Taufemback, Cleiton and Da Silva, Sergio (2011): Spectral Analysis Informs the Proper Frequency in the Sampling of Financial Time Series Data.
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Applied econometricians tend to show a long neglect for the proper frequency to be considered while sampling the time series data. The present study shows how spectral analysis can be usefully employed to fix this problem. The case is illustrated with ultra-high-frequency data and daily prices of four selected stocks listed on the Sao Paulo stock exchange.
|Item Type:||MPRA Paper|
|Original Title:||Spectral Analysis Informs the Proper Frequency in the Sampling of Financial Time Series Data|
|Keywords:||Econophysics; Spectral analysis; Aliasing; Sampling; Financial time series|
|Subjects:||C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data ; Data Access|
|Depositing User:||Sergio Da Silva|
|Date Deposited:||09 Feb 2011 16:25|
|Last Modified:||03 Mar 2017 17:16|
 Y. Ait-Sahalia, P.A. Mykland, L. Zhang, How often to sample a continuous-time process in the presence of market microstructure noise?, Review of Financial Studies 18 (2005) 351.
 C.E. Shannon, Communication in the presence of noise, Proceedings of the Institute of Radio Engineers 37 (1949) 10.
 S. Haykin, Communication Systems, 4th edition, New York, John Wiley & Sons, Inc., 2000.
 T.S. Rappaport, Wireless Communications: Principles and Practice, 2nd edition, Upper Saddle River, Prentice Hall, 2002.
 P. Bloomeld, Fourier Analysis of Time Series: An Introduction, 2nd edition, John Wiley & Sons, Inc., 2000.
 C. Granger, P. Newbold, Forecasting Economic Time Series, New York, Academic Press, 1977.
 R.A. Ashley, J.G.P. Dwyer, Time domain aliasing and nonlinear modelling, Virginia Tech working paper, 1998.
 W.H. Press, B.P. Flannery, S.A. Teukolsky, W.T. Vetterling, Numerical Recipes in FORTRAN 77: The Art of Scientific Computing, 2nd edition, New York, Cambridge University Press, 1992.
 N.R. Lomb, Least-squares frequency analysis of unequally spaced data, Astrophysics and Space Science 39 (1976) 447.
 J.D. Scargle, Studies in astronomical time series analysis. II – Statistical aspects of spectral analysis of unevenly spaced data, Astrophysical Journal 263 (1982) 835.
 I. Giampaoli, W.L. Ng, N. Constantinou, Analysis of ultra-high-frequency financial data using advanced Fourier transforms, Finance Research Letters 6 (2009) 47.
 G.L. Bretthorst, Generalizing the Lomb-Scargle periodogram, American Institute of Physics Conference Proceedings 568 (2001) 241.
 M. Zechmeister, M. Kurster, The generalised Lomb-Scargle periodogram. A new formalism for the floating-mean and Keplerian periodograms, Astronomy & Astrophysics 496 (2009) 577.
 Y. Qi, T.P. Minka, R.W. Picard, Bayesian spectrum estimation of unevenly sampled nonstationary data, ICASSP 02 IEEE International Conferences 2 (2002) 1473.
 P. Broersen, S. de Waele, Some benefits of aliasing in time series analysis, Proceedings of EUSIPCO 2000, Tampere, Finland, 2000.