Summary:
At the Central Financial Work Conference in October 2023, President Xi Jinping emphasized the need to uphold risk prevention and control as the perennial focus of the financial profession and ensure that risks are identified, revealed, and resolved as early as possible. It remains a key future task for the financial profession to improve the risk monitoring and early-warning system and the ability to reduce and control financial risk. Considering the great attention aroused by tail-risk events, such as the simultaneous crashes of both the stock and bond markets, a major priority is to identify the co-movements of risk across different markets and enhance the risk early-warning system for both the stock and bond markets. In the context of stock-bond risk co-movement, it is of both academic and practical significance to forecast stock market and bond market risk accurately and effectively using state-of-the-art machine learning techniques. This will help to overcome the limitations of traditional risk prediction methods and improve the risk monitoring and early-warning system for both the stock market and bond market. It will also contribute to identifying risk co-movements across different markets, thus preventing extreme risk events like simultaneous crashes of the stock and bond markets. This paper conducts an analysis based on a sample of Chinese listed companies in the period from January 2015 to September 2022. First, we employ the MVMQ-CAViaR model and quantile regression method to investigate the tail-risk co-movements between the stock market and bond market. Additionally, we distinguish between the heterogeneous risk spillover relationships of companies with and without default records. The results show that stock-bond risk co-movement is more significant for companies that have defaulted. Next, this paper utilizes state-of-the-art machine learning techniques, namely the quantile regression forest, quantile gradient boosting model, and quantile regression neural network, to construct prediction models for tail risk based on the emerging perspective of stock-bond risk co-movement. On this basis, we further evaluate the bi-directional predictive power between stock market risk and bond market risk using the quantile loss function, quantile goodness of fit, and Diebold-Mariano test. For most prediction models for the “bond market risk → stock market risk” direction, the results indicate that considering the bond market risk increases the robustness and accuracy of the forecasting of stock market risk by improving the goodness of fit and reducing fitting errors. The application of a machine learning framework including the quantile gradient boosting model, quantile regression forest, and quantile regression neural network significantly strengthens the prediction of stock market risk. In contrast, in the prediction model for “stock market risk → bond market risk”, the quantile gradient boosting model outperforms the other models in predicting bond market risk. This is attributable to the gradient boosting method, which corrects fitting errors through iterative learning, thereby better capturing the stock market risk information. Furthermore, we divide the sample according to industry attributes to assess the heterogeneous predictive power of different models. We find evidence of asymmetric predictive power between stock market risk and bond market risk, in which a unidirectional predictive power for “bond market risk → stock market risk” is documented in most industries while a bi-directional predictive power for “stock market risk → bond market risk” is only evident in the materials, daily consumption, finance, and real estate industries. Meanwhile, under the “stock market risk → bond market risk” prediction framework, the finance industry has the most forecastable bond market risk, as it can be identified by all three machine learning models. Finally, this paper yields policy implications for strengthening the risk prediction system for key fields in China. First, policy makers should improve the tail-risk early-warning framework for China's capital market using the new perspective of stock-bond risk co-movement. Second, state-of-the-art machine learning techniques should be promoted in the financial regulation field to enhance the financial stability guarantee system. Third, regulators should improve the industry-level financial risk forecasting system and tail-risk early warning for key fields including the finance industry.
杨子晖, 张平淼, 林师涵. 股票市场与债券市场的风险联动与预测研究——基于机器学习的前沿视角[J]. 金融研究, 2024, 523(1): 131-149.
YANG Zihui, ZHANG Pingmiao, LIN Shihan. Risk Co-movement and Forecasting of the Stock Market and Bond Market Based on the Forefront Perspective of Machine Learning. Journal of Financial Research, 2024, 523(1): 131-149.
[1]戴志锋、朱皓阳和尹华,2022,《我国石油、黄金、房地产和金融部门间系统风险动态溢出效应研究》,《系统工程理论与实践》第10期,第2603～2616页。 [2]李政、梁琪和方意,2019,《中国金融部门间系统性风险溢出的监测预警研究——基于下行和上行ΔCoES指标的实现与优化》,《金融研究》第2期,第40～58页。 [3]刘程程、苏治和宋鹏,2020,《全球股票市场间风险传染的测度、监管及预警》,《金融研究》第11期,第94～112页。 [4]王重润和王文静,2021,《同业业务对中小银行系统性风险溢出的影响》,《南方金融》第8期,第40~52页。 [5]杨子晖、张平淼和林师涵,2022,《系统性风险与企业财务危机预警——基于前沿机器学习的新视角》,《金融研究》第8期,第152～170页。 [6]杨子晖和李东承,2021,《系统性风险指标是否具有前瞻性的预测能力？》,《经济学(季刊)》第2期,第617～644页。 [7]杨子晖和周颖刚,2018,《全球系统性金融风险溢出与外部冲击》,《中国社会科学》第12期,第69～90+200～201页。 [8]Acemoglu, D., A. Ozdaglar, and A. Tahbaz-Salehi, 2015, “Systemic Risk and Stability in Financial Networks”, American Economic Review, 105(2), pp.564 ～ 608. [9]Benoit, S., J. E. Colliard, C. Hurlin and C. Pérignon, 2017, “Where the Risks Lie: A Survey on Systemic Risk”, Review of Finance, 21(1), pp.109 ～ 152. [10]Debarsy, N., C. Dossougoin, C. Ertur and J. Y. Gnabo., 2018, “Measuring Sovereign Risk Spillovers and Assessing the Role of Transmission Channels: A Spatial Econometrics Approach”, Journal of Economic Dynamics and Control, 87, pp.21 ～ 45. [11]Even-Tov, O., 2017, “When Does the Bond Price Reaction to Earnings Announcements Predict Future Stock Returns？”, Journal of Accounting and Economics, 64(1), pp. 167 ～ 182. [12]Giglio, S., B. Kelly and S. Pruitt, 2016, “Systemic Risk and the Macroeconomy: An Empirical Evaluation”, Journal of Financial Economics, 119(3), pp.457 ～ 471. [13]Griffin, P. A., H. A. Hong and J. B. Kim, 2016, “Price Discovery in the CDS Market: The Informational Role of Equity Short Interest”, Review of Accounting Studies, 21(4), pp.1116 ～ 1148. [14]Han, B., A. Subrahmanyam and Y. Zhou, 2017, “The Term Structure of Credit Spreads, Firm Fundamentals, and Expected Stock Returns”, Journal of Financial Economics, 124(1), pp.147 ～ 171. [15]Huynh, T. D. and Y. Xia, 2023, “Panic Selling when Disaster Strikes: Evidence in the Bond and Stock Markets”, Management Science, 69(12), pp.7448 ～ 7467. [16]Jiang, S. and H. Fan, 2018, “Credit Risk Contagion Coupling with Sentiment Contagion”, Physica A: Statistical Mechanics and its Applications, 512, pp.186 ～ 202. [17]Kellner, R., M. Nagl and D. Rösch, 2022, “Opening the Black Box-quantile Neural Networks for Loss Given Default Prediction”, Journal of Banking and Finance, 134, 106334. [18]Landry, M., T. P. Erlinger, D. Patschke and C. Varrichio, 2016, “Probabilistic Gradient Boosting Machines for Gefcom2014 Wind Forecasting”, International Journal of Forecasting, 32(3), pp.1061 ～ 1066. [19]Leippold, M., Q. Wang and W. Zhou, 2022, “Machine Learning in the Chinese Stock Market”, Journal of Financial Economics, 145(2), pp.64 ～ 82. [20]Machado, J. A. and J. S. Silva, 2019, “Quantiles via Moments”, Journal of Econometrics, 213(1), pp.145 ～ 173. [21]Mattera, R. and P. Otto,2024, “Network log-ARCH Models for Forecasting Stock Market Volatility”, International Journal of Forecasting, forthcoming. [22]McLean, R. D. and M. Zhao, 2014, “The Business Cycle, Investor Sentiment, and Costly External Finance”, The Journal of Finance, 69(3), pp.1377 ～ 1409. [23]Nagy, G. I., G. Barta, S. Kazi, G. Borbély and G. Simon, 2016, “GEFCom2014: Probabilistic Solar and Wind Power Forecasting Using a Generalized Additive Tree Ensemble Approach”, International Journal of Forecasting, 32(3), pp.1087 ～ 1093. [24]Pang, C. and Y. Wang, 2020, “Stock Pledge, Risk of Losing Control and Corporate Innovation”, Journal of Corporate Finance, 60, 101534. [25]Petmezas, D. and D. Santamaria, 2014, “Investor Induced Contagion during the Banking and European Sovereign Debt Crisis of 2007-2012: Wealth Effect or Portfolio Rebalancing？”, Journal of International Money and Finance, 49, pp.401 ～ 424. [26]Petropoulos, A., V. Siakoulis, E. Stavroulakis and N. E. Vlachogiannakis, 2020, “Predicting Bank Insolvencies Using Machine Learning Techniques”, International Journal of Forecasting, 36(3), pp.1092 ～ 1113. [27]Procasky, W. J. and A. Yin, 2022, “Forecasting High-yield Equity and CDS Index Returns: Does Observed Cross-market Informational Flow Have Predictive Power？”, Journal of Futures Markets, 42(8), pp.1466 ～ 1490. [28]Procasky, W. J. and A. Yin, 2023, “The Impact of COVID-19 on the Relative Market Efficiency and Forecasting Ability of Credit Derivative and Equity Markets”, International Review of Financial Analysis, 90, 102926. [29]Scholz, M., S. Sperlich and J. P. Nielsen, 2016, “Nonparametric Long Term Prediction of Stock Returns with Generated Bond Yields”, Insurance: Mathematics and Economics, 69, pp.82 ～ 96. [30]Skintzi, V. D., 2019, “Determinants of Stock-Bond Market Comovement in the Eurozone under Model Uncertainty”, International Review of Financial Analysis, 61, pp.20 ～ 28. [31]Tang, D. Y. and H. Yan, 2010, “Market Conditions, Default Risk and Credit Spreads”, Journal of Banking and Finance, 34(4), pp.743～753. [32]Tolikas, K. and N. Topaloglou, 2017, “Is Default Risk Priced Equally Fast in the Credit Default Swap and the Stock Markets？ An Empirical Investigation”, Journal of International Financial Markets, Institutions and Money, 51, pp.39 ～ 57. [33]Wang, X., Y. Wu and Z. Zhong, 2020, “The Comovements of Stock, Bond, and CDS Illiquidity before, during, and after the Global Financial Crisis”, Journal of Financial Research, 43(4), pp. 965 ～ 998. [34]White, H., T. H. Kim and S. Manganelli, 2015, “VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles”, Journal of Econometrics, 187(1), pp.169 ～ 188. [35]Wu, Q. and X. Yan, 2019, “Capturing Deep Tail Risk via Sequential Learning of Quantile Dynamics”, Journal of Economic Dynamics and Control, 109, 103771.
