This study uses a Time-Varying Parameter Stochastic Volatility Vector Autoregression (TVP-SV-VAR) model to conduct an empirical analysis of the dynamic effects of China’s stock market volatility on the agricultural loan market and its channels. The results show that the relationship between stock market and agricultural loan market volatility is time varying and is always positive. The investor sentiment is a major conduit through which the effect takes place. This time-varying effect and transmission mechanism are most apparent between 2011 and 2017 and have since waned and stabilized. These have significant implications for the stable and orderly development of the agricultural loan market, highlighting the importance of the sound financial market system and timely policy, better market monitoring and early warning system and the formation of a mature and sound agricultural credit mechanism.

1. Aastveit, K. A., Furlanetto, F., & Loria, F. (2023). Has the Fed responded to house and stock prices? A time-varying analysis. Review of Economics and Statistics, 105(5), 1314-1324. doi.org/10.1162/rest_a_01120.
2. Akay, E., & Hirshleifer, D. (2021). Social finance as cultural evolution, transmission bias, and market dynamics. Proceedings of the National Academy of Sciences, 118. doi.org/10.1073/pnas.2015568118
3. Albulescu, C. T. (2021). COVID-19 and the United States financial markets’ volatility. Finance research letters, 38, 101699. doi.org/10.1016/j.frl.2020.101699.
4. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645-1680. doi.org/10.1111/j.1540-6261.2006.00885.x
5. Barry, P. J., & Lee, W. F. (1983). Financial stress in agriculture: Implications for agricultural lenders. American Journal of Agricultural Economics, 65(5), 945-952. doi.org/10.2307/1240396.
6. Barry, P. J., Baker, C. B., & Sanint, L. R. (1981). Farmers' credit risks and liquidity management. American Journal of Agricultural Economics, 63(2), 216-227. doi.org/10.2307/1239557.
7. Bennani, H. (2019). Does People's Bank of China communication matter? Evidence from stock market reaction. Emerging Markets Review, 40, 100617. doi.org/10.1016/j.ememar.2019.05.002.
8. Bernanke, B. & Blinder, A. (1988). Credit, Money, and Aggregate Demand. American Economic Review, 78, 435-439. DOI 10.3386/w2534.
9. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Eeri Research Paper, 31(3), 307-327. doi.org/10.1016/0304-4076(86)90063-1
10. Bourveau, T., & Schoenfeld, J. (2017). Shareholder activism and voluntary disclosure. Review of Accounting Studies, 22, 1307-1339. doi.org/10.1007/s11142-017-9408-0.
11. Breitenlechner, M., & Nuutilainen, R. (2023). China’s Monetary Policy and the Loan Market: How Strong is the Credit Channel in China?. Open Economies Review, 34(3), 555-577. doi.org/10.1007/s11079-022-09705-2.Clapp, J., Isakson, S. R., & Visser, O. (2017). The complex dynamics of agriculture as a financial asset: Introduction to symposium. Agriculture and Human Values, 34, 179-183. doi.org/10.1007/s10460-016-9682-7.
12. Corbet, S., Hou, Y. G., Hu, Y., Oxley, L., & Xu, D. (2021). Pandemic-related financial market volatility spillovers: Evidence from the Chinese COVID-19 epicentre. International Review of Economics & Finance, 71, 55-81. doi.org/10.1016/j.iref.2020.06.022.
13. Cui, H., & Zhao, H. (2023). Economic policy uncertainty, entrepreneur confidence, and export trade: An empirical analysis based on the TVP-SV-VAR model. Research on Technology Economics and Management, (10), 94-99. doi.org/10.3969/j.issn.1004-6033.2023.10.018.
14. Drabenstott, M., & Heffernan, P. (1984). Financial futures: a useful tool for transferring interest rate risk away from farm borrowers or lenders? American journal of agricultural economics, 66(5), 614-619. doi.org/10.2307/1240964.
15. Du, X., Cheng, J., Zhu, D., & Xing, M. (2023). Does central bank communication on financial stability work?——An empirical study based on Chinese stock market. International Review of Economics & Finance, 85, 390-407. doi.org/10.1016/j.iref.2023.02.003
16. Fang, T., & Su, Z. (2021). Does uncertainty matter for US financial market volatility spillovers? Empirical evidence from a nonlinear Granger causality network. Applied Economics Letters, 28(21), 1877-1883. doi.org/10.1080/13504851.2020.1854656
17. Fang, Z., Ni, Y., & Zhuang, J. (2011). The impact of monetary policy shocks on stock market liquidity: An empirical study based on the Markov regime-switching VAR model. Financial Research, (07), 43-56. doi.org/10.3969/j.issn.1001-1070.2011.07.006
18. Fernández-Amador, O., Gächter, M., Larch, M., & Peter, G. (2013). Does monetary policy determine stock market liquidity? New evidence from the euro zone. Journal of Empirical Finance, 21, 54-68. doi.org/10.1016/j.jempfin.2012.12.008
19. Fischer, S., & Merton, R. C. (1984). Macroeconomics and finance: The role of the stock market. In Carnegie-Rochester conference series on public policy (Vol. 21, pp. 57-108). North-Holland. doi.org/10.1016/0167-2231(84)90005-8
20. Gao, Z., & Liang, X. (2023). Research on the impact of investor sentiment on stock market returns based on VAR and EGARCH models. Journal of Zhejiang University (Science Edition), (04), 434-441+454. doi.org/10.3969/j.issn.1673-5650.2023.04.007
21. Ghosh, S. (2020). Bank lending and monetary transmission: Does politics matter? Journal of Quantitative Economics, 18(2), 359-381. doi.org/10.1007/s40953-019-00190-y
22. Giglio, S., Maggiori, M., Stroebel, J., & Utkus, S. (2020). Inside the mind of a stock market crash (No. w27272). National Bureau of Economic Research. DOI 10.3386/w27272
23. Gu, Y., & Wang, Y. (2023). Can macroprudential tools manage RMB exchange rate expectations? An empirical examination based on the TVP-SV-VAR model. International Finance Research, 06, 47-59. doi.org/10.16475/j.cnki.1006-1029.2023.06.008
24. Guo, W., Lu, L., & Zhong, Y. (2024). How does investor sentiment affect stock market bubbles?—With suggestions on regulating stock market bubbles. Financial Regulation Research, 01, 61-78. doi.org/10.13490/j.cnki.frr.2024.01.004
25. Haitsma, R., Unalmis, D., & De Haan, J. (2016). The impact of the ECB's conventional and unconventional monetary policies on stock markets. Journal of Macroeconomics, 48, 101-116. doi.org/10.1016/j.jmacro.2016.02.004
26. Hayo, B., & Neuenkirch, M. (2015). Self-monitoring or reliance on media reporting: How do financial market participants process central bank news? Journal of Banking & Finance, 59, 27-37. doi.org/10.1016/j.jbankfin.2015.06.004
27. He, Y., Liu, S., & Jiang, H. (2024). Investor structure and stock market fluctuations: a quantitative analysis. Applied Economics Letters, 1-5. doi.org/10.1080/13504851.2023.2300958
28. Hu, Y. (2018). Research on the transmission path of monetary policy through the stock market (Doctoral dissertation, Northwestern Polytechnical University). https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2022&filename=1020706855.nh
29. Huang, D. S., Jiang, F. W., Tu, J., & Zhou, G. F. (2015). Investor sentiment aligned: A powerful predictor of stock returns. Review of Financial Studies, 28(3), 791-837. doi.org/10.1093/rfs/hhu080
30. Huang, K., Chen, Y., Liu, S., & Wen, Z. (2021). "Black Swan" events and the dynamic network of volatility transmission in China's financial markets. Journal of Financial Economics Research, (05), 31-47. doi.org/CNKI:SUN:JIRO.0.2021-05-003
31. Hubbs, T., & Kuethe, T. (2017). A disequilibrium evaluation of public intervention in agricultural credit markets. Agricultural Finance Review, 77(1), 37-49. doi.org/10.1108/AFR-04-2016-0032
32. Hughes, D. W. (1981). Impacts of regulatory change on financial markets for agriculture: discussion. American journal of agricultural economics, 63(5). doi.org/10.2307/1241270
33. Hung, K. C., & Ma, T. (2017). Does monetary policy have any relationship with the expectations of stock market participants? Journal of Multinational Financial Management, 39, 100-117. doi.org/10.1016/j.mulfin.2016.11.004
34. Jiang, F., Meng, L., & Tang, G. (2021). Media text sentiment and stock return prediction. Economics (Quarterly), 04, 1323-1344. doi.org/10.13821/j.cnki.ceq.2021.04.10
35. Kahneman, D. A. N. I. E. L., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 363-391. doi.org/10.2307/1914185
36. Kassouri, Y., & Kacou, K. Y. T. (2022). Does the structure of credit markets affect agricultural development in West African countries?. Economic Analysis and Policy, 73, 588-601. doi.org/10.1016/j.eap.2021.12.015
37. Khan, M., Kayani, U. N., Khan, M., Mughal, K. S., & Haseeb, M. (2023). COVID-19 Pandemic & Financial Market Volatility; Evidence from GARCH Models. Journal of Risk and Financial Management, 16(1), 50. doi.org/10.3390/jrfm16010050
38. Kuethe, T., & Hubbs, T. (2021). Credit booms and financial instability in US agriculture. Agricultural Finance Review, 81(1), 1-20. doi.org/10.1108/AFR-04-2020-0055
39. LaDue, E. L., & Leatham, D. J. (1984). Floating versus fixed-rate loans in agriculture: effects on borrowers, lenders, and the agriculture sector. American Journal of Agricultural Economics, 66(5), 607-613. doi.org/10.2307/1240963
40. LEE, C. M. C., Shleifer, A., & Thaler, R. H. (1991). Investor sentiment and the closed-end fund puzzle. The Journal of Finance, 46(1), 75-109. doi.org/10.1111/j.1540-6261.1991.tb03746.x
41. Li, C., & Zheng, K. (2022). The internal logic of China's economic policy and financial market volatility: From the perspective of big data mining. Modern Economic Research, (09), 49-61. doi.org/10.13891/j.cnki.mer.2022.09.002
42. Li, L., Cao, X., & Ding, W. L. (2023). The indirect time-varying impact of interest rates on stock prices: An empirical study based on the TVP-SV-VAR model. Accounting Friend, (09), 16-22. doi.org/10.3969/j.issn.1004-5937.2023.09.003
43. Li, R., Qian, Z., & Sun, T. (2017). The effectiveness of China's monetary policy and its interaction with the stock market: An empirical study based on the SVAR model. Economic Theory and Economic Management, 03, 48-60. doi.org/10.3969/j.issn.1004-1804.2017.03.005
44. Liu, J., & Shen, Y. (2022). Can central bank communication mitigate financial market volatility? Modern Economic Research, (03), 36-43. doi.org/10.13891/j.cnki.mer.2022.03.011
45. Liu, T. (2020). An empirical analysis of the impact of economic policy uncertainty on stock market volatility (Doctoral dissertation, Central University of Finance and Economics). https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2022&filename=1020830853.nh
46. Lu, M. (2008). Crisis management of the stock market and commercial banks. Research on Financial Issues, (08), 119-123. doi.org/10.3969/j.issn.1000-2854.2008.08.020
47. Lü, Z., Xu, J., & Liu, L. (2023). The interest rate transmission mechanism and transmission efficiency of monetary policy in China: empirical analysis based on TVP-SV-VAR model. Journal of the Asia Pacific Economy, 1-35. doi.org/10.1080/13547860.2023.2206691
48. Luo, J., & Hu, J. (2023). Agricultural credit guarantees, credit supply, and agricultural economic development. Finance & Trade Research, 03, 68-79. doi.org/10.19337/j.cnki.34-1093/f.2023.03.006
49. Luo, X. (2020). Research on stock market price and volatility forecasting in China based on deep learning (Doctoral dissertation, Zhongnan University of Economics and Law). https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2022&filename=1021511449.nh
50. Mckibbin, W. J., Wang, Z., & Coyle, W. (2001). The asian financial crisis and global adjustments: implications for us agriculture. Japanese Economic Review, 52(4), 471-490. doi.org/10.1111/1468-5876.00207
51. Moessner, R. (2014). Effects of explicit FOMC policy-rate guidance on equities and risk measures. Applied Economics, 46(18), 2139-2153. doi.org/10.1080/00036846.2014.894668
52. Morck, R., Yeung, B., & Yu, W. (2000). The information content of stock markets: why do emerging markets have synchronous stock price movements? Journal of financial economics, 58(1-2), 215-260. doi.org/10.1016/S0304-405X(00)00071-4
53. Nakajima, J. (2011). Time-varying parameter VAR model with stochastic volatility: An overview of methodology and empirical applications. Monetary and Economic Studies, 29, 107-142.
54. Pietola, K., Myyrä, S., & Heikkilä, A. M. (2011). The penetration of financial instability in agricultural credit and leveraging. Brussels, Belgium: Centre for European Policy Studies (CEPS).
55. Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies, 72(3), 821-852. doi.org/10.1111/j.1467-937X.2005.00353.x
56. Qian, Z., Wang, F., & Sun, T. (2021). The impact of financial cycles on real estate prices: An empirical study based on the SV-TVP-VAR model. Financial Research, 03, 58-76. doi.org/10.3969/j.issn.1002-2563.2021.03.004
57. Regmi, M., & Featherstone, A. M. (2022). Competition, performance and financial stability in US agricultural banking. Agricultural Finance Review, 82(1), 67-88. doi.org/10.1108/AFR-12-2020-0185
58. Reif, M. (2022). Time‐Varying Dynamics of the German Business Cycle: A Comprehensive Investigation. Oxford Bulletin of Economics and Statistics, 84(1), 80-102. doi.org/10.1111/obes.12464
59. Rodriguez, G., Castillo B, P., & Ojeda Cunya, J. A. (2023). Time-Varying Effects of External Shocks on Macroeconomic Fluctuations in Peru: An Empirical Application using TVP-VAR-SV Models. Open Economies Review, 1-36. doi.org/10.1007/s11079-023-09742-5
60. Schwert, G. W. (1989). Why does stock market volatility change over time? Journal of finance, 44(5), 1115-1153. doi.org/10.1111/j.1540-6261.1989.tb02647.x
61. Schwert, G. W. (1990). Stock market volatility. Financial analysts journal, 23-34. doi.org/10.2469/faj.v46.n3.23
62. Shan, J., & Wang, H. (2024). Financial risk spillover between capital markets and the real sector and the effect of macro policy regulation. Journal of Central University of Finance and Economics, (04), 3-17. doi.org/10.19681/j.cnki.jcufe.2024.04.007
63. Shane, M. D., & Liefert, W. M. (2000). The international financial crisis: macroeconomic linkages to agriculture. American Journal of Agricultural Economics, 82(3), 682-687. http://www.jstor.org/stable/1244624
64. Song, C., & Zhang, Y. (2023). Economic policy uncertainty, financial stability, and economic fluctuations: A dynamic analysis based on the TVP-SV-VAR model. Finance Theory and Practice, (02), 32-37. doi.org/10.16339/j.cnki.hdxbcjb.2023.02.005
65. Sun, L., & Zhu, Y. (2022). The risk spillover effect of the Chinese stock market on commercial banks. Applied Probability and Statistics, (02), 285-302. doi.org/10.3969/j.issn.1000-0215.2022.02.006
66. Tang, C., & Liu, X. (2023). Bitcoin speculation, investor attention and major events. Are they connected?. Applied Economics Letters, 30(8), 1033-1041. doi.org/10.1080/13504851.2022.2033677
67. Thiem, C. (2020). Cross-Category, Trans-Pacific Spillovers of Policy Uncertainty and Financial Market Volatility. Open Economies Review, 31(2), 317-342. doi.org/10.1007/s11079-019-09559-1
68. Wang, L., & Liu, H. (2022). Can central bank communication effectively respond to sudden “tests”?: A text analysis of People’s Bank of China communication events. Journal of Beijing Institute of Technology (Social Sciences Edition), (01), 77-89. doi.org/10.15918/j.jbitss1009-3370.2022.1900
69. Wang, Y., & Wang, Y. (2014). The role of investor sentiment in asset pricing. Management Review, (06), 42-55. doi.org/10.14120/j.cnki.cn11-5057/f.2014.06.039
70. Wang, Y., Liu, H., & Wu, L. (2009). Information transparency, institutional investors, and stock price synchronicity. Financial Research, (12), 162-174. doi.org/10.1016/j.jfineco.2005.01.003
71. Wang, Y., Yuan, J., & Liu, T. (2023). The dynamic spillover effects of U.S. fiscal and monetary policy adjustments on the Chinese economy: An empirical study based on the TVP-SV-VAR model. International Trade Issues, (07), 54-68. doi.org/10.13687/j.cnki.gjjmts.2023.07.007
72. Wang, Z. L., & Liu, N. (2024). High goodwill impairment and stock price crash risk: A study based on investor sentiment. Finance and Accounting Monthly, (14), 80-84. doi.org/10.16144/j.cnki.issn1002-8072.2024.14.015
73. Wen, X. C. (2017). The impact of changes in investor sentiment and monetary policy adjustments on stock market cycles: Evidence from a stock market DSGE model with heterogeneous expectations. Journal of Central University of Finance and Economics, (08), 23-36+46. doi.org/10.19681/j.cnki.jcufe.2017.08.004
74. Xie, S., & Mo, T. (2014). Index futures trading and stock market volatility in China: a difference‐in‐difference approach. Journal of Futures Markets, 34(3), 282-297. doi.org/10.1002/fut.21650
75. Xing, D., & Guan, Z. (2020). Stock market volatility, economic policy uncertainty, and household participation in financial markets: An empirical analysis based on CFPS panel data. New Finance, (11), 57-64. doi.org/CNKI:SUN:XJRO.0.2020-11-011
76. Xing, T., & Wang, X. (2022). The impact of economic policy uncertainty on the development of China's capital market and its countermeasures. Zhongzhou Academic Journal, (06), 14-20. doi.org/10.3969/j.issn.1000-0105.2022.06.003
77. Xu, A., & Zheng, X. (2023). Impact of the COVID-19 pandemic, investor sentiment, and corporate financing constraints. Fiscal Science, (02), 76-86. doi.org/10.19477/j.cnki.10-1368/f.2023.02.013
78. Xu, B. (2018). Speculators in stock index futures and stock market volatility: Empirical evidence from the CSI 300 index futures. Economic Survey, (02), 151-157. doi.org/10.15931/j.cnki.1006-1096.20180105.008
79. Xue, F. (2005). A study of investor behavior based on emotions (Doctoral dissertation, Fudan University). https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFD9908&filename=2005121470.nh
80. Yang, S., Niu, D., Liu, T., & Wang, Z. (2021). The impact of investor sentiment on the financialization of real enterprises from a behavioral finance perspective. Management Review, (06), 3-15. doi.org/10.14120/j.cnki.cn11-5057/f.2021.06.001
81. Yang, X. (2016). Research on volatility and options of the Chinese stock market (Doctoral dissertation, University of International Business and Economics). https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2017&filename=1017002338.nh
82. Yi, H. (2020). Interview with the Chairman of the China Securities Regulatory Commission on China's capital market opening to the world and U.S.-China cross-border regulatory cooperation issues. Retrieved from http://www.csrc.gov.cn/csrc/c100028/c1000757/content.shtml
83. You, W., Chen, S., Chen, J., & Ren, Y. (2023). The impact of "speak much, act little" environmental responsibility performance on stock price crash risk: The mediating effect of investor sentiment. China Management Science. doi.org/10.16381/j.cnki.issn1003-207x.2023.1361
84. Yu, X. W. (2013). A study on the relationship between government bond market volatility and stock market volatility. Commercial Times, (04), 57-58. doi.org/CNKI:SUN:SYJJ.0.2013-04-026
85. Yu, Y., Wei, H., & Chen, T. (2022). Applications of the Investor Sentiment Polarization Model in Sudden Financial Events. Systems, 10(3), 75. doi.org/10.3390/systems10030075
86. Zhang, C., & Yu, T. (2023). Rural credit investment, service outsourcing level, and urban-rural income gap. Economic System Reform, (01), 89-98. doi.org/CNKI:SUN:JJTG.0.2023-01-010
87. Zhang, X., Zhou, H., & Lee, C. C. (2022). Systemic risk of China’s financial industry during the spread of the COVID-19 epidemic and the breakdown of crude oil negotiation. Emerging Markets Finance and Trade, 58(1), 56-69. doi.org/10.1080/1540496X.2021.1968824
88. Zhao, Y., Mou, D., Li, Z. H., & Ma, J. M. (2024). The impact of economic policy uncertainty on the IPO underpricing rate of the Science and Technology Innovation Board. Investment Research, (04), 120-144. doi.org/10.15932/j.cnki.tzyj.2024.04.008
89. Zheng, M., Ni, Y., & Liu, L. (2010). The impact of monetary policy on stock prices in China: An empirical analysis based on the Markov regime-switching VAR model. Economic Management, (11), 7-15. doi.org/10.19616/j.cnki.bmj.2010.11.004
90. Zhou, D., Siddik, A. B., Guo, L., & Li, H. (2023). Dynamic relationship among climate policy uncertainty, oil price and renewable energy consumption—Findings from TVP-SV-VAR approach. Renewable Energy, 204, 722-732. doi.org/10.1016/j.renene.2023.01.018
91. Zhu, N., Chen, Y., & Xu, Y. (2019). Can new monetary policy tools stabilize the capital market?—Empirical evidence based on China's stock index returns. Systems Engineering, (01), 86-100. doi.org/CNKI:SUN:GCXT.0.2019-01-009
92. Zou, W., Wang, X., & Xie, X. (2020). Financial market response to central bank communication: An event study based on the stock market. Financial Research, (02), 34-50. doi.org/10.16381/j.cnki.issn1001-8004.2020.02.003