By Sam van de Schootbrugge 02-09-2020

High-Frequency Capital Flow Proxies Are Good At Predicting BoP Flows

(10 min read)
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Policymakers need timely information on capital flows to inform decisions on monetary policy and foreign exchange intervention. Official balance of payments (BoP) data are of little help, given their long release lag. Instead, high-frequency capital flow proxies can serve as early warning indicators. The availability of such data has improved dramatically over the past two decades, but a lack of transparency from private sector data providers combined with conceptual and measurement issues means there are no comparable datasets.
A new IMF working paper is set out as a guide for those who need timely information on capital flow developments. The paper:
• Provides an overview of high-frequency datasets currently used as proxies for portfolio/capital flows.
• Conducts a meta-analysis on the use of the data sources in the empirical literature.
• Addresses a gap in the availability of free, timely and reliable portfolio flow data.
• Measures how well widely used portfolio flow proxies track portfolio flow data in real-time.
The key conclusions are as follows:
• There are four key capital flow data sources: the IMF BoP Statistics, the BIS Locational and Consolidated Banking Statistics, the IIF Capital Flow Data and Portfolio Flow Trackers, and the EPFR Fund Flow Data.
• A review of the literature shows that academic researchers prefer the IMF balance of payments database, while policymakers favour the high-frequency data sources, such as the Emerging Portfolio Fund Research (EPFR) database.
• In a nowcasting ‘horse race’, the paper shows that all high-frequency portfolio flow proxies have significant predictive content for BoP portfolio flows. Among the various predictors, IIF portfolio flow trackers generally outperform EPFR fund flow data.
On this last result, Figure 1 shows the results from the nowcasting horse race. The capital flow proxies are weekly/monthly EPFR data, daily/weekly IIF data and monthly KP data (explained later). The performance of these proxies at predicting actual equity flows (left) and debt flows (right) is measured by the root mean squared forecasting error (RMSFE) in percentage of GDP – the lower the percentage, the better the estimate.
On both counts, daily/monthly IIF proxies are better high-frequency predictors of actual BoP flows, and the predicting power improves over the quarter. The RHS axis expresses the forecast performance as a share of absolute average quarterly flows. The RMSFEs of debt and equity flows based on daily and monthly IIF data outperform weekly and monthly EPFR data, respectively, by around 20% early in the quarter and 50% at the end of the quarter.

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