Commenced in January 2007
Paper Count: 30840
Statistical Computational of Volatility in Financial Time Series Data
Abstract:It is well known that during the developments in the economic sector and through the financial crises occur everywhere in the whole world, volatility measurement is the most important concept in financial time series. Therefore in this paper we discuss the volatility for Amman stocks market (Jordan) for certain period of time. Since wavelet transform is one of the most famous filtering methods and grows up very quickly in the last decade, we compare this method with the traditional technique, Fast Fourier transform to decide the best method for analyzing the volatility. The comparison will be done on some of the statistical properties by using Matlab program.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1327812Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1975
 R. F. Engle and V. K. Ng. Measuring and testing the impact of news on volatility, Journal of Finance. Vol. 48, 1993, pp. 1749ÔÇö1778.
 C. Chu and S.-J. Lin, Detecting parameter shift in GARCH models. Econometric Reviews. Vol. 14, 1995, pp. 241ÔÇö266.
 S.-J. Lin and Yang, J. Testing shift in financial models with conditional heteroskedasticity: An empirical distribution function approach. Research Paper 30, University of Technology Sydney, Quantitative Finance Research Group. 1999.
 G. Janacek, and L. Swift, Time series forecasting, simulation and applications. Ellis Hoe wood limited. England. 1993.
 S.Oraintara, y.Chen, and Q.Nguyen. Integer fast Fourier transform. IEEE Transaction on signal processing. Vol. 50 NO. 3. 2002.
 B. James Ramsey, Wavelets in Economics and Finance: Past and Future. C.V. Starr Center for Applied Economics, Department of Economics Faculty of Arts and Science, New York University. 2002.
 B. Whitchera, Peter. F. Craigmileb and Peter Brownc. Time-varying spectral analysis in neurophysiologic time series using Hilbert wavelet pairs, Signal Processing vol. 85. 2005, pp. 2065-2081.
 A.Razdan, Wavelet correlation coefficient of strongly correlated time series, Physics A. 333, 2004, pp: 335-342.
 A. Arneodo, B.Audit, N.Decoster, J.F. Muzy, and C.Vaillant, Waveletbased multiracial formalism: applications to DNA sequences. Springer, Berlin, 2002. pp. 27-102.
 R. Gencay, F.Seluk and B.Whitcher, An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press, New York. 2002.
 S. Mallat. A Wavelet Tour of Signal Processing. Academic Press, San Diego. 2001.
 D.E. Newland, An Introduction to Random Vibrations, Spectral and Wavelet Analysis (third ed). Prentice-Hall. Englewood Cliffs, NJ. 1993.
 A.H. Siddiqi, Applied Functional Analysis, Marcel Dekker, New York. 2004.
 I. Daubechies, . Ten Lectures on Wavelets, PA. SIAM and Philadelphia. 1992.
 Chang Chiann and Pedro A. Moretin . A wavelet analysis for time series, Nonparametric Statistics, vol. 10, 1998, pp: 1-46.
 Philippe Masset . Analysis of Financial Time-Series Using Fourier and Wavelet Methods. University of Fribourg (Switzerland) - Faculty of Economics and Social Science. 2008.
 Todd Wittman. Time-Series Clustering and Association Analysis of Financial Data, CS 8980 Project. 2002, unpublished.