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| Openness and Money Demand: Measuring the Opportunity Cost Effects of International Financial Markets |
| QIN Duo, LU Shan, WANG Huiwen, Sophie van Huellen, WANG Qingchao
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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 |
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Abstract 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 with CIFI-enhanced money demand models yield positive outcomes. Our key findings are as follows: (i) We find strong evidence of the effects of foreign opportunity costs on China's money demand based on the statistical significance and constancy of the coefficients of CIFIs and overall comparisons of model explanatory power; (ii) the effect of the short-run CIFIs is particularly robust, as evidenced by the 2007-2008 US-led financial crisis; however, in the enhanced error-correction term of the long-run CIFIs, a temporary coefficient variation toward insignificance is observed, which is interpreted as resulting from the emergency measures taken by the People's Bank of China in response to the crisis; (iii) model performance comparisons of the CIFIs produced with and without the first step of unsupervised dimension reduction show the necessity of this step in that it helps reduce redundant information in large financial datasets; (iv) tracing the compositions of CIFIs back to individual financial input indicators yields various patterns and features that enable the identification of the sources of the aggregate foreign opportunity cost effects.
The explicit links between disaggregate input indicators and aggregate CIFIs provide valuable tools for policymakers to monitor external financial shocks from different geographical regions and markets and assess their aggregate risks in real time. Our CIFI algorithm opens a novel route of model-based composite construction. This route also sheds light on why the conventional route of principal component-based factor analysis is insufficient to construct composite indices for macro-modeling.
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Received: 28 August 2019
Published: 02 October 2021
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