Forecasting realised volatility using ARFIMA and HAR models

References

• Andersen, T.G. and Bollerslev, T., Deutsche Mark – Dollar volatility: Intraday activity patterns, macroeconomic announcements. J. Financ., 1998, 53(1), 219–265. doi:10.1111/0022-1082.85732.
   Web of Science ®Google Scholar
• Andersen, T.G., Bollerslev, T. and Diebold, F.X., Roughing it up: Including jump components in the return volatility, 2007, 89(November), 701–720. doi:10.1162/rest.89.4.701.
   Google Scholar
• Andersen, T.G., Bollerslev, T. and Huang, X., A reduced form framework for modeling volatility of speculative prices based on realized variation measures. J. Econom., 2011, 160(1), 176–189. doi:10.1016/j.jeconom.2010.03.029.
   Web of Science ®Google Scholar
• Andersen, T.G., Bollerslev, T., Diebold, F.X. and Labys, P., Exchange rate returns standardized by realized volatility are (nearly) Gaussian. Multi. Financ. J., 2000, 4(September), 159–179. doi:10.3386/w7488.
   Google Scholar
• Andersen, T.G., Bollerslev, T., Diebold, F.X. and Labys, P., Modeling and forecasting realized volatility. Econometrica, 2003, 71, 579–625.
   Web of Science ®Google Scholar
• Andersen, T.G., Bollerslev, T., Diebold, F.X. and Labys, P., The distribution of realized exchange rate volatility. J. Am. Stat. Assoc., 2001, 96(August), 42–55. doi:10.1198/016214501750332965.
   Google Scholar
• Andersen, T.G., Bollerslev, T., Frederiksen, P. and Ørregaard Nielsen, M., Continuous-time models, realized volatilities, and testable distributional implications for daily stock returns. J. Appl. Econometrics, 2010, 25(2), 233–261.
   Web of Science ®Google Scholar
• Areal, N.M.P.C. and Taylor, S.J., The realized volatility of FTSE-100 futures prices. J. Futures Markets, 2002, 22(7), 627–648. doi:10.1002/fut.10018.
   Web of Science ®Google Scholar
• Bandi, F.M. and Perron, B., Long memory and the relation between implied and realized volatility. J. Financ. Economet., 2006, 4(4), 636–670. doi:10.1093/jjfinec/nbl003.
   Google Scholar
• Beine, M., Laurent, S. and Lecourt, C., Official central bank interventions and exchange rate volatility: Evidence from a regime-switching analysis. Eur. Econ. Rev., 2003, 47(5), 891–911. doi:10.1016/S0014-2921(02)00306-9.
   Web of Science ®Google Scholar
• Bollerslev, T. and Mikkelsen, H.O., Modeling and pricing long memory in stock market volatility. J. Econom., 1996, 73(1), 151–184. doi:10.1016/0304-4076(95)01736-4.
   Web of Science ®Google Scholar
• Bollerslev, T. and Wright, J.H., High-frequency data, frequency domain inference, and volatility forecasting. Rev. Econ. Stat., 2001, 83(4), 596–602. doi:10.1162/003465301753237687.
   Web of Science ®Google Scholar
• Bollerslev, T., Patton, A.J. and Quaedvlieg, R., Exploiting the errors: A simple approach for improved volatility forecasting. J. Econom., 2016, 192(1), 1–18. doi:10.1016/j.jeconom.2015.10.007.
   Web of Science ®Google Scholar
• Brownlees, C.T., Engle, R.F. and Kelly, B.T., A Practical Guide to Volatility Forecasting through Calm and Storm. SSRN Elect. J., 2011, 14(2), 1–37. doi:10.2139/ssrn.1502915.
   Google Scholar
• Caporin, M., Rossi, E. and De Magistris, P.S., Chasing volatility - A persistent multiplicative error model with jumps chasing volatility a persistent multiplicative error model with jumps, 2014.
   Google Scholar
• Clements, M.P., Galvão, A.B. and Kim, J.H., Quantile forecasts of daily exchange rate returns from forecasts of realized volatility. J. Empir. Financ., 2008, 15(4), 729–750. doi:10.1016/j.jempfin.2007.12.001.
   Web of Science ®Google Scholar
• Corsi, F., A simple approximate long-memory model of realized volatility. J. Financ. Economet., 2009, 7, 174–196. doi:10.1093/jjfinec/nbp001.
   Web of Science ®Google Scholar
• Corsi, F., Pirino, D. and Reno, R., Threshold bipower variation and the impact of jumps on volatility forecasting. J. Econom., 2010, 159(2), 276–288.
   Web of Science ®Google Scholar
• Deo, R., Hurvich, C. and Lu, Y., Forecasting realized volatility using a long-memory stochastic volatility model: Estimation, prediction and seasonal adjustment. J. Econom., 2006, 131, 29–58. doi:10.1016/j.jeconom.2005.01.003.
   Web of Science ®Google Scholar
• Ding, Z., Granger, C.W.J. and Engle, R.F., A long memory property of stock market returns and a new model. J. Empir. Financ., 1993, 1(1), 83–106. doi:10.1016/0927-5398(93)90006-D.
   Google Scholar
• Dufrenot, G., Guegan, D. and Peguin-Feissolle, A., Changing-regime volatility: A fractionally integrated SETAR model. Appl. Financ. Econ., 2008, 18(7), 519–526. doi:10.1080/09603100600993778.
   Google Scholar
• Fuertes, A.M., Izzeldin, M. and Kalotychou, E., On forecasting daily stock volatility: The role of intraday information and market conditions. Int. J. Forecast., 2009, 25(2), 259–281. doi:10.1016/j.ijforecast.2009.01.006.
   Web of Science ®Google Scholar
• Gourieroux, C. and Jasiak, J. Financial Econometrics, 2001 (Princeton University Press: Princeton, NJ).
   Google Scholar
• Granger, C.W.J. and Joyeux, R., An introduction to long-memory time series models and fractional differencing. J. Time Ser. Anal., 1980, 1(1), 15–29.
   Google Scholar
• Granger, C.W.J. and Terasvirta, T., A simple nonlinear time series model with misleading linear properties. Econ. Lett., 1999, 62(2), 161–165. doi:10.1016/S0165-1765(98)00228-6.
   Web of Science ®Google Scholar
• Hyung, N., Poon, S.H. and Granger, C.W.J., A source of long memory in volatility. Front. Econ. Globaliza., 2008, 3, 329–380. doi:10.1016/S1574-8715(07)00209-6.
   Google Scholar
• Koopman, S.J., Jungbacker, B. and Hol, E., Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. J. Empir. Financ., 2005, 12(3), 445–475. doi:10.1016/j.jempfin.2004.04.009.
   Google Scholar
• LeBaron, B., Stochastic volatility as a simple generator of financial power laws and long memory. Quant. Finance, 2001, 1, 621–631.
   Google Scholar
• Maekawa, K. and Xinhong, L., Long memory in the realized volatility of returns on the Yen / US $ exchange rate during the three financial crises. Working Paper, Hiroshima University of Economics, 2011, 1–24.
   Google Scholar
• Martens, M. and Zein, J., Predicting financial volatility: High-frequency timeseries forecasts vis-‘a-vis implied volatility, J. Futures Markets, 2004, 24(11), 1005–1028.
   Web of Science ®Google Scholar
• Martens, M., Forecasting daily exchange rate volatility using intraday returns. J. Int. Money. Finance., 2001, 20, 1–23. doi:10.1016/S0261-5606(00)00047-4.
   Web of Science ®Google Scholar
• Martens, M., Van Dijk, D. and De Pooter, M., Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements. Int. J. Forecast., 2009, 25(2), 282–303.
   Web of Science ®Google Scholar
• Müller, U. A., Dacorogna, M.M., Davé, R.D., Olsen, R.B., Pictet, O.V. and von Weizsäcker, J.E., Volatilities of different time resolutions – Analyzing the dynamics of market components. J. Empir. Financ., 1997, 4(2–3), 213–239. doi:10.1016/S0927-5398(97)00007-8.
   Google Scholar
• Oomen, R.C.A., Modelling realized variance when returns are serially correlated. Discussion Papers, the open access publication server of the ZBW, 2004. Available online at: http://hdl.handle.net/10419/51076
   Google Scholar
• Oomen, R.C.A., Using high frequency stock market index data to calculate, model and forecast realised return variance. EUI Working Paper, 6, 2001.
   Google Scholar
• Patton, A.J. and Sheppard, K., Optimal combinations of realised volatility estimators. Int. J. Forecast., 2009, 25(2), 218–238. doi:10.1016/j.ijforecast.2009.01.011.
   Web of Science ®Google Scholar
• Patton, A.J., Volatility forecast comparison using imperfect volatility proxies. J. Econom., 2011, 160(1980), 246–256. doi:10.1016/j.jeconom.2010.03.034.
   Google Scholar
• Pong, S., Shackleton, M.B., Taylor, S.J. and Xu, X., Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models. J. Bank. Financ., 2004, 28, 2541–2563. doi:10.1016/j.jbankfin.2003.10.015.
   Web of Science ®Google Scholar
• Ray, B. and Tsay, R., Long-range dependence in daily stock volatilities. J. Bus. Econ. Stat., 2000, 18( January 2013), 254–262. doi:10.2307/1392562.
   Web of Science ®Google Scholar
• Scharth, M. and Medeiros, M.C., Asymmetric effects and long memory in the volatility of Dow Jones stocks. Int. J. Forecast., 2009, 25(2), 304–327. doi:10.1016/j.ijforecast.2009.01.008.
   Web of Science ®Google Scholar
• Syczewska, E.M. Financial crisis influence on the BUX index of Hungarian Stock Exchange. Long memory measures: 1991–2008. Working Paper, Warsaw School of Economics, 2010.
   Google Scholar
• Thomakos, D.D. and Wang, T., Realized volatility in the futures markets. J. Empir. Financ., 2003, 10, 321–353. doi:10.1016/S0927-5398(02)00052-X.
   Google Scholar
• Wang, Y.-H. and Yen, K.-C., The information content of the implied volatility term structure on future returns. SSRN Elect. J., 2016, 1–44. Available online at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2483222
   Google Scholar