
Resources Policy, Elsevier, vol. 78(C).
- Peng-Fei Dai & Xiong Xiong & Wei-Xing Zhou, 2020. "The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model," Papers 2007.12838, arXiv.org.
- Fu, Fangjian, 2009. "Idiosyncratic risk and the cross-section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 91(1), pages 24-37, January.
- Xiafei Li & Yu Wei & Xiaodan Chen & Feng Ma & Chao Liang & Wang Chen, 2022. "Which uncertainty is powerful to forecast crude oil market volatility? New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4279-4297, October.
- Liu, Yang & Han, Liyan & Xu, Yang, 2021. "The impact of geopolitical uncertainty on energy volatility," International Review of Financial Analysis, Elsevier, vol. 75(C).
- Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
- Gong, Xu & Xu, Jun, 2022. "Geopolitical risk and dynamic connectedness between commodity markets," Energy Economics, Elsevier, vol. 110(C).
- Abid, Ilyes & Dhaoui, Abderrazak & Kaabia, Olfa & Tarchella, Salma, 2023. "Geopolitical risk on energy, agriculture, livestock, precious and industrial metals: New insights from a Markov Switching model," Resources Policy, Elsevier, vol. 85(PA).
- Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
- Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
- Chao Liang & Feng Ma & Lu Wang & Qing Zeng, 2021. "The information content of uncertainty indices for natural gas futures volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1310-1324, November.
- Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
- Mark J. Flannery & Aris A. Protopapadakis, 2002. "Macroeconomic Factors Do Influence Aggregate Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 15(3), pages 751-782.
- Conrad, Christian & Loch, Karin & Rittler, Daniel, 2014. "On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 26-40.
- 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.
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This paper has been announced in the following NEP Reports:- NEP-AGR-2024-04-29 (Agricultural Economics)
- NEP-INT-2024-04-29 (International Trade)
- NEP-MAC-2024-04-29 (Macroeconomics)
- NEP-RMG-2024-04-29 (Risk Management)
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