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Exchange Rate Predictability and a Monetary Model with Time-varying Cointegration Coefficients

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  • Cheolbeom Park

    (Department of Economics, Korea University, Seoul, Republic of Korea)

  • Sookyung Park

    (Department of Economics, Korea University, Seoul, Republic of Korea)

Abstract

Many studies have pointed out that the underlying relations and functions for the monetary model (e.g. the PPP relation, the money demand function, monetary policy rule, etc.) have undergone parameter instabilities and that the relation between exchange rates and macro fundamentals are unstable due to the shift in the economic models in foreign exchange traders’ views or the scapegoat effect in Bacchetta and van Wincoop (2009). Facing this, we consider a monetary model with time-varying cointegration coefficients in order to understand exchange rate movements. We provide statistical evidence against the standard monetary model with constant cointegration coefficients but find favorable evidence for the time-varying cointegration relationship between exchange rates and monetary fundamentals. Furthermore, we demonstrate that deviations between the exchange rate and fundamentals from the time-varying cointegration relation have strong predictive power for future changes in exchange rates through in-sample analysis, out-of-sample analysis, and directional accuracy tests.

Suggested Citation

  • Cheolbeom Park & Sookyung Park, 2013. "Exchange Rate Predictability and a Monetary Model with Time-varying Cointegration Coefficients," Discussion Paper Series 1302, Institute of Economic Research, Korea University.
  • Handle: RePEc:iek:wpaper:1302
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    3. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    4. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2016. "Exchange rate predictability in a changing world," Journal of International Money and Finance, Elsevier, vol. 62(C), pages 1-24.
    5. de Truchis, Gilles & Dell’Eva, Cyril & Keddad, Benjamin, 2017. "On exchange rate comovements: New evidence from a Taylor rule fundamentals model with adaptive learning," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 82-98.
    6. Funashima, Yoshito, 2020. "Money stock versus monetary base in time–frequency exchange rate determination," Journal of International Money and Finance, Elsevier, vol. 104(C).
    7. Matteo Barigozzi & Antonio M. Conti, 2018. "On the Stability of Euro Area Money Demand and Its Implications for Monetary Policy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(4), pages 755-787, August.
    8. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    9. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. ""An application of deep learning for exchange rate forecasting"," IREA Working Papers 202201, University of Barcelona, Research Institute of Applied Economics, revised Jan 2022.
    10. Joscha Beckmann & Michael Kühl, 2017. "The Role for Long-run Target Values of the Exchange Rate in the Bank of Japan's Policy Reaction Function," The World Economy, Wiley Blackwell, vol. 40(9), pages 1836-1865, September.
    11. Yunjung Kim & Cheolbeom Park, 2020. "Are exchange rates disconnected from macroeconomic variables? Evidence from the factor approach," Empirical Economics, Springer, vol. 58(4), pages 1713-1747, April.
    12. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Does investor attention matter? The attention-return relationships in FX markets," Economic Modelling, Elsevier, vol. 68(C), pages 644-660.
    13. Gogolin, Fabian & Kearney, Fearghal & Lucey, Brian M. & Peat, Maurice & Vigne, Samuel A., 2018. "Uncovering long term relationships between oil prices and the economy: A time-varying cointegration analysis," Energy Economics, Elsevier, vol. 76(C), pages 584-593.
    14. Jeyhun I. Mikayilov & Fakhri J. Hasanov & Marzio Galeotti, 2018. "Decoupling of C02 Emissions and GDP: A Time-Varying Cointegration Approach," IEFE Working Papers 101, IEFE, Center for Research on Energy and Environmental Economics and Policy, Universita' Bocconi, Milano, Italy.
    15. Neto, David, 2014. "The FMLS-based CUSUM statistic for testing the null of smooth time-varying cointegration in the presence of a structural break," Economics Letters, Elsevier, vol. 125(2), pages 208-211.
    16. Chou, Yu-Hsi, 2018. "Understanding the sources of the exchange rate disconnect puzzle: A variance decomposition approach," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 267-287.

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    More about this item

    Keywords

    Exchange rate; Monetary model; Predictability; Time-varying cointegration;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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