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2018 | 10 | nr 1 | 1--25
Tytuł artykułu

Modelling Foreign Exchange Realized Volatility Using High Frequency Data: Long Memory versus Structural Breaks

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, we model realized volatility constructed from intra-day highfrequency data. We explore the possibility of confusing long memory and structural breaks in the realized volatility of the following spot exchange rates: EUR/USD, EUR/JPY, EUR/CHF, EUR/GBP, and EUR/AUD. The results show evidence for the presence of long memory in the exchange rates' realized volatility. FromtheBai-Perrontest,wefoundstructuralbreakpointsthatmatch significant events in financial markets. Furthermore, the findings provide strong evidence in favour of the presence of long memory. (original abstract)
Rocznik
Tom
10
Numer
Strony
1--25
Opis fizyczny
Twórcy
  • University of Bisha and University of Tunis
autor
  • University of Tunis
  • Centre de la Vielle Charité and McGill University
  • University of Tunis
Bibliografia
  • [1] Agiakloglou C., Newbold P., Wohar M. (1993). Bias in an estimator of the fractional difference parameter. Journal of Time Series Analysis, 14(3), 235-246.
  • [2] Aloy M., Boutahar M., Gente K., Péguin-Feissolle A. (2011). Purchasing power parity and the long memory properties of real exchange rates: does one size fit all? Economic Modelling, 28(3), 1279-1290.
  • [3] Andersen T. G., Bollerslev T. (1997). Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns. The Journal of Finance, 52(3), 975-1005.
  • [4] Andersen T. G., Bollerslev T. (1997). Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance, 4(2), 115-158.
  • [5] Andersen T. G., Bollerslev T. (1998). Deutsche mark-dollar volatility: intraday activity patterns, macroeconomic announcements, and longer run dependencies. The Journal of Finance, 53(1), 219-265.
  • [6] Andersen T. G., Bollerslev T., Diebold F. X., Ebens H. (2001). The distribution of realized stock return volatility. Journal of Financial Economics, 61(1), 43-76.
  • [7] Andersen T. G., Bollerslev T., Diebold F. X., Labys P. (2001). The distribution of realized exchange rate volatility. Journal of the American Statistical Association, 96(453), 42-55.
  • [8] Andersen T. G., Bollerslev T., Diebold F. X., Labys P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579-625.
  • [9] Andersen T. G., Bollerslev T., Lange S. (1999). Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon. Journal of Empirical Finance, 6(5), 457-477.
  • [10] Andrews D. W., Guggenberger P. (2003). A bias-reduced log-periodogram regression estimator for the long-memory parameter. Econometrica, 71(2), 675- 712.
  • [11] Bai J., Perron P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.
  • [12] Bai J., Perron P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1-22.
  • [13] Baillie R., Cecen A. A., Han Y. W. (2000). High frequency Deutsche markUS dollar returns: FIGARCH representations and non linearities. Multinational Finance Journal, 4(3/4), 247-267.
  • [14] Baillie R. T. (1996). Long memory processes and fractional integration in econometrics. Journal of Econometrics, 73(1), 5-59.
  • [15] Baillie R.T., Bollerslev T. (1990). A multivariate generalized ARCH approach to modeling risk premia in forward foreign exchange rate markets. Journal of International Money and Finance, 9(3), 309-324.
  • [16] Baillie R. T., Bollerslev T., Mikkelsen H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3-30.
  • [17] Baillie R.T., King M.L.(1996). Editors' introduction: Fractional differencing and long memory processes. Journal of Econometrics, 73(1), 1-3.
  • [18] Beltratti A., Morana C. (2006). Breaks and persistency: macroeconomic causes of stock market volatility. Journal of Econometrics, 131(1), 151-177.
  • [19] Beran J., Ocker D. (1999). SEMIFAR forecasts, with applications to foreign exchange rates. Journal of Statistical Planning and Inference, 80(1), 137-153.
  • [20] Bollerslev T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • [21] Breidt F. J., Crato N., De Lima P. (1998). The detection and estimation of long memory in stochastic volatility. Journal of Econometrics, 83(1), 325-348.
  • [22] Charfeddine L., Guégan, D. (2011). Which is the Best Model for the US Inflation Rate: A Structural Change Model or a Long Memory Process?. The IUP Journal of Applied Economics, 10(1), 5-25.
  • [23] Cheung Y.-W. (1993). Long memory in foreign-exchange rates. Journal of Business & Economic Statistics, 11(1), 93-101.
  • [24] Chiriac R., Voev V. (2011). Modelling and forecasting multivariate realized volatility. Journal of Applied Econometrics, 26(6), 922-947.
  • [25] Choi K., Yu W.-C., Zivot E. (2010). Long memory versus structural breaks in modeling and forecasting realized volatility. Journal of International Money and Finance, 29(5), 857-875.
  • [26] Choi K., Zivot E. (2007). Long memory and structural changes in the forward discount: An empirical investigation. Journal of International Money and Finance, 26(3), 342-363.
  • [27] Chortareas G., Jiang Y., Nankervis J. C. (2011). Forecasting exchange rate volatility using high-frequency data: Is the euro different? International Journal of Forecasting, 27(4), 1089-1107.
  • [28] Corsi F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.
  • [29] Davidson J. (2004). Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model. Journal of Business & Economic Statistics, 22(1), 16-29.
  • [30] Davidson J., Sibbertsen P.(2009). Tests of bias in log-periodogram regression. Economics Letters, 102(2), 83-86.
  • [31] Davidson R., MacKinnon J. G. (2004). Econometric theory and methods, Oxford University Press, New York.
  • [32] Diebold F. X., Husted S. , Rush M. (1991). Real exchange rates under the gold standard. Journal of Political Economy, 99(6), 1252-1271.
  • [33] Diebold F. X., Inoue A. (2001). Long memory and regime switching. Journal of Econometrics, 105(1), 131-159.
  • [34] Ding Z., Granger C.W.(1996). Modeling volatility persistence of speculative returns: a new approach. Journal of Econometrics, 73(1), 185-215.
  • [35] Ding Z., Granger C.W., Engle R.F.(1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83-106.
  • [36] Engel C., Hamilton J. D. (1990). Long swings in the dollar: Are they in the data and do markets know it? The American Economic Review, 80(4), 689-713.
  • [37] Fox R., Taqqu M. S. (1986). Large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series. The Annals of Statistics, 14(2), 517-532.
  • [38] French K. R., Schwert G. W., Stambaugh R. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1), 3-29.
  • [39] Gençay R., Dacorogna M., Muller U. A., Pictet O., Olsen R. (2001). An introduction to high-frequency finance, Academic Press, San Diego and London.
  • [40] Geweke J., Porter-Hudak S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4(4), 221-238.
  • [41] Granger C. W., Ding Z. (1996). Varieties of long memory models. Journal of Econometrics, 73(1), 61-77.
  • [42] Granger C. W., Hyung N. (2004). Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns. Journal of Empirical Finance, 11(3), 399-421.
  • [43] Granger C. W., Joyeux R. (1980). An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis, 1(1), 15-29.
  • [44] Granger C. W., Teräsvirta T. (1999). A simple nonlinear time series model with misleading linear properties. Economics Letters, 62(2), 161-165.
  • [45] Hamilton J. D. (1990). Analysis of time series subject to changes in regime. Journal of Econometrics, 45(1), 39-70.
  • [46] Hosking J. (1981). Fractional differencing, Biometrika, 68, 165-176.
  • [47] Hsieh D. A. (1989). Testing for nonlinear dependence in daily foreign exchange rates. Journal of Business, 62(3), 339-368.
  • [48] Hurst H. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770-808.
  • [49] Koopman S. J., Jungbacker B., Hol E. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance, 12(3), 445-475.
  • [50] Kuensch H. R. (1987). Statistical aspects of self-similar processes. Proceedings of the First World Congress of the Bernoulli Society, 1, 67-74.
  • [51] Kumar D., Maheswaran S. (2015). Long memory in Indian exchange rates: an application of power-law scaling analysis. Macroeconomics and Finance in Emerging Market Economies, 8(1-2), 90-107.
  • [52] Lamoureux C.G., Lastrapes W.D.(1990). Heteroskedasticity in stock return data: volume versus GARCH effects. The Journal of Finance, 45(1), 221-229.
  • [53] Lawrance A., Kottegoda N. (1977). Stochastic modelling of riverflow time series. Journal of the Royal Statistical Society. Series A (General), 140(1), 1-47.
  • [54] Lee H.-Y., Chen S.-L. (2006). Why use Markov-switching models in exchange rate prediction? Economic Modelling, 23(4), 662-668.
  • [55] Liu M. (2000). Modeling long memory in stock market volatility. Journal of Econometrics, 99(1), 139-171.
  • [56] Mandelbrot B., Wallis J. (1968). Noah, Joseph and operational hydrology. Water Resources Research, 4(5), 909-918.
  • [57] Mandelbrot B. B., Taqqu M.S. (1979). Robust R/S analysis of long run serial correlation, IBM Thomas J. Watson Research Division.
  • [58] Martens M. (2001). Forecasting daily exchange rate volatility using intraday returns. Journal of International Money and Finance, 20(1), 1-23.
  • [59] McAleer M., Medeiros M. C. (2008). A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries. Journal of Econometrics, 147(1), 104-119.
  • [60] McLeod A. I., Hipel K. W. (1978). Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon. Water Resources Research, 14(3), 491-508.
  • [61] Mikosch T., Starica C. (2000). Is it really long memory we see in financial returns. Extremes and integrated risk management, 12, 149-168.
  • [62] Mikosch T., Starica C. (2004). Changes of structure in financial time series and the GARCH model. Revstat Statistical Journal, 2(1), 41-73.
  • [63] Morana C., Beltratti A.(2004). Structural change and long-range dependence in volatility of exchange rates: either, neither or both? Journal of Empirical Finance, 11(5), 629-658.
  • [64] Morana C., Beltratti A.(2008). Comovements in international stock markets. Journal of International Financial Markets, Institutions and Money, 18(1), 31- 45.
  • [65] Nikolsko-Rzhevskyy A., Prodan R. (2012). Markov switching and exchange rate predictability. International Journal of Forecasting, 28(2), 353-365.
  • [66] Ohanissian A., Russell J. R., Tsay R. S. (2008). True or spurious long memory? Anewtest.JournalofBusiness&EconomicStatistics,26(2),161-175.
  • [67] Perron P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361-1401.
  • [68] Perron P. (1990). Testing for a unit root in a time series with a changing mean. Journal of Business & Economic Statistics, 8(2), 153-162.
  • [69] Perron P., Qu Z. (2007). An analytical evaluation of the log-periodogram estimate in the presence of level shifts. Unpublished Manuscript, Department of Economics, Boston University.
  • [70] Perron P., Qu Z. (2010). Long-memory and level shifts in the volatility of stock market return indices. Journal of Business & Economic Statistics, 28(2), 275-290.
  • [71] Pesaran M.H., Timmermann A.(2004). How costly is it to ignore breaks when forecasting the direction of a time series? International Journal of Forecasting, 20(3), 411-425.
  • [72] Pong S., Shackleton M. B., Taylor S. J., Xu X. (2004). Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models. Journal of Banking & Finance, 28(10), 2541-2563.
  • [73] Pooter M. d., Martens M., Dijk D. v. (2008). Predicting the daily covariance matrix for S&P 100 stocks using intraday data-but which frequency to use? Econometric Reviews, 27(1-3), 199-229.
  • [74] Robinson P. M. (1995). Gaussian semiparametric estimation of long range dependence. The Annals of Statistics, 23(5), 1630-1661.
  • [75] Shimotsu K. (2006). Simple(but effective) tests of long memory versus structural breaks. Queen's Economics Dept. Working Paper, 1101.
  • [76] Taylor S. J., Xu X. (1997). The incremental volatility information in one million foreign exchange quotations. Journal of Empirical Finance, 4(4), 317- 340.
  • [77] Tse Y. K. (1998). The conditional heteroscedasticity of the yen-dollar exchange rate. Journal of Applied Econometrics, 13(1), 49-55.
  • [78] Varneskov R., Voev V. (2013). The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts. Journal of Empirical Finance, 20, 83-95.
  • [79] Varneskov R.T., Perron P.(2015). Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns: Supplementary Appendix. Unpublished Manuscript, Boston University.
  • [80] Velasco C. (1999). Gaussian Semiparametric Estimation of Non-stationary Time Series. Journal of Time Series Analysis, 20(1), 87-127.
  • [81] Vilasuso J.(2002). Forecasting exchange rate volatility. Economics Letters,76(1), 59-64.
  • [82] Whitle P. (1951). Hypothesis testing in time series analysis (Vol. 4), Almqvist & Wiksells.
  • [83] Yalama A., Celik S. (2013). Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market. Economic Modelling, 30, 67-72.
  • [84] Yang K., Chen L. (2014). Realized Volatility Forecast: Structural Breaks, Long Memory, Asymmetry, and Day-of-the-Week Effect. International Review of Finance, 14(3), 345-392.
  • [85] Zivot E., Andrews D. W. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251-270.
Typ dokumentu
Bibliografia
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