The study re-examines the exchange rate disconnect puzzle by lending support to that stance that among the causes of the puzzle is poor measurement of fundamentals that are used to predict exchange rate. Engel et al. (2015) show that exchange rate has inherent information that is difficult to extract from macroeconomic fundamentals. Thus, there is the need to circumvent the problem of accurately measuring the fundamentals. In what has become the norm in the literature, information is extracted from exchange rate which is used to predict exchange rate. Hence, studies have commonly resorted to the use of factor model.
Several theories have been used to forecast exchange rate. However, the portfolio balance theory is recently gaining attention. Despite the relative satisfactory performance of this theory, it has been observed that the important role of Global Financial Cycle has not been accounted for. Accounting for this feature is similar to solving the “scapegoat” effect. Thus, this study hypothesizes that Global financial cycle should be used as: (i) proxy for capital flows and (ii) predictor for exchange rate. The objective of the study is to forecast exchange rate.
This objective is achieved in a two-step approach. In the first step, we extract factors and construct the global financial cycle based on four types of capital flows. The second stage dwells on using results from the first stage as predictors for exchange rate.
The study builds a dataset of 20 developed and emerging countries for the period 1990Q1-2017Q2. The Empirical evidences suggest that our approach to forecast exchange rate
21 is able to beat the benchmark random walk model. The performance of all the models is quite impressive. For instance, the PI, OI and BANK models are able to, on the average, accurately predict 14, 17 and 17 bilateral exchange rates against the USD, respectively. Also, our results show that the performance of our model is more short term inclined. However, the performance of the late sample size is quite lower (in terms of Theil U statistics) for all the models.
Accounting for statistical properties of the series in the model significantly improves the predictive prowess of the models. It is safe to conclude that the exchange rate premium puzzle is caused for poor measurement of the fundamentals. Once this problem is accounted for, the puzzle fizzles out, at worst or disappears, at best.
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Appendix: Data Description and Source
Variable Description/Definition Measure Source
Exchange Rate Bilateral exchange rate between a country and the United States (U.S). Exchange rate is defined as the number of units of local currency per one American Dollar (USD).
Log International Financial
Statistics (IFS)
FDI Foreign Direct Investment Inflow
“… cross-border investments associated with a resident in one economy having control or a
Portfolio Investment “…cross-border transactions and positions involving debt or equity securities, other than those included in direct investment or reserve asset”.
% of GDP IMF International
Investment Position Statistics
Bank Flows Category of cross-border
investments classified in
27 Other Flows “a residual category that includes
positions and transactions other than those included in direct
VIX Index VIX measures market expectation of near term volatility conveyed by stock index option prices. It is used to measure investors’ risk averseness.
It is an index FRED St. Louis
3 Months T-Bill Interest rate at which Treasury bills with a 3-month maturity are sold on the secondary market.
Money Supply Growth rate of the US M2 money supply
% IFS
Commodity Price Log of the quarter average of oil price (West Texas Intermediate)
% IFS
trade openness; Sum of the log import and export scaled to log of GDP
% IFS
Financial Development
Credit to the private sector scaled to GDP
% IFS
Note: Definition of the types of capital flows are extracted from the sixth edition of the IMF International Investment Position Manual (BPM6)
Source: Author’s computation
28