Estimating the persistence and the autocorrelation function of a time series that is measured with error

Econometric Theory

Volumn 30, Issue 1, 2014, Pages 60-93

  Hansen, Peter R ^a   Lunde, Asger ^b  

^a EUROPEAN UNIVERSITY INSTITUTE   (Italy)

^b AARHUS UNIVERSITY   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84896462761     PISSN: 02664666     EISSN: 14694360     Source Type: Journal    
DOI: 10.1017/S0266466613000121     Document Type: Article

References (59)

1. Anatolyev, S. (2001) Optimal instrumentals in time series: A survey. Journal of Economic Surveys 21, 143-173.
2. Andersen, T.G. & T. Bollerslev (1997) Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns. Journal of Finance 52(3), 975-1005.
3. Andersen, T.G. & T. Bollerslev (1998) Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review 39(4), 885-905.
4. Andersen, T.G., T. Bollerslev, F.X. Diebold, & P. Labys (2003) Modeling and forecasting realized volatility. Econometrica 71(2), 579-625.
5. Andersen, T.G., T. Bollerslev, & N. Meddahi (2004) Analytic evaluation of volatility forecasts. International Economic Review 45, 1079-1110.
6. Andersen, T.G., T. Bollerslev, & N. Meddahi (2005) Correcting the errors: A note on volatility forecast evaluation based on high-frequency data and realized volatilities. Econometrica 73, 279-296.
7. Andersen, T.G., T. Bollerslev, & N. Meddahi, (2011) Market microstructure noise and realized volatility forecasting. Journal of Econometrics 160, 220-234.
8. Anderson, T. & H. Rubin (1949) Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics 20, 46-63.
9. Ashley, R. & D. Vaughan (1986) Measuring measurement error in economic time series. Journal of Business and Economic Statistics 4, 95-103.
10. Barndorff-Nielsen, O.E., P.R. Hansen, A. Lunde, & N. Shephard (2008) Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise. Econometrica 76, 1481-1536.
11. Barndorff-Nielsen, O.E., P.R. Hansen, A. Lunde, & N. Shephard (2009) Realised kernels in practice: Trades and quotes. Econometrics Journal 12, 1-33.
12. Barndorff-Nielsen, O.E., B. Nielsen, N. Shephard, &C. Ysusi (2004) Measuring and forecasting financial variability using realised variance with and without a model. In A.C. Harvey, S.J. Koopman, & N. Shephard (eds.), State Space and Unobserved Components Models: Theory and Applications', pp. 205-235. Cambridge University Press.
13. Barndorff-Nielsen, O.E. & N. Shephard (2002) Econometric analysis of realised volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society B 64, 253-280.
14. Bollerslev, T. & J.H. Wright (2000) Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data. Journal of Econometrics 98, 81-106.
15. Bollerslev, T. & J.H. Wright (2001) High-frequency data, frequency domain inference, and volatility forecasting. Review of Economics & Statistics 83(4), 596-602.
16. Bollerslev, T. & H. Zhou (2002) Estimating stochastic volatility diffusion using conditional moments of integrated volatility. Journal of Econometrics 109, 33-65.
17. Chong, T.T. & G.C. Lui (1999) Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data. Economics Letters 63, 285-194.
18. Ding, Z., C.W.J. Granger, & R.F. Engle, (1993) A long memory property of stock market returns and a new model. Journal of Empirical Finance 1, 83-106.
19. Doornik, J.A. (2006) Ox: An Object-Orientated Matrix Programming Langguage, 5th ed. Timberlake Consultants Ltd.
20. Forsberg, L. & E. Ghysels (2007) Why do absolute returns predict volatility so well? Journal of Financial Econometrics 5, 31-67.
21. Fuller, W.A. (1996) Introduction to Time Series, 2nd ed. Wiley.
22. Ghysels, E. & E. Jacquier (2006) Market Beta Dynamics and Portfolio Efficiency. Working paper. Chapel Hill, NC: University of North Carolina, Department of Economics.
23. Granger, C.W.J. (1980) Long memory relationships and the aggregation of dynamic models. Jounal of Econometrics 14, 227-238.
24. Haldrup, N. & M.O. Nielsen (2007) Estimation of fractional integration in the presence of data noise. Computational Statistics & Data Analysis 51, 3100-3114.
25. Hall, A. ( 1989) Testing for a unit roor in the presence of moving average errors. Biometrika 76, 49-56.
26. Hansen, L.P. (1985) A method for calculating bounds on asymptotic covariance matrices of generalized method of moments estimators. Journal of Econometrics 30, 203-238.
27. Hansen, L.P., J.C. Heaton, & M. Ogaki (1988) Efficiency bounds implied by multiperiod conditional moment restrictions. Journal of the American Statistical Association 83, 863-871.
28. Hansen, L.P. & K.J. Singleton (1996) Efficient estimation of linear asset-pricing models with moving average errors. Journal of Business & Economic Statistics 14, 53-68.
29. Hansen, P.R. & G. Horel, (2009) Quadratic Variation by Markov Chains. CREATES Research Paper 2009-13.
30. Hansen, P.R. & A. Lunde (2005a) A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics 20, 873-889.
31. Hansen, P.R. & A. Lunde (2005b) A realized variance for the whole day based on intermittent highfrequency data. Journal of Financial Econometrics 3, 525-554.
32. Hansen, P.R. & A. Lunde (2006) Consistent ranking of volatility models. Journal of Econometrics 131, 97-121.
33. Hansen, P.R., A. Lunde, & J.M. Nason (2011) The model confidence set. Econometrica 79, 456-497.
34. Harvey, A.C. (1993) Time Series Models, 2nd ed. Harvester Wheatsheaf.
35. Harvey, A.C. (1998) Long memory in stochastic volatility. In J. Knight & S. Satchell (eds.), Forecasting Volatility in Financial Markets, pp. 307-320. Butterworth-Heineman.
36. Harvey, A.C. (2001) Testing in unobserved components models. Journal of Forecasting 20, 1-19.
37. Harvey, A.C. & T. Proietti (eds.) (2005) Readings in Unobserved Components Models. Oxford University Press.
38. Harvey, O. & G. De Rossi (2006) Signal extraction. In T.C. Mills & K. Patterson (eds.), Palgrave Handbook of Econometrics, vol. 1, Econometric Theory. Ch. 27, pp. 970-1000. Palgrave Macmillan.
39. Hurvich, C.M., E. Moulines, & P. Soulier (2005) Estimating long memory in volatility. Econometrica 73, 1283-1328.
40. Koopman, S.J., B. Jungbacker & E. Hol (2005) Forecasting daily variability of the S &P 100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance 12, 445-475.
41. Maheu, J.M. & T.H. McCurdy (2002) Nonlinear features of realized FX volatility. Review of Economics & Statistics 84, 668-681.
42. Meddahi, N. (2001) An Eigenfunction Approach for Volatility Modeling. Cirano Working Paper 2001s-70.
43. Meddahi, N. (2002) A theoretical comparison between integrated and realized volatilities. Journal of Applied Econometrics 17, 479-508.
44. Meddahi, N. ( 2003) ARMA representation of integrated and realized variances. The Econometrics Journal 6, 334-355.
45. Meddahi, N. & E. Renault (2004) Temporal aggregation of volatility models. Journal of Econometrics 119, 355-379.
46. Muller, U.K. & M.W. Watson (2008) Testing models of low-frequency variablility. Econometrica 76, 979-1016.
47. Patton, A. (2011) Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics 160, 246-256.
48. Patton, A.J. & K.K. Sheppard (2009) Evaluating volatility forecasts. In T.G. Andersen, R.A. Davis, J.P. Kreiss, & T. Mikosch (eds.), Handbook of Financial Time Series, pp. 801-838. Springer Verlag.
49. Perron, P. & S. Ng (1996) Useful modifications of some unit root tests with dependent errors and their local asymptotic properties. Review of Economic Studies 63, 435-463.
50. Phillips, P.C.B. & B.E. Hansen (1990) Statistical inference in instrumental variables regression with I(1) processes. Review of Economic Studies 57, 99-125.
51. Schwert, G.W. (1989) Tests for unit roots: A Monte-Carlo investigation. Journal of Business and Economic Statistics 7, 147-160.
52. Stock, J.H. & M.W. Watson (1999) Forecasting inflation. Journal of Monetary Economics 44, 293-335.
53. Sun, Y. & P.C.B. Phillips (2003) Nonlinear log-periodogram regression for perturbed fractional processes. Journal of Econometrics 115, 355-389.
54. Taylor, S.J. (2005) Asset Price Dynamics, Volatility, and Prediction, Princeton University Press.
55. Watson, M.W. (1986) Univariate detrending methods with stochastic trends. Journal of Monetary Economics 18, 49-75.
56. West, K.D. (2001) On optimal instrumental variable estimation of stationary time series model. International Economic Review 42, 1043-1050.
57. White, H. (2000) Asymptotic Theory for Econometricians, rev. ed. Academic Press.
58. Wright, J.H. (1999) Testing for a unit root in the volatility of asset returns. Journal of Applied Econometrics 14, 309-318.
59. Zhang, L., P.A. Mykland, & Y. Ä?t-Sahalia (2005) A tale of two time scales: Determining integrated volatility with noisy high frequency data. Journal of the American Statistical Association 100, 1394-1411.