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Autor
Chojnowski Michał (Warsaw School of Economics), Dybka Piotr (Warsaw School of Economics)
Tytuł
Is exchange rate moody? forecasting exchange rate with google trends data
Źródło
Econometric Research in Finance, 2017, vol. 2, nr 1, s. 1-21, tab., wykr., bibliogr. 13 poz.
Słowa kluczowe
Kurs walutowy, Prognozowanie, Analizy głównych komponentów
Exchange rates, Forecasting, Principal Component Analysis (PCA)
Uwagi
JEL clasiffication: C53, F31, F37, G17
summ.
Abstrakt
This paper proposes a novel method of exchange rate forecasting. We extend the present value model based on observable fundamentals by including three unobserved fundamentals: credit-market, nancial-market, and price-market sentiments. We develop a method of sentiments extraction from Google Trends data on searched queries for different markets. Our method is based on evolutionary algorithms of variable selection and principal component analysis (PCA). Our results show that the extended vector autoregressive model (VAR) which includes markets' sentiment, shows better forecasting capabilities than the model based solely on fundamental variables or the random walk model (naïve forecast).(original abstract)
Dostępne w
Biblioteka Szkoły Głównej Handlowej
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Bibliografia
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  1. Angeletos, G.-M. (2008). Idiosyncratic Sentiments and Coordination Failures. MIT Department of Economics Working Paper 08-12.
  2. Askitas, N. and Zimmermann, K. (2009). Google Econometrics and Unemployment Forecasting. IZA Discussion Papers 4201, Institute for the Study of Labor (IZA).
  3. Ca' Zorzi, M., Mućk, J., and Rubaszek, M. (2016). Real Exchange Rate Forecasting and PPP: This Time the Random Walk Loses. Open Economies Review, 27(3):585{609.
  4. Choi, H. and Varian, H. (2012). Predicting the Present with Google Trends. The Economic Record, 88(s1):2{9.
  5. D'Amuri, F. and Marcucci, J. (2012). The predictive power of Google searches in forecasting unemployment. Temi di discussione (Economic working papers) 891, Bank of Italy, Economic Research and International Relations Area.
  6. Diebold, F. and Mariano, R. (1995). Comparing Predictive Accuracy. Journal of Business and Economic Statistics, 13(3):253{63.
  7. Engel, C. and West, K. (2005). Exchange Rates and Fundamentals. Journal of Political Economy, 113(3):485{517.
  8. Garratt, A. and Mise, E. (2014). Forecasting exchange rates using panel model and model averaging. Economic Modelling, 37(C):32{40.
  9. Ince, O. (2014). Forecasting exchange rates out-of-sample with panel methods and real-time data. Journal of International Money and Finance, 43:1 { 18.
  10. Ko, H.-H. and Ogaki, M. (2015). Granger causality from exchange rates to fundamentals: What does the bootstrap test show us? International Review of Economics and Finance, 38(C):198{206.
  11. Mark, N. and Sul, D. (2012). When Are Pooled Panel-Data Regression Forecasts of Exchange Rates More Accurate than the Time-Series Regression Forecasts? In J. James, I. W. M. and Sarno, L., editors, Handbook of Exchange Rates, chapter When are pooled paneldata regression forecasts of exchange rates more accurate than the time-series regression forecasts?, pages 265{281. Wiley-Blackwell.
  12. McLaren, N. and Shanbhogue, R. (2011). Using internet search data as economic indicators. Bank of England Quarterly Bulletin, 51(2):134{140.
  13. Morales-Arias, L. and Moura, G. (2013). Adaptive forecasting of exchange rates with panel data. International Journal of Forecasting, 29(3):493{509.
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ISSN
2451-1935
Język
eng
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