Interest Rate Changes, Trading Behaviour and Financial Stability——A Continuous-Time DSGE Model with Heterogeneous Agents and Beliefs
YAN Yu, TONG Yan, HASI Muqier, JIN Zhuang
International Business School, Beijing Foreign Studies University; School of Economics and Management, University of Chinese Academy of Sciences; School of Finance, Zhongnan University of Economics and Law; Inner Mongolia University of Finance and Economics
Summary:
The fine-tuning of monetary policy always influences the trading sentiment of investors in risk assets to some extent. The interaction between trader sentiment and financial market stability has been a focal point in financial research. However, existing studies have not provided a comprehensive understanding of this mechanism. This paper establishes a heterogeneous agent continuous-time DSGE model that incorporates heterogeneous beliefs to explore how differences in expected forms affect responses to monetary policy. By introducing heterogeneous expectations, a more thorough discussion of risk asset pricing is achieved. Specifically, investors are divided into convergent expectation traders, represented by institutional investors, and extrapolative expectation traders, characterized by algorithmic trading and momentum strategies. The price of risk assets can be expressed as an explicit expression of dividends and expectations. This paper provides an analytical solution for risk asset pricing under the assumption of heterogeneous expectations, enhancing the understanding of how prices are influenced by exogenous processes and trader expectations. The well-defined property of this equation reveals a clear relationship between risk asset prices and various exogenous processes, serving as a foundation for numerical simulations and offering significant references for future empirical asset pricing model designs. One of the key contributions of this study is the integration of stock market trading behaviors' amplification effects on monetary policy into a classic continuous-time general equilibrium model, facilitating a unified discussion of the excess volatility puzzle and financial stability. Despite the frequent continuous oscillation of risk asset prices and extensive literature examining stock price responses to monetary policy shocks, this paper presents a novel perspective based on heterogeneous beliefs within a general equilibrium framework. Numerical simulation results indicate that irrational traders who follow trends influence the effects of monetary policy shocks on financial markets. The findings show that accommodative monetary policy directly influences the movements of risk asset prices. When traders expect that monetary easing will continue, they choose to increase their investments in risk assets, resulting in a sustained upward trend in prices. During this process, extrapolative expectation traders recognize this trend, forming expectations of continued price increases and actively reallocating assets, further driving up risk asset prices. Financial market stability has long been an important research topic in economics and finance. However, the rational behaviors and equilibrium assumptions in traditional theories fail to fully explain actual market operations. Many studies demonstrate the presence of various types of investors in financial markets, particularly focusing on extrapolative expectation traders, whose behavior is primarily driven by emotions and psychological factors, deviating from rational expectations based on fundamentals and market equilibrium. This paper investigates the impact of extrapolative expectation traders on financial stability, exploring their behavioral patterns, market influences, and the potential risks and challenges they pose to financial stability. The results indicate that the pattern of behavior of extrapolative expectation traders significantly affects the stability of financial markets. This paper not only focuses on the effects of extrapolative expectations on financial markets but also examines their impact on the effects of monetary policy changes. Given the close relationship between monetary policy formulation and financial regulation in China, policymakers need to coordinate closely with regulatory bodies to craft effective policy measures. Thus, monetary policymakers must monitor the behaviors of different types of expectation traders in the market closely and take appropriate policy actions. For instance, when the market overly relies on extrapolative expectations and forms noticeable price bubbles, policymakers can adopt tighter monetary policies to curb market overheating and prevent instability. Additionally, they can enhance market transparency and efficiency through information disclosure and regulation, reducing distortions caused by information asymmetry and incompleteness. To maintain financial market stability, it is essential to strengthen both monitoring and management of extrapolative expectation traders' behaviors, improve investors' rational decision-making capabilities and risk management awareness, reinforce information disclosure and regulatory frameworks, and enhance international cooperation to collectively address these impacts, thereby improving market efficiency and stability and promoting sustainable economic development.
闫昱, 童彦, 哈斯木其尔, 金桩. 利率变动、交易行为与金融稳定——一个融合异质信念的异质代理人连续时间DSGE模型[J]. 金融研究, 2024, 534(12): 20-39.
YAN Yu, TONG Yan, HASI Muqier, JIN Zhuang. Interest Rate Changes, Trading Behaviour and Financial Stability——A Continuous-Time DSGE Model with Heterogeneous Agents and Beliefs. Journal of Financial Research, 2024, 534(12): 20-39.
[1]卞志村和高洁超, 2014,《适应性学习, 宏观经济预期与中国最优货币政策》, 《经济研究》第4期, 第32~46页。 [2]邓燕飞、董丰、徐迎风和冯文伟, 2017, 《价格刚性, 异质性预期和通货膨胀动态》, 《管理世界》第9期, 第17~26页。 [3]李力、王博、刘潇潇和郝大鹏, 2016,《短期资本, 货币政策和金融稳定》, 《金融研究》第9期, 第18~32页。 [4]刘海波, 邵飞飞和钟学超, 2019, 《我国结构性减税政策及其收入分配效应——基于异质性家庭NK-DSGE的模拟分析》, 《财政研究》第3期, 第30~46页。 [5]宫庆彬和刁训娣, 2024, 《投资者异质性、分红与资产价格的复杂动态》, 《管理工程学报》第3期, 第46~57页。 [6]江春,向丽锦和肖祖沔,2018,《货币政策、收入分配及经济福利——基于DSGE模型的贝叶斯估计》,《财贸经济》第3期,第17~34页。 [7]谭政勋、刘娟和郑尊信, 2024, 《全要素生产率、投资者外推预期与中国股票市场异象》,《经济研究》第2期, 第97~115页。 [8]潘敏和周闯, 2019, 《宏观审慎监管, 房地产市场调控和金融稳定——基于贷款价值比的 DSGE 模型分析》, 《国际金融研究》第4期, 第14~23页。 [9]司登奎、葛新宇、曾涛和李小林, 2019, 《房价波动, 金融稳定与最优宏观审慎政策》, 《金融研究》第11期, 第38~56页。 [10]王曦、王茜和陈中飞, 2016, 《货币政策预期与通货膨胀管理——基于消息冲击的 DSGE 分析》, 《经济研究》第2期, 第16~29页。 [11]王自力, 2005, 《金融稳定与货币稳定关系论》, 《金融研究》第5期, 第1~11页。 [12]吴念鲁和郧会梅, 2005, 《对我国金融稳定性的再认识》, 《金融研究》第2期, 第152~158页。 [13]邢曙光和黄梅波, 2015, 《最优区域间转移支付规则》, 《金融研究》第11期, 第98~114页。 [14]张晓芳和张宸瑄, 2020, 《我国家庭消费结构与货币政策效果分析——基于异质性家庭的DSGE模型》, 《软科学》,第5期, 第43~49页。 [15]张雪兰和何德旭, 2012, 《货币政策立场与银行风险承担——基于中国银行业的实证研究 (2000—2010)》,《经济研究》第5期, 第31~44页。 [16]赵玮和李勇, 2022, 《需求结构、异质性预期和房价波动——兼论限购限贷政策与货币政策效果》, 《南开经济研究》第7期, 第81~99页。 [17]Bacchetta, P., E. Mertens, and E. V. Wincoop, 2009, “Predictability in Financial Markets: What Do Survey Expectations Tell Us?”, Journal of International Money and Finance, 28(3), pp. 406~426. [18]Barberis, N., R. L. Greenwood, and A. Shleifer, 2015, “X-CAPM: An Extrapolative Capital Asset Pricing Model.”, Journal of Financial Economics, 115(1), pp. 1~24. [19]Barberis, N., A. Shleifer, and R. Vishny, 1998, “A Model of Investor Sentiment. ”, Journal of Financial Economics, 49, pp. 307~343. [20]Bilbiie, F. O., 2008, “Limited Asset Markets Participation, Monetary Policy and (Inverted) Aggregate Demand Logic.”, Journal of Economic Theory, 140 (1), pp. 162~196. [21]Bollerslev, T., and R. T. Baillie, 1990, “A Multivariate Generalized ARCH Approach to Modeling Risk Premia in Forward Foreign Exchange Rate Markets”, Journal of International Money and Finance, 19(3), pp. 309~324. [22]Brock, W. A. and C. H. Hommes, 1998, “Heterogeneous Beliefs and Routes To Chaos in A Simple Asset Pricing Model. ”, Journal of Economic Dynamics and Control, 22(8-9), pp. 1235~1274. [23]Campbell, J. Y., and N. G. Mankiw., 1989, “Consumption, Income and Interest Rates: Reinterpreting the Time Series Evidence.”,NBER Macroeconomics Annual 1989, 4, pp. 185~246. [24]Challe, E., J. Matheron, X. Ragot, and J. F. Rubio-Ramirez, 2017, “Precautionary Saving and Aggregate Demand.”, Quantitative Economics, 8 (2), pp. 435~478. [25]Daniel, K. D., D. Hirshleifer, and A. Subrahmanyam, 1998, “Investor Psychology and Security Market Under-and Overreactions. ”, The Journal of Finance, 53(6), pp. 1839~1885. [26]Engle, R. F., 2002, “Dynamic Conditional Correlation:A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models”, Journal of Business & Economic Statistics,20(3), pp. 339~350. [27]Gali, J. 2014, “Monetary Policy and Rational Asset Price Bubbles. ”, American Economic Review, 104(3), pp. 721~752. [28]Golosov, M., J. Hassler, P. Krusell, and A. Tsyvinski, 2014,“Optimal Taxes on Fossil Fuel in General Equilibrium”, Econometrica, 82(1), pp. 41~88. [29]Gromb, D.,and D. Vayanos, 2002, “ Equilibrium and Welfare in Markets with Financially Constrained Arbitrageurs.”, Journal of Financial Economics, 66(2-3), pp. 361~407. [30]Harrison, J. M., and D. M. Kreps, 1978, “Speculative Investor Behavior in a Stock Market with Heterogeneous Expectations. ”, The Quarterly Journal of Economics, 92(2), pp. 323~336. [31]Hirshleifer, D., J. Li, and J. Yu, 2015, “Asset Pricing in Production Economies with Extrapolative Expectations.”, Journal of Monetary Economics, 76, pp. 87~106. [32]Kaplan, G., B. Moll, and G. L. Violante, 2018, “Monetary Policy According To HANK. ”, American Economic Review, 108(3), pp. 697~743. [33]Krusell, P., and A. Smith, 1998, “Income and Wealth Heterogeneity in the Macroeconomy”, Journal of Political Economy, 106(5), pp. 867~896. [34]Kyle, A. S. and W. Xiong, 2001, “Contagion As A Wealth Effect. ”, The Journal of Finance, 56(4), pp. 1401~1440. [35]Massaro, D., 2013, “Heterogeneous Expectations in Monetary DSGE Models”, Journal of Economic Dynamics and Control, 37(3), pp. 680~692. [36]Shiller, R. J., 1981, “Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends? ”, American Economic Review, 71(3), pp. 421~436. [37]Werning, I., 2015, “Incomplete Markets and Aggregate Demand (No. w21448)”, National Bureau of Economic Research. [38]Xiong, W., 2001, “Convergence Trading With Wealth Effects: An Amplification Mechanism in Financial Markets. ”, Journal of Financial Economics, 62(2), pp. 247~292. [39]Yan, Y., Y. Tong, and Y. Wang, 2024, “Contagion Mechanisms Under Heterogeneous Beliefs”, Applied Economics, pp. 1~25.