Please use this identifier to cite or link to this item:
|Title: ||Google 的搜尋熱度是否有助於預測匯率？|
Does Google Trends-ruled Exchange Rate Predictions Beat Random Walk?
|Issue Date: ||2019-08-07 15:51:14 (UTC+8)|
|Abstract: ||在匯率預測的研究上，Meese and Rogoff（1983）發現基本面模型的預測力甚至不及隨機漫步模型，經歷了多方研究的驗證後，隨機漫步模型逐漸立於不敗之地。Rossi（2013）從這些研究歸結出兩點：只有在長天期預測時，基本面模型才能打敗隨機漫步，且其預測能力因時而變。本文以創新的方式，試圖捕捉因時而變的市場訊號。|
Since Meese and Rogoff (1983), fundamental models’ inabilities to beat random walk in exchange rate predictions have been widely documented. It is concluded by Rossi (2013) that the fundamental models can only beat random walk at long horizons, and the predictive ability at most be time-varying and occasional. Our research invokes another new attempt that deals directly with the varying predictive ability for fundamental models across time.
We ask whether the time-varying nature can be tracked straightforward, when signaling to market. If the signals can be detected and received, there is a higher chance to improve the exchange rate predictability. Tapping into the information extracted from Google Trends, we check if the signals are captured and reflected. Unlike past studies where predictions were usually conducted at medium-to-long horizons, we focus on out-of-sample daily predictions at short horizons. Given the observable and obtainable real time Google Trends index (GTI), we justify the high forecast frequency. Aside from that, our predictions greatly differentiate from past studies with an easy yet novel 2-layer approach following an aggregated result. For the 2-layer approach, we have GTI-ruled model selection in the first layer and predictive models in the second layer. Rather than evaluating the performance in statistical sense, our study places an emphasis on that of the directions of change.
The results for the 2-layer predictions though reaffirm the time-varying nature, by counting the statistical success and failure, a higher rate of beating the random walk confirms GTI’s aggregate power in tracking the such nature. The aggregated predictions further legitimate the use of Google Trends search intensity in capturing such time-varying nature at short horizons.
|Reference: ||Bacchetta, P. and Van Wincoop, E. (2003), “Can Information Heterogeneity Explain the Exchange Rate Determination Puzzle?” National Bureau of Economic Research Working Paper No. 9498.|
Bacchetta, P. and Van Wincoop, E. (2004), “A Scapegoat Model of Exchange-Rate Fluctuations,” American Economic Review, vol. 94 (2), pages 114-118.
Brock, W., Lakonishok, J. and LeBaron, B. (1992), “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” The Journal of Finance, 47(5), 1731-1764.
Bulut, L. (2018), “Google Trends and the Forecasting Performance of Exchange Rate Models,” Journal of Forecasting, 37(3), 303-315.
Chinn, M. D. and Meese, R. A. (1995), “Banking on Currency Forecasts: How Predictable Is Change in Money?” Journal of International Economics, vol. 38(1–2), pages 161-178.
Clark, T. E. and West, K. D. (2007), “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models,” Journal of Econometrics, vol. 138(1), pages 291-311.
Da, Z., Engelberg, J. and Gao, P. (2011), “In Search of Attention,” Journal of Finance, vol. 66, issue 5, 1461-1499.
De Grauwe, P. and Grimaldi, M. (2006), “Exchange Rate Puzzles: A Tale of Switching Attractors,” European Economic Review, vol. 50, issue 1, 1-33
Dick, C. D. and Menkhoff, L. (2013), “Exchange Rate Expectations of Chartists and Fundamentalists,” Journal of Economic Dynamics and Control, vol. 37, issue 7, pages 1362-1383.
Engel, C. and Hamilton, J. (1990), “Long Swings in the Dollar: Are They in the Data and Do Markets Know It?” American Economic Review, vol. 80(4), pages 689-713.
Frankel, J. A. and Froot, K. (1990), “Chartists, Fundamentalists, and Trading in the Foreign Exchange Market,” American Economic Review, 80(2), 181-85.
Kahneman, D. (1973), “Attention and Effort,” Prentice-Hall, Englewood Cliffs, NJ.
Kuo, B.-S., Lan, C.-Y. and Yeh, B.-H. (2018), “Carry Trade Strategy in the Presence of Central Bank Interventions: The Economic Value of Fundamentals,” Taiwan Economic Review, 46, 363-399. (in Chinese)
Mark, N. C. (1995), “Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability,” American Economic Review, vol. 85(1), pages 201-218.
Mark, N. C. and Sul, D. (2001), “Nominal Exchange Rates and Monetary Fundamentals: Evidence from a Small Post-Bretton Woods Panel,” Journal of International Economics, vol. 53(1), pages 29-52.
Markiewicz, A., Verhoeks, R., Verschoor, W. and Zwinkels, R. (2017), “The Winner Takes it All: Predicting Exchange Rates with Google Trend,” SSRN Electronic Journal, 10.2139/ssrn.3020932.
Meese, R. and Rogoff, K. (1983), “Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?” Journal of International Economics, vol. 14(1-2), pages 3-24.
Molodtsova, T. and Papell, D. H. (2009), “Out-of-sample Exchange Rate Predictability with Taylor Rule Fundamentals,” Journal of International Economics, vol. 77(2), pages 167-180.
Rime, D., Sarno, L. and Sojli, E. (2010), “Exchange Rate Forecasting, Order Flow and Macroeconomic Information,” Journal of International Economics, vol. 80(1), pages 72-88.
Rossi, B. (2013), “Exchange Rate Predictability,” CAFE Research Paper No. 13.16.
Spronk, R., Verschoor, W. F. C. and Zwinkels, R. C. J. (2013), “Carry trade and foreign exchange rate puzzles,” European Economic Review, vol. 60(C), pages 17-31.
|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0107351005|
|Data Type: ||thesis|
|Appears in Collections:||[國際經營與貿易學系 ] 學位論文|
Files in This Item:
All items in 政大典藏 are protected by copyright, with all rights reserved.