Analysis of Multi-scale Volatility Features of Interest Rate in China Based on TEI@I Methodology

Abstract

Abstract: The interest rate reflects the supply and demand relationship of capital market. It also has an important impact on economic growth, the operation of real enterprises, financial products pricing and policy regulation. The fluctuation characteristics of interest rate has been a research focus in both academic area and market area. From the perspective of system management, this paper makes a deep research on various factors which have important impact on interest rate in both money supply and demand system and systematically sorts out theoretical basis of interest rate decision from both macro and micro aspects. The return of 10-year CDB bond is selected as the proxy index of the market-oriented long-term interest rate. Based on the methodology of TEI@I, the EMD model is used to decompose the sequence of interest rate. The results show that, the interest rate has multi-scale features, and each component has different fluctuation characteristics. The long-term and middle long-term trends are mainly affected by the opening policy of Chinese financial markets. The low-frequency cycle is mainly affected by the factors of capital demand side, while the high-frequency cycle is mainly affected by the supply side. Further, the over-expected random disturbances are mostly accompanied by the impact of unexpected events.

Key words: TEI@I, interest rate, multi-scale, EMD, VAR

Cui Xiao, Guo Kun, Jin Zhenni, Yang Gan, Liao Zhewen. Analysis of Multi-scale Volatility Features of Interest Rate in China Based on TEI@I Methodology[J]. Management Review, 2020, 32(7): 111-122.

References

[1] Diebold F. X., Piazzesi M., Rudebusch G. D. Modeling Bond Yields in Finance and Macroeconomics[J]. American Economic Review, 2005,95(2):415-420
[2] Fama E. F. Short-Term Interest Rates as Predictors of Inflation[J]. American Economic Review, 1975,65(3):269-282
[3] Coroneo L., Giannone D., Modugno M. Unspanned Macroeconomic Factors in the Yield Curve[J]. Journal of Business & Economic Statistics, 2016,34(3):472-485
[4] Leeper E. M., Gordon D. B. In Search of the Liquidity Effect[J]. Journal of Monetary Economics, 1992,29(1):341-369
[5] Das S. R. The Surprise Element:Jumps in Interest Rates[J]. Journal of Econometrics, 2002,106(1):27-65
[6] Hong B., Elmer S. Volatility of the Interest Rate, Debt and Firm Investment:Dutch Evidence[J]. Journal of Corporate Finance, 2002,8(2):179-193
[7] Diebold F. X., Li C. Forecasting the Term Structure of Government Bond Yields[J]. Journal of Econometrics, 2005,130(2):337-364
[8] Bali T. G., Wu L. A Comprehensive Analysis of the Short-term Interest Rate Dynamics[J]. Journal of Banking and Finance, 2005,30(4):1269-1290
[9] Brandt M. W., Kavajecz K. A Price Discovery in the US Treasury Market:The Impact of Orderflow and Liquidity on the Yield Curve[J]. The Journal of Finance, 2004,59(6):2623-2654
[10] Andritzky J. R., Bannister G. J., Tamirisa N. T. The Impact of Macroeconomic Announcements on Emerging Market Bonds[J]. Emerging Markets Review, 2006,8(1):1-37
[11] Carr J. Money Supply, Interest Rates and the Yield Curve[J]. Journal of Money, Credit and Banking, 2011,4(3):582-594
[12] 秦宛顺,王永宏. 影响上海国债二级市场收益因素的实证分析[J]. 金融研究, 2000,(5):72-79
[13] 潘冠中,马晓兰. 应该用哪一个模型来描述中国货币市场利率的动态变化[J]. 数量经济技术经济研究, 2006,1(12):54-63
[14] 范龙振,张处. 中国债券市场债券风险溢酬的宏观因素影响分析[J]. 管理科学学报, 2009,12(6):116-124
[15] 张金清,周茂彬. 中国短期利率波动性效应实证比较研究[J]. 系统工程学报, 2010,25(3):320-325
[16] 孙国峰,蔡春春. 货币市场利率、流动性供求与中央银行流动性管理——对货币市场利率波动的新分析框架[J]. 经济研究, 2014,49(12):33-44
[17] 尚玉皇,郑挺国,夏凯. 宏观因子与利率期限结构:基于混频Nelson-Siegel模型[J]. 金融研究, 2015,(6):14-29
[18] 张华庭,夏海波. 基于EGARCH模型对我国金融市场基准利率波动性的实证研究[J]. 现代经济信息, 2015,1(3):305-306
[19] 尚玉皇,郑挺国. 短期利率波动测度与预测:基于混频宏观-短期利率模型[J]. 金融研究, 2016,(11):47-62
[20] 张明,郭子睿,何帆. "钱荒"为什么会发生?——上海银行间同业拆放利率的影响因素分析[J]. 国际金融研究, 2016,(12):84-93
[21] 戴严科,林曙. 利率波动、融资约束与存货投资——来自中国制造业企业的证据[J]. 金融研究, 2017,(4):95-111
[22] 牛慕鸿,张黎娜,张翔. 利率走廊、利率稳定性和调控成本[J]. 金融研究, 2017,(7):16-28
[23] 尚玉皇,郑挺国. 基准收益率曲线与宏观经济:基于混频模型的研究[J]. 经济研究, 2018,53(6):36-51
[24] 董阳,周锐,王浩. 中国国债收益率曲线分析与预测[J]. 经济学报, 2019,(10):1-20
[25] 杨炳铎,汤教泉. 中国债券收益率的可预测性检验[J]. 系统工程理论与实践, 2019,39(4):970-985
[26] Wang S., Yu L., Lai K. K. Crude Oil Price Forecasting with TEI@I Methodology[J]. Journal of Systems Science and Complexity, 2005,(2):145-166
[27] Yan Y., Xu W., Bu H., et al. Method for Housing Price Forecasting Based on TEI@I Methodology[J]. Systems Engineering-Theory & Practice Online, 2007,27(7):1-9
[28] 郭琨,崔啸,王珏,等. 京十二条"房地产调控政策的影响——基于TEI@I方法论[J]. 管理科学学报, 2012,5(4):4-11
[29] 王轩,杨海珍. 人民币汇率波动原因的集成分析与实际有效汇率预测[J]. 金融论坛, 2017,22(8):24-34
[30] Tian X., Liu L., Lai K. K., et al. Analysis and Forecasting of Port Logistics Using TEI@I Methodology[J]. Transportation Planning and Technology, 2013,36(8):685-702
[31] 陈志斌,刘静. 金融危机背景下企业现金流运行中的政策影响研究[J]. 会计研究, 2011,(4):42-49
[32] 钟少颖,王蕊,陈锐. 货币政策,市场预期与房地产价格[J]. 管理评论, 2016,28(5):13-22
[33] Massimo G. The Role of House Prices in the Monetary Transmission Mechanism Across European Countries[J]. Scottish Journal of Political Economy, 2005,52(4):218-229
[34] 易行健,杨碧云. 世界各国地区居民消费率决定因素的经验检验[J]. 世界经济, 2015,38(1):3-24
[35] 赵尚梅,史宏梅,杜华东. 地方政府在城市商业银行的大股东掏空行为——从地方政府融资平台贷款视角的研究[J]. 管理评论, 2013,25(12):32-41
[36] Huang N. E., Shen Z., Long S. R. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[J]. Proceedings of the Royal Society of London. Series A:Mathematical, Physical and Engineering Sciences, 1998,454(1971):903-995
[37] 汤铃,李建平,孙晓蕾,等. 基于模态分解的国家风险多尺度特征分析[J]. 管理评论, 2012,24(8):3-10
[38] 龚旭,文凤华,黄创霞,等. 模型及其对金融资产波动率的预测研究[J]. 管理评论, 2017,29(1):19-32
[39] Box G., Jenkins M. Time Series Forecasting Analysis and Control[M]. San Francisco:Holden Day, 1970
[40] Sims C. A Macroeconomics and Reality[J]. Econometrica:Journal of the Econometric Society, 1980,1(1):1-48