Access the full text.
Sign up today, get DeepDyve free for 14 days.
(2003)
Ltd. Appl. Stochastic Models Bus. Ind
A. Offord (1938)
Introduction to the Theory of Fourier IntegralsNature, 141
P. Brockwell, R. Davis (2013)
Time Series: Theory and Methods
K. Worden, G. Tomlinson (2019)
Nonlinearity in Structural Dynamics
R. Carmona, W. Hwang, B. Torrésani (1998)
Practical Time-Frequency Analysis, Volume 9: Gabor and Wavelet Transforms, with an Implementation in S
N. Huang, Zheng Shen, S. Long, Manli Wu, Hsing Shih, Q. Zheng, N. Yen, C. Tung, Henry Liu (1998)
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysisProceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454
R. Mantegna, H. Stanley (1999)
An Introduction to Econophysics: Contents
Computer Implemented Empirical Mode Decomposition Method, Apparatus, and Article of Manufacture. (US Provisional Application Serial Number 60/023
K. Worden, G. Tomlinson (2000)
Nonlinearity in Structural Dynamics: Detection, Identification and Modelling
A. Shiryaev (1999)
Essentials of stochastic finance
I. Daubechies (1992)
Ten Lectures on WaveletsComputers in Physics, 6
J. Bendat, A. Piersol (1987)
Random Data: Analysis and Measurement Procedures
A. Potamianos, P. Maragos (1994)
A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulationSignal Process., 37
A. Rao, Khaled Hamed, Huey-Long Chen (2003)
Time-frequency analysis
Figure 11. The values of the Logrithm Ratio of Consecutive Value (LRCV), a popular proxy to present the market variability. Notice that the data is not stationary
Rosario Mantegna, H. Stanley (1999)
An Introduction to Econophysics: Correlations and Complexity in FinanceNature
S. Hahn (1996)
Hilbert Transforms in Signal Processing
P. Flandrin (1998)
Time-Frequency/Time-Scale Analysis
N. Huang, Zheng Shen, S. Long (1999)
A new view of nonlinear water waves: the Hilbert spectrumAnnual Review of Fluid Mechanics, 31
E. Bacry, A. Arneodo, U. Frisch, Y. Gagne, E. Hopfinger (1991)
Wavelet analysis of fully developed turbulence data and measurement of scaling exponents
(1965)
Rational theory of warrant pricing, Industrial Management Review
Scientifiques L’É.N.S, L. Bachelier, ÏK Bachelier
Théorie de la spéculationAnnales Scientifiques De L Ecole Normale Superieure, 17
B. Mandelbrot, R. Gomory, P. Cootner (1997)
Fractals and Scaling in Finance: Discontinuity, Concentration, Risk. Selecta Volume E
Yu-Kweng Lin, G. Cai (1967)
Probabilistic Structural Dynamics: Advanced Theory and Applications
Thomas Cavicchi, Umberto Spagnolini (2018)
Digital signal processing8th International Multitopic Conference, 2004. Proceedings of INMIC 2004.
Huang Huang, Shen Shen, Long Long
A new view of nonlinear wavesAnnual Review of Fluid Mechanics, 31
D. Inman (2002)
Nonlinearity in Structural Dynamics: Detection, Identification and ModellingProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 216
M. Nash (2002)
Practical Time-Frequency Analysis, Gabor and Wavelet Transforms With an Implementation in STechnometrics, 44
T. Mills (1990)
Time series techniques for economists
B. Mandelbrot (1997)
Fractals and Scaling in Finance
(1991)
Oceanographic and atmospheric applications of spatial statistics and digital image analysis. In Spatial Statistics and Digital Image Analysis
A new method, the Hilbert–Huang Transform (HHT), developed initially for natural and engineering sciences has now been applied to financial data. The HHT method is specially developed for analysing non‐linear and non‐stationary data. The method consists of two parts: (1) the empirical mode decomposition (EMD), and (2) the Hilbert spectral analysis. The key part of the method is the first step, the EMD, with which any complicated data set can be decomposed into a finite and often small number of intrinsic mode functions (IMF). An IMF is defined here as any function having the same number of zero‐crossing and extrema, and also having symmetric envelopes defined by the local maxima, and minima respectively. The IMF also thus admits well‐behaved Hilbert transforms. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to non‐linear and non‐stationary processes. With the Hilbert transform, the IMF yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy–frequency–time distribution, which we designate as the Hilbert Spectrum. Comparisons with Wavelet and Fourier analyses show the new method offers much better temporal and frequency resolutions. The EMD is also useful as a filter to extract variability of different scales. In the present application, HHT has been used to examine the changeability of the market, as a measure of volatility of the market. Published in 2003 by John Wiley & Sons, Ltd.
Applied Stochastic Models in Business and Industry – Wiley
Published: Jul 1, 2003
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.