经济开放与货币需求:国际金融风险及持币成本的测度  

Openness and Money Demand:Measuring the Opportunity Cost Effects of International Financial Markets

作　　者：秦朵[1] 卢珊 王惠文[3] Sophie van Huellen 王庆超 QIN Duo;LU Shan;WANG Huiwen;Sophie van Huellen;WANG Qingchao(School of Oriental and African Studies,University of London;School of Statistics and Mathematics,Central University of Finance and Economics;School of Economics and Management,Beihang University;Expedia Group)

机构地区：[1]伦敦大学亚非学院,英国伦敦 [2]中央财经大学统计与数学学院,北京100081 [3]北京航空航天大学经济管理学院,北京100191 [4]Expedia Group,英国伦敦

出　　处：《金融研究》2021年第9期30-50,共21页Journal of Financial Research

基　　金：北京凯恩克劳斯经济研究基金会的大力支持;国家自然科学基金资助(基金号:72001222,72021001);中央财经大学的学科建设经费、科研创新团队支持计划、新兴交叉学科建设项目的支持

摘　　要：在中国开放经济体制下的基准货币需求模型中,本文将源于国际金融市场的持币成本设为遗漏潜变量,并构建特定的国际金融综合指数(CIFI)作为该潜变量的测度。借鉴机器学习与测度理论,本文利用对数误差修正模型提出了分步降维的CIFI构造算法,构造了长期CIFI和短期CIFI。结果表明,CIFI构造中的无监督降维步骤有助于减少高维金融数据中的冗余信息。实证分析发现,国际机会成本对中国货币需求具有规律性的前导影响,而在2007至2008年国际金融危机期间,央行的应急措施对长期CIFI所代表的非均衡冲击起到明显的阻截效果,对短期CIFI的影响基本是持续不变的。通过综合指数构造与宏观货币需求模型的算法连接,可以利用CIFI的构成结构从前导时间与影响强度两方面追踪冲击货币需求的国际金融风险的具体来源,这为宏观决策者监测国际金融市场提供了颇有规律的信息。在方法论上,本研究为如何利用模型监测国际金融市场影响宏观经济开辟了一条新路。Standard money demand models neglect the direct effects of economic openness.This omission is problematic when domestic opportunity cost variables fail to fully reflect the dynamics of international financial markets.Examining the effect of this omission is of great practical importance given the ever-increasing openness of China's economy.We propose composite international financial indices(CIFIs)to measure the latent variables that are omitted in standard money demand models.Using techniques from machine learning and measurement theory,we develop a novel model-based approach to construct CIFIs that combines both unsupervised and supervised dimension reduction methods.The choice of the popular error-correction model for the money demand function leads us to construct two types of CIFIs:long-run and short-run CIFIs.We collect a large set of around 100 financial input indicators to construct CIFIs using monthly data for the 1993M9-2015M6 period.These input indicators are obtained from 21 economies,covering almost all of China's major trading partners.The CIFI construction algorithm contains two stages of aggregation.First,it produces composite financial input indicators by aggregating groups of financial indicators.These groups are formed using clustering methods under the unsupervised learning approach.Second,it uses supervised dimension reduction methods to aggregate the composite financial input indicators following the principle of partial least-squares(PLS).The algorithm produces short-run CIFIs by targeting money growth rates,whereas it forms the target of long-run CIFIs using the error-correction term of standard money demand models.The second supervised aggregation stage sets the input indicators as leading indicators by construction,allows for dynamic dis-synchronization among them,and performs dynamic backward selection of different lags to make the dynamic input forms of the leading indicators as simple as possible.Concatenation is imposed on the resulting CIFIs during regular data updates.Experiments w

关 键 词：货币需求 国际金融风险 复合型测度 无监督降维 有监督降维 指数构造 

分 类 号：F125[经济管理—世界经济] F822F831