Summary:
In the context of declining effectiveness in traditional monetary policy tools, major central banks worldwide increasingly rely on public communication to support their policy objectives. At the 15th Lujiazui Forum in 2024, Governor Pan Gongsheng emphasized that “a key feature of the modern monetary policy framework is the central bank's ability to transparently and clearly communicate its policy considerations and future outlook to markets and the public,” underscoring the critical role of central bank communication in contemporary policy frameworks. Concurrently, the government bond yield curve, serving as a benchmark rate with a complete maturity structure, constitutes an essential component of China's interest rate system. As interest rate liberalization advances and the treasury bond market matures, the term structure of government bond yields increasingly reflects forward-looking market expectations. Notably, institutions such as the European Central Bank and the Bank of Japan have integrated yield curve analysis into their monetary policy frameworks. Consequently, strengthening the modern monetary policy framework necessitates leveraging both the signaling effects of central bank communication and enhancing the transmission efficiency of the yield curve. Existing research on central bank communication and bond yields exhibits two primary limitations: First, there is a lack of systematic examination regarding the impact of communication across the entire yield curve. Second, existing literature has a predominant focus on the unidirectional influence of communication on market yields, overlooking the critical role of market feedback in policy formulation. To address these gaps, this study integrates central bank communication into a no-arbitrage term structure model grounded in financial market principles. By adopting mixed-frequency econometric methods, we incorporate information flows combining both high-and low-frequency data. This framework enables an investigation into the bidirectional interaction between central bank communication and government bond yields in China from 2002 to 2023. Utilizing textual analysis techniques, we construct a sentiment index for central bank communication and decompose communication content into two dimensions: economic/financial fundamentals and policy guidance. This decomposition allows for quantifying their differential impacts on the interest rate term structure. This research overcomes challenges in modeling the “signaling mechanism” of central bank communication and addressing complexities in mixed-frequency data integration. Its contributions are threefold. First, by embedding central bank communication within a mixed-frequency affine term structure model under no-arbitrage constraints, it provides a novel perspective for evaluating the efficacy of communication strategies. Second, the application of textual analysis to measure communication sentiment enables precise identification of signaling mechanisms within mixed-frequency information environments, enhancing the accuracy and timeliness of expectations management assessment. Third, through the lens of signaling effects, it elucidates the bidirectional dynamics between communication and the term structure, revealing variations in transmission mechanisms and effectiveness across different economic conditions. Empirical findings reveal three key insights. First, a bidirectional interaction exists between central bank communication and the yield curve: positive communication sentiment lowers the entire yield curve, narrows the term spread, and elevates medium-term yields; conversely, the slope and curvature factors of the yield curve significantly inform central bank communication decisions. Second, the expectation guidance effect of communication exhibits maturity-dependent heterogeneity, exerting a stronger influence on short-to-medium maturities than on the long end. Additionally, positive communication sentiment reduces risk premiums demanded by markets. Third, with the deepening of interest rate liberalization and refinement of communication practices, the guidance efficacy of the People's Bank of China's communication has strengthened, while market feedback increasingly informs communication strategies. Currently, signaling effects predominantly derive from historical economic and policy interpretations; the guiding potential of forward-looking policy signals remains underutilized. This study offers empirical foundations and policy insights for optimizing central bank communication strategies and leveraging market signals to enhance monetary policy effectiveness. To strengthen expectation guidance, establishing tiered and differentiated communication channels could improve market comprehension. Simultaneously, central banks could deepen the extraction of medium-to-long-term growth signals embedded in the yield curve, utilizing communication to bolster market confidence in sustainable economic health.
尚玉皇, 刘华, 申峰. 预期的博弈:央行沟通与国债收益率曲线[J]. 金融研究, 2025, 543(9): 20-38.
SHANG Yuhuang, LIU Hua, SHEN Feng. The Game of Expectations: Central Bank Communication and the Government Bond Yield Curve. Journal of Financial Research, 2025, 543(9): 20-38.
[1]陈良源、林建浩、王少林和詹焯扬,2021,《央行沟通对于货币政策实际干预的预测能力研究》,《统计研究》第1期,第38～50页。 [2]董青马、张皓越、马剑文和尚玉皇,2024,《央行沟通与资产价格——识别“潜在”未预期货币政策信息》,《金融研究》第6期,第 40～59页。 [3]关禹、王雪标和孙光林,2019,《货币政策对国债利率期限结构的影响机制——基于预期渠道和溢价渠道双重视角的实证分析》,《当代财经》第1期,第53~65页。 [4]郭豫媚、陈伟泽和陈彦斌,2016,《中国货币政策有效性下降与预期管理研究》,《经济研究》第1期,第28～41页。 [5]郭豫媚和周璇,2018,《央行沟通、适应性学习和货币政策有效性》,《经济研究》第4期,第77~91页。 [6]姜富伟,胡逸驰和黄楠,2021a,《央行货币政策报告文本信息、宏观经济与股票市场》,《金融研究》第6期,第95～113页。 [7]姜富伟、孟令超和唐国豪,2021b,《媒体文本情绪与股票回报预测》,《经济学(季刊)》第4期,第 1323～1344页。 [8]林建浩、陈良源和宋登辉,2019,《如何测度央行行长的口头沟通信息——一种基于监督学习的文本分析方法》,《统计研究》第8期,第3～18页。 [9]林建浩和赵文庆,2015,《中国央行沟通指数的测度与谱分析》,《统计研究》第1期,第52～58页。 [10]缪延亮,2023,《信心的博弈——现代中央银行与宏观经济》,中信出版集团。 [11]聂丽和石凯,2024,《中央银行前瞻性指引的政策语义与预期渠道研究》,《当代财经》第11期,第70～83页。 [12]欧阳志刚和胡雯华,2024,《央行沟通公告有助于提升政策利率的传导效率吗》,《数量经济技术经济研究》第10期,第69～88页。 [13]尚玉皇和张皓越,2023,《国债收益率与高频宏观因子——基于非规则混频利率期限结构模型》,《统计研究》第10期,第109～123页。 [14]尚玉皇、张皓越和董青马,2025,《中国货币政策传导效应:统筹发展与安全视角》,《中国工业经济》第1期,第 24～42页。 [15]尚玉皇、李炜祺和董青马,2022,《公开市场操作与利率期限结构行为——基于混频数据信息的研究视角》,《金融研究》第6期,第16～35页。 [16]尚玉皇和郑挺国,2023,《中国宏观经济与利率期限结构:混频作用机制及跨逆周期调控效应》,《财贸经济》第11期,第1～18页。 [17]熊海芳和王志强,2012,《货币政策意外、利率期限结构与通货膨胀预期管理》,《世界经济》第6期,第30～55页。 [18]易纲,2019,《中国的利率体系与利率市场化改革》,《金融研究》第9期,第1～11页。 [19]张一帆、林建浩、杨扬和邓益萌,2022,《央行沟通、信息冲击与国债市场波动》,《系统工程理论与实践》第3期,第575～590页。 [20]Ang, A. and M. Piazzesi, 2003, “A No-ArbitrageVector Autoregression of Term Structure Dynamics With Macroeconomic and Latent Variables”, Journal of Monetary Economics, 50(4), pp.745～787. [21]Bernanke, B. S.,2020, “The New Tools of Monetary Policy”, American Economic Review, 110(4), pp.943-983. [22]Christensen, J. H. E., F. X. Diebold and G. D. Rudebusch, 2011, “The Affine Arbitrage～Free Class of Nelson-Siegel Term Structure Models”, Journal of Econometrics, 164(1), pp. 4～20. [23]Diebold, F. X.and C. Li, 2006, “Forecasting The Term Structure of Government Bond Yields”, Journal of Econometrics, 130(2), pp. 337～364. [24]Eusepi, S., 2010, “Central Bank Communication and The Liquidity Trap”, Journal of Money, Credit and Banking, 42(2-3), pp. 373～397. [25]Geraats, P. M., 2002, “Central Bank Transparency”, The Economic Journal, 112(483), pp. F532～F565. [26]Hansen, S., M. McMahon and M. Tong, 2019, “The Long-Run Information Effect of Central Bank Communication”, Journal of Monetary Economics, 108, pp. 185～202. [27]Kung, H., 2015, “Macroeconomic Linkages Between Monetary Policy and The Term Structure of Interest Rates”, Journal of Financial Economics,115(1) pp. 42～57. [28]Kuttner, K.N, 2001, “Monetary Policy Surprises and Interest Rates: Evidence From The Fed Funds Futures Market”, Journal of Monetary Economics, 47(3), pp.523～544. [29]Leombroni, M., A. Vedolin, G. Venter and P. Whelan, 2021, “Central Bank Communication and the Yield Curve”, Journal of Financial Economics, 141(3), pp. 860～880. [30]Moench, E., 2012, “Term Structure Surprises: The Predictive Content of Curvature, Level, and Slope”, Journal of Applied Econometrics, 27(4), pp. 574～602. [31]Morris, S. and H. S. Shin, 2002, “Social Value of Public Information, American Economic Review”, 92 (5), pp.1521～1534. [32]Nakamura, E.and J. Steinsson, 2018, “High-Frequency Identification of Monetary Non-Neutrality: The Information Effect”, The Quarterly Journal of Economics, 133(3), pp. 1283～1330. [33]Rudebusch, G. D.and T. Wu, 2008, “A Macro-Finance Model of the Term Structure, Monetary Policy and the Economy”, The Economic Journal, 118(530), pp.906～926. [34]Shang Y.and T.Zheng, 2018, “Fitting and Forecasting Yield Curves With a Mixed-Frequency Affine Model: Evidence from China”, Economic Modelling, 68, pp.145～154. [35]Shin, H.S., 2017, “Can Central Banks Talk too Much?”, Speech at the ECB conference on Communications Challenges for Policy Effectiveness, Accountability and Reputation. [36]Swanson, E. T., 2021, “Measuring the Effects of Federal Reserve Forward Guidance and Asset Purchases on Financial Markets”, Journal of Monetary Economics,118, pp.32～53. [37]Vayanos, D. and J. L.Vila., 2021, “A Preferred‐Habitat Model of The Term Structure of Interest Rates”, Econometrica, 89(1), pp.77～112.
