Statistical Computational of Volatility in Financial Time Series Data
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.
[1] R. F. Engle and V. K. Ng. Measuring and testing the impact of news on
volatility, Journal of Finance. Vol. 48, 1993, pp. 1749ÔÇö1778.
[2] C. Chu and S.-J. Lin, Detecting parameter shift in GARCH models.
Econometric Reviews. Vol. 14, 1995, pp. 241ÔÇö266.
[3] 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.
[4] G. Janacek, and L. Swift, Time series forecasting, simulation and
applications. Ellis Hoe wood limited. England. 1993.
[5] S.Oraintara, y.Chen, and Q.Nguyen. Integer fast Fourier transform. IEEE
Transaction on signal processing. Vol. 50 NO. 3. 2002.
[6] 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.
[7] 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.
[8] A.Razdan, Wavelet correlation coefficient of strongly correlated time
series, Physics A. 333, 2004, pp: 335-342.
[9] 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.
[10] R. Gencay, F.Seluk and B.Whitcher, An Introduction to Wavelets and
Other Filtering Methods in Finance and Economics, Academic Press,
New York. 2002.
[11] S. Mallat. A Wavelet Tour of Signal Processing. Academic Press, San
Diego. 2001.
[12] D.E. Newland, An Introduction to Random Vibrations, Spectral and
Wavelet Analysis (third ed). Prentice-Hall. Englewood Cliffs, NJ. 1993.
[13] A.H. Siddiqi, Applied Functional Analysis, Marcel Dekker, New York.
2004.
[14] I. Daubechies, . Ten Lectures on Wavelets, PA. SIAM and Philadelphia.
1992.
[15] Chang Chiann and Pedro A. Moretin . A wavelet analysis for time series,
Nonparametric Statistics, vol. 10, 1998, pp: 1-46.
[16] Philippe Masset . Analysis of Financial Time-Series Using Fourier and
Wavelet Methods. University of Fribourg (Switzerland) - Faculty of
Economics and Social Science. 2008.
[17] Todd Wittman. Time-Series Clustering and Association Analysis of
Financial Data, CS 8980 Project. 2002, unpublished.
[1] R. F. Engle and V. K. Ng. Measuring and testing the impact of news on
volatility, Journal of Finance. Vol. 48, 1993, pp. 1749ÔÇö1778.
[2] C. Chu and S.-J. Lin, Detecting parameter shift in GARCH models.
Econometric Reviews. Vol. 14, 1995, pp. 241ÔÇö266.
[3] 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.
[4] G. Janacek, and L. Swift, Time series forecasting, simulation and
applications. Ellis Hoe wood limited. England. 1993.
[5] S.Oraintara, y.Chen, and Q.Nguyen. Integer fast Fourier transform. IEEE
Transaction on signal processing. Vol. 50 NO. 3. 2002.
[6] 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.
[7] 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.
[8] A.Razdan, Wavelet correlation coefficient of strongly correlated time
series, Physics A. 333, 2004, pp: 335-342.
[9] 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.
[10] R. Gencay, F.Seluk and B.Whitcher, An Introduction to Wavelets and
Other Filtering Methods in Finance and Economics, Academic Press,
New York. 2002.
[11] S. Mallat. A Wavelet Tour of Signal Processing. Academic Press, San
Diego. 2001.
[12] D.E. Newland, An Introduction to Random Vibrations, Spectral and
Wavelet Analysis (third ed). Prentice-Hall. Englewood Cliffs, NJ. 1993.
[13] A.H. Siddiqi, Applied Functional Analysis, Marcel Dekker, New York.
2004.
[14] I. Daubechies, . Ten Lectures on Wavelets, PA. SIAM and Philadelphia.
1992.
[15] Chang Chiann and Pedro A. Moretin . A wavelet analysis for time series,
Nonparametric Statistics, vol. 10, 1998, pp: 1-46.
[16] Philippe Masset . Analysis of Financial Time-Series Using Fourier and
Wavelet Methods. University of Fribourg (Switzerland) - Faculty of
Economics and Social Science. 2008.
[17] Todd Wittman. Time-Series Clustering and Association Analysis of
Financial Data, CS 8980 Project. 2002, unpublished.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:49308", author = "S. Al Wadi and Mohd Tahir Ismail and Samsul Ariffin Abdul Karim", title = "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.", keywords = "Fast Fourier transforms, Haar wavelet transform,Matlab (Wavelet tools), stocks market, Volatility.", volume = "4", number = "2", pages = "205-5", }