Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/96594
標題: 探討技術分析於GARCH模型預測外匯波動度之成效-以日幣、墨西哥比索為例
Examining the Performance of FX Volatility Forecasting in Using Technical Analysis-Evidence from Japanese Yen and Mexican Peso
作者: 陳怡安
Yi-An Chen
關鍵字: 外匯;技術分析指標;GARCH 模型;波動率預測;逐步預測力優劣檢定法;Foreign Exchange;GARCH Model;SSPA Test;Technical Analysis;Volatility Forecasting
引用: 翁慈宗(2009),資料探勘的發展與挑戰。科學發展,422,33-35。 Alberg, D., Shalit, H., and Yosef, R. (2008). 'Estimating stock market volatility using asymmetric GARCH models.' Applied Financial Economics, 18(15), 1201-1208. Alexander, S. S. (1961). 'Price movements in speculative markets: Trends or random walks.' Industrial Management Review (pre-1986), 2(2), 7-26. Andersen, T.G. and Bollerslev, T. (1998). 'Answering the skeptics: Yes, standard volatility models do provide accurate forecasts.' International economic review, 39(4), 885-905. Andersen, T. G., Bollerslev, T., Diebold, F. X. and Labys, P. (2000). 'Exchange rate returns standardized by realized volatility are (nearly) Gaussian.' Multinational Finance Journal, 4, 159-179. Andersen, T.G., Bollerslev, T., Diebold, F.X. and Labys, P. (2003). 'Modeling and Forecasting Realized Volatility.' Econometrica, 71(2), 529-626. Bollerslev, T. (1986). 'Generalized autoregressive conditional heteroskedasticity.' Journal of Econometrics, 31(3), 307-327. Brock, W., Lakonishok, J. and LeBaron, B. (1992). 'Simple technical trading rules and the stochastic properties of stock returns.' Journal of Finance, 47(5), 1731-1764. Chordia, T., Goyal, A. and Saretto, A. (2017). 'p-hacking:Evidence from two million trading strategies.' Swiss Finance Institute Research, 17-37. Efron, B. (1979). 'Bootstrap methods: another look at the jackknife.' The Annals of Statistics, 7(1), 1-26. Engle, R.F. (1982). 'Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation.' Econometrica, 50(4), 987-1008. Fama, E.F. (1970). 'Efficient capital markets: A review of theory and empirical work.' The Journal of Finance, 25(2), 383-417. Gartley, H. M. (1935). Profits in the stock market. Health Research Books. Gokcan, S. (2000). 'Forecasting volatility of emerging stock markets: linear versus non‐linear GARCH models' Journal of Forecasting, 19(6), 499-504. Hansen, P .R. (2005). 'A test for superior predictive ability.' Journal of Business and Economic Statistics, 23(4), 365-380. Hansen, P .R. and Lunde, A. (2005). 'A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?' Journal of Applied Econometrics, 20(7), 873 889. Hsu, P .H., Hsu Y .C. and Kuan C.M. (2010). 'Testing the predictive ability of technical analysis using a new stepwise test without data-snooping bias,' Journal of Empirical Finance, 17(3), 471-484. Klaassen, F. (2002). 'Improving GARCH volatility forecasts with regime-switching GARCH.' Empirical Economics, 27(2), 363-394. LeBaron, B. (1999). 'Technical trading rule profitability and foreign exchange intervention' Journal of International Economics, 49(1), 125-143. Lo, A. W., Mamaysky, H. and Wang, J. (2000). 'Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation.' The Journal of Finance, 55(4), 1705-1765. Neely, C. J. and Weller, P. A. (2003). 'Intraday technical trading in the foreign exchange market.' Journal of International Money and Finance, 22(2), 223-237. Nelder, J.A. and Mead, R. (1965). 'A simplex method for function minimization.' The Computer Journal, 7 (4), 308-313. Politis, D. N. and Romano, J. P. (1992). 'A circular block-resampling procedure for stationary data.' Exploring the limits of bootstrap, 263-270. Politis, D.N. and Romano, J.P . (1994). 'The stationary bootstrap.' Journal of the American Statistical Association, 89(428), 1303-1313. Romano, J.P . and Wolf. M. (2005). 'Stepwise multiple testing as formalized data snooping.' Econometrica, 73(4), 1237–1282. Romano, J.P . and Wolf. M. (2007). 'Control of generalized error rates in multiple testing.' The Annals of Statistics, 35(4), 1378–1408. Sullivan, R., Timmermann, A. and White, H. (1999). 'Data-snooping, technical trading rule performance, and the bootstrap.' Journal of Finance, 54(5), 1647-1691. Xie, H. and Li, J. (2010). 'Intraday volatility analysis on S&P 500 stock index future.' International Journal of Economics and Finance, 2(2), 26-34. White, H. (2000). 'A reality check for data snooping.' Econometrica, 68(5), 1097- 1126. Wyckoff, R.D. (1910). 'Studies in tape reading.' (Fraser Publishing Company, Burlington, Vermont).
摘要: 
本研究之主要目的為探討使用技術分析於 GARCH 模型,是否改善對於
外匯波動率之預測能力。首先,是以實現波動率為數據資料,採用四大類技術分析指標,如為移動平均線(Moving Average,以下簡稱 MA)、濾嘴法則(Filter Rules,以下簡稱 FR)、支撐與壓力(Support and Resistance,以下簡稱 SR)以及通道突破(Channel Breakout,以下簡稱 CB),使用技術交易規則來產生交易訊號,將其應用於兩種外匯匯率,包括成熟市場的日幣與新興市場的墨西哥比索,再將交易訊號加入於以日報酬率為數據資料的 GARCH 模型之條件變異數內進行參數估計,估計方法採用最大概似估計法(Maximum likelihood),並選擇逐日滾動之方法進行預測。最後,為了避免資料探勘偏誤之問題,選擇逐步預測力優劣檢定挑出可擊敗 GARCH 基準模型之本研究模型,其中將 MAE 和 MSE 作為評價基準建立損失函數。實證結果發現本研究使用技術分析調整後,在成熟市場與新興市場皆有少部分競爭模型對波動度之預測可顯著擊敗GARCH 基準模型。

The main purpose of this paper is to explore technical analysis to improve GARCH model of predictive ability in the foreign exchange volatility. First, we use realized volatility as data and apply four technical indicators to produce technical trading signals. For example, Moving Average, Filter Rules, Support and Resistance and Channel Breakout. And we can apply to two different foreign exchange rates, including the mature markets of JPY and emerging markets of MXN. Then we add these trading signals to the conditional variance of GARCH model with daily return data and the parameter estimation is performed. For estimating the parameters of GARCH model, estimation method uses the Maximum likelihood estimation and rolling-window forecast method. Finally, in order to avoid the data snooping problem, we choose the SSPA test to evaluate the effectiveness of forecasting, by using the measurements of MAE and MSE as two evaluation criteria to build up the
loss functions. After adjusting GARCH model, our results show adjusted GARCH model in mature market and emerging markets can outperform the benchmark model.
URI: http://hdl.handle.net/11455/96594
Rights: 同意授權瀏覽/列印電子全文服務,2020-07-26起公開。
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