Estimation of Long Memory in Integrated Variance

REFERENCES

• Andersen , T. G. , Benzoni , L. , Lund , J. ( 2002 ). An empirical investigation of continuous-time equity returns models . Journal of Finance 57 : 1239 – 1284 .
   Web of Science ®Google Scholar
• Andersen , T. G. , Bollerslev , T. , Diebold , F. X. , Ebens , H. ( 2001a ). The distribution of stock return volatility . Journal of Financial Economics 61 : 43 – 76 .
   Web of Science ®Google Scholar
• Andersen , T. G. , Bollerslev , T. , Diebold , F. X. , Labys , P. ( 2001b ). The distribution of exchange rate volatility . Journal of the American Statistical Association 96 : 42 – 55 .
   Web of Science ®Google Scholar
• Andersen , T. G. , Bollerslev , T. , Diebold , F. X. , Labys , P. ( 2003 ). Modeling and forecasting realized volatility . Econometrica 71 : 579 – 625 .
   Web of Science ®Google Scholar
• Arteche , J. ( 2004 ). Gaussian semiparametric estimation in long memory in stochastic volatility and signal plus noise models . Journal of Econometrics 119 ( 1 ): 131 – 154 .
   Web of Science ®Google Scholar
• Arteche , J. ( 2012 ). Semiparametric inference in correlated long-memory signal plus noise models . Econometric Reviews 31 : 440 – 474 .
   Web of Science ®Google Scholar
• Baillie , R. T. ( 1996 ). Long memory processes and fractional integration in econometrics . Journal of Econometrics 73 ( 1 ): 5 – 59 .
   Web of Science ®Google Scholar
• Bandi , F. , Russell , J. R. ( 2006 ). Separating market microstructure noise from volatility . Journal of Financial Economics 79 : 655 – 692 .
   Web of Science ®Google Scholar
• Bandi , F. , Russell , J. R. ( 2008 ). Microstructure noise, realized volatility, and optimal sampling . Review of Economic Studies 75 ( 2 ): 339 – 369 .
   Web of Science ®Google Scholar
• Barndorff-Nielsen , O. E. , Shephard , N. ( 2002a ). Econometric analysis of realized volatility and its use in estimating stochastic volatility models . Journal of Royal Statistical Society, Series B 64 : 253 – 280 .
   Google Scholar
• Barndorff-Nielsen , O. E. , Shephard , N. ( 2002b ). Estimating quadratic variation using realized variance . Journal of Applied Econometrics 17 ( 5 ): 457 – 477 .
   Web of Science ®Google Scholar
• Bollerslev , T. , Mikkelsen , H. O. (1996). Modeling and pricing long memory in stock market volatility. Journal of Econometrics 73:151–184.
   Web of Science ®Google Scholar
• Casas , I. , Gao , J. ( 2008 ). Econometric estimation in long-range dependent volatility models: theory and practice . Journal of Econometrics 147 ( 1 ): 72 – 83 .
   Web of Science ®Google Scholar
• Chambers , M. J. ( 1996 ). The estimation of continuous parameter long-memory time series models . Econometric Theory 12 : 374 – 390 .
   Web of Science ®Google Scholar
• Comte , F. ( 1996 ). Simulation and estimation of long memory continuous time models . Journal of Time Series Analysis 17 : 19 – 36 .
   Google Scholar
• Comte , F. , Coutin , L. , Renault , E. ( 2010 ). Affine fractional stochastic volatility models . Annals of Finance 0 : 1 – 42 .
   Google Scholar
• Comte , F. , Renault , E. ( 1998 ). Long-memory in continuous-time stochastic volatility models . Mathematical Finance 8 : 291 – 323 .
   Web of Science ®Google Scholar
• Dacorogna , M. , Muller , U. A. , Nagler , R. J. , Olsen , R. B. , Pictet , O. V. ( 1993 ). A geographical model for the daily and weekly seasonal volatility in the foreign exchange market . Journal of International Money and Finance 12 : 413 – 438 .
   Web of Science ®Google Scholar
• Davidson , J. , Hashimzade , N. ( 2009 ). Type i and type ii fractional brownian motions: A reconsideration . Computational Statistics & Data Analysis 53 ( 6 ): 2089 – 2106 .
   Web of Science ®Google Scholar
• Deo , R. S. , Hurvich , C. M. ( 2001 ). On the log periodogram regression estimator of the memory parameter in long memory stochastic volatility models . Econometric Theory 17 ( 4 ): 686 – 710 .
   Web of Science ®Google Scholar
• Ding , Z. , Granger , C. , Engle , R. ( 1993 ). A long memory property of stock market returns and a new model . Journal of Empirical Finance 1 : 83 – 106 .
   Google Scholar
• Frederiksen , P. , Nielsen , F. S. , Nielsen , M. Ø. ( 2008 ). Local polynomial whittle estimation of perturbed fractional processes. CREATES Research Papers 2008-29, School of Economics and Management, University of Aarhus .
   Google Scholar
• Geweke , J. , Porter-Hudak , S. ( 1984 ). The estimation and application of long memory time series models . Journal of Time Series Analysis 4 : 221 – 238 .
   Google Scholar
• Granger , C. , Ding , Z. ( 1996 ). Modeling volatility persistence of speculative returns . Journal of Econometrics 73 : 185 – 215 .
   Web of Science ®Google Scholar
• Haldrup , N. , Nielsen , M. Ø. ( 2007 ). Estimation of fractional integration in the presence of data noise . Computational Statistics & Data Analysis 51 ( 6 ): 3100 – 3114 .
   Web of Science ®Google Scholar
• Hansen , P. R. , Lunde , A. ( 2005 ). A realized variance for the whole day based on intermittent high-frequency data . Journal of Financial Econometrics 3 ( 4 ): 525 – 554 .
   Google Scholar
• Hansen , P. R. , Lunde , A. ( 2006 ). Realized variance and market microstructure noise . Journal of Business and Economic Statistics 24 ( 2 ): 127 – 161 .
   Web of Science ®Google Scholar
• Hansen , P. R. , Lunde , A. ( 2010 ). Estimating the persistence and the autocorrelation function of a time series that is measured with error. Research Paper 2010-8, CREATES .
   Google Scholar
• Harvey , A. C. ( 1998 ). Long memory in stochastic volatility . In: Knight , J. , Satchell , S. , eds. Forecasting Volatility in Financial Markets . London : Butterworth-Heinemann .
   Google Scholar
• Hurvich , C. , Ray , B. ( 1995 ). Estimation of the memory parameter for nonstationary or noninvertible fractionally integrated processes . Journal of Time Series Analysis 16 : 17 – 42 .
   Google Scholar
• Hurvich , C. M. , Moulines , E. , Soulier , P. ( 2005 ). Estimating long memory in volatility . Econometrica 73 ( 4 ): 1283 – 1328 .
   Web of Science ®Google Scholar
• Hurvich , C. M. , Ray , B. K. ( 2003 ). The local whittle estimator of long-memory stochastic volatility . Journal of Financial Econometrics 1 ( 3 ): 445 – 470 .
   Google Scholar
• Marinucci , D. , Robinson , P. M. ( 1999 ). Alternative forms of fractional brownian motion . Journal of Statistical Planning and Inference 80 : 111 – 122 .
   Web of Science ®Google Scholar
• Martens , M. , Van Dijk , D. , De Pooter , M. ( 2009 ). Forecasting s&p 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements . International Journal of Forecasting 25 : 282 – 303 .
   Web of Science ®Google Scholar
• McAleer , M. , Medeiros , M. ( 2008 ). Realized volatility: a review . Econometric Reviews 27 ( 1–3 ): 10 – 45 .
   Web of Science ®Google Scholar
• Meddahi , N. ( 2002 ). A theoretical comparison between integrated and realized volatility . Journal of Applied Econometrics 17 : 475 – 508 .
   Web of Science ®Google Scholar
• Nielsen , F. S. ( 2008 ). Local polynomial whittle estimation covering non-stationary fractional processes. CREATES Research Papers 2008-28, School of Economics and Management, University of Aarhus .
   Google Scholar
• Nielsen , M. Ø. , Frederiksen , P. ( 2008 ). Finite sample accuracy and choice sampling frequency in integrated volatility estimation . Journal of Empirical Finance 15 ( 15 ): 265 – 286 .
   Google Scholar
• Solo , V. (1992). Intrinsic random functions and the paradox of 1/f noise. SIAM Journal on Applied Mathematics 52:280–291.
   Google Scholar
• Sun , Y. , Phillips , P. C. B. ( 2003 ). Nonlinear log-periodogram regression for perturbed fractional processes . Journal of Econometrics 115 ( 2 ): 355 – 389 .
   Web of Science ®Google Scholar
• Velasco , C. ( 1999a ). Gaussian semiparametric estimation of non-stationary time series . Journal of Time Series Analysis 20–1 : 87 – 127 .
   Google Scholar
• Velasco , C. ( 1999b ). Non-stationary log-periodogram regression . Journal of Econometrics 91 ( 2 ): 325 – 371 .
   Web of Science ®Google Scholar
• Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/lecr
   Google Scholar