Abstract
Fluctuations in currency exchange rates have enormous economic impact, affecting a wide range of market participants including governments, banks, corporates and individuals. Tracking currency exchange rates is crucial in many perspectives. While the financial press closely tracks and discusses major currency exchange pairs on a high frequency basis, scientific literature has been mostly silent in studying their patterns. This paper proposes an innovative approach in the endeavor of tracking the yen exchange rate against US Dollar (USDJPY), the second most traded currency pair. The proposed approach applies econometrics and information they in a real-time manner, leveraging the former on compressing high dimensional sparse data and the latter on quantifying nonlinear dependency. Using merely macroeconomics information and strictly avoiding look-ahead bias, the resulting tracking index from this approach achieves significant linear correlation with USDJPY and demonstrates ability to explain about 37% variances in USDJPY fluctuations over a 13-year period. The proposed approach has three phases: “vintage” generation, “vintage” storage and “vintage” transformation. The proposed approach is based on a deployed system which was started building in 2007. Since then, the system has been evolving systematically all the time to adapt to the ever-changing global macroeconomic environments.
The proposed approach is innovative in the following four aspects:
Use a real time database
Cover a broad range of macroeconomic information
Update information in a timely manner
Consider the dependency between countries.
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Notes
- 1.
See Sect. 2 for the definition of “revisions”.
- 2.
See Sect. 2 for the definition of “vintage”.
- 3.
Look-ahead bias occurs when using information or data in a study that would not have been known or available during the period being analyzed.
- 4.
Capital Expenditure.
- 5.
See Sect. 2 for the definition of “alternative data”.
- 6.
The real value is the nominal value adjusted for inflation and other related measures, e.g. the nominal value of gross domestic product is usually adjusted by a deflator to derive its real values.
- 7.
Macroeconomics data is typically not available for today and the immediate past (“ragged edge”) and subject to revision.
- 8.
A stationary time series is one with constant statistical properties such as mean, variance, autocorrelation, etc.
- 9.
Standardization is the process of scaling variables so that they are comparable.
- 10.
The number of privately-owned new houses on which construction has been started in a given period.
- 11.
Permits for new-home construction.
- 12.
Industrial production.
- 13.
The percentage of resources used by corporations and factories to produce goods in manufacturing, mining, and electric and gas utilities.
- 14.
The ISM Non-Manufacturing Index (NMI) is an economic index based on surveys of more than 400 non-manufacturing firms purchasing and supply executives, within 60 sectors across the nation, by the Institute of Supply Management (ISM).
- 15.
Federal Reserve Bank of Richmond.
- 16.
National Association of Home Builders.
- 17.
Short-Term Economic Survey of Enterprises in Japan.
- 18.
The Economy Watchers Current Index measures the current mood of businesses that directly service consumers, such as barbers, taxi drivers, and waiters.
References
Botman, D., Danninger, S., Schiff, J.: Abenomics: lessons learned from two decades of conventional and unconventional monetary policies. In: Can Abenomics succeed? Overcoming the legacy of Japan’s lost decades. IMF (2015)
The New York Times. https://archive.nytimes.com/query.nytimes.com/gst/fullpage-9C02E7DA153AF930A15753C1A9679D8B63.html. Last accessed 23 October 2011
Haselton, M., Nettle D., Andrews, P.: The evolution of cognitive bias. In: The Handbook of Evolutionary Psychology, pp. 724–746. Wiley, New Jersey (2005)
Ito, T., Isard P., Symansky S.: Economic growth and real exchange rate: an overview of the balassa-samuelson hypothesis in Asia. In: Ito, T., Krueger, O (eds.) Changes in Exchange Rates in Rapidly Developing Countries: Theory, Practice, and Policy Issues, pp. 109–28. University of Chicago Press, Chicago (1999)
Sarac, T., Karagoz, K.: Impact of short-term interest rate on exchange rate: the case of Turkey. In: Murat Karagoz, M., Bildirici, M. (eds.) Procedia Economics and Finance 2016, vol. 38, pp. 195–202. https://doi.org/10.1016/S2212-5671(16)30190-3 (2016)
Kwofie, C., Ansah, R.: A study of the effect of inflation and exchange rate on stock market returns in Ghana. Int. J. Math. Math. Sci. https://doi.org/10.1155/2018/7016792 (2018)
McKenzie, R., Gamba, M.: Data and metadata requirements for building a real-time database to perform revisions analysis. In: OECD/Eurostat Task Force on “Performing Revisions Analysis for Sub-Annual Economic Statistics (2008)
Bouwman, K., Jacobs, J.: Forecasting with real-time macroeconomic data: the ragged-edge problem and revisions. J. Macroecon. (2011)
Harvey, A.: Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge (1989)
Lucas, R.: Understanding business cycles. Carnegie-Rochester Conf. Ser. Publ. Policy 5, 7–29 (1977)
Kullback, S., Leibler, R.: On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951)
Ito, S.: Backward transfer entropy: Informational measure for detecting hidden Markov models and its interpretations in thermodynamics, gambling and causality. Sci. Rep. 6, 36831 (2016)
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Wang, N. (2021). When Econometrics Met Information Theory: A Real-Time Approach to Track Yen Exchange Rate. In: Braha, D., et al. Unifying Themes in Complex Systems X. ICCS 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-67318-5_11
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