Volatility (finance): Difference between revisions

From formulasearchengine
Jump to navigation Jump to search
en>Allens
 
Line 1: Line 1:
Alyson Meagher is the name her parents gave her but she doesn't like when people use her full name. My wife and I reside in Mississippi  [http://chungmuroresidence.com/xe/reservation_branch2/152663 real psychic] and I love every working day living here. Invoicing is what I do for a living but  psychic chat online - [http://isaworld.pe.kr/?document_srl=392088 check this link right here now] - I've usually needed my own company. The favorite hobby for him and his kids is fashion and he'll be starting some thing else alongside with it.<br><br>Also visit my web page [http://findyourflirt.net/index.php?m=member_profile&p=profile&id=117823 psychic chat online]
In the [[mathematics|mathematical]] topic of [[wavelet]] theory, the '''cascade algorithm''' is a [[numerical method]] for calculating function values of the basic [[Wavelet#Scaling_function|scaling]] and [[wavelet]] functions of a [[discrete wavelet transform]] using an iterative algorithm. It starts from values on a coarse sequence of sampling points and produces values for successively more densely spaced sequences of sampling points. Because it applies the same operation over and over to the output of the previous application, it is known as the ''cascade algorithm''.
 
== Successive approximation ==
 
The iterative algorithm generate successive approximations to ψ(''t'') or φ(''t'') from {''h''} and {''g''} filter coefficients. If the algorithm converges to a fixed point, then that fixed point is the basic scaling function or wavelet.
 
The iterations are defined by
 
: <math>\varphi^{(k+1)}(t)=\sum_{n=0}^{N-1} h[n] \sqrt 2 \varphi^{(k)} (2t-n)</math>
 
For the ''k''<sup>th</sup> iteration, where an initial φ<sup>(0)</sup>(''t'') must be given.
 
The frequency domain estimates of the basic scaling function is given by
 
: <math>\Phi^{(k+1)}(\omega)= \frac {1} {\sqrt 2} H\left( \frac {\omega} {2}\right) \Phi^{(k)}\left(\frac {\omega} {2}\right)</math>
 
and the limit can be viewed as an infinite product in the form
 
: <math>\Phi^{(\infty)}(\omega)= \prod_{k=1}^{\infty} \frac {1} {\sqrt 2} H\left( \frac {\omega} {2^k}\right) \Phi^{(\infty)}(0).</math>
 
If such a limit exists, the spectrum of the scaling function is
 
: <math>\Phi(\omega)= \prod_{k=1}^\infty \frac {1} {\sqrt 2} H\left( \frac {\omega} {2^k}\right) \Phi^{(\infty)}(0)</math>
 
The limit does not depends on the initial shape assume for φ<sup>(0)</sup>(''t''). This algorithm converges reliably to φ(''t''), even if it is discontinuous.
 
From this scaling function, the wavelet can be generated from
 
: <math>\psi(t)= \sum_{n=- \infty}^{\infty} g[n]{\sqrt 2} \varphi^{(k)} (2t-n).</math>
 
Plots of the function at each iteration is shown in Figure 1.
 
Successive approximation can also be derived in the frequency domain.
 
<!-- Missing image removed: [[File:Figure cascade.png|'''Figure 1. Iterations of the successive approximations of Daubechies wavelet and scale function by the cascade algorithm.''']] -->
 
== References ==
* [[C. Sidney Burrus|C.S. Burrus]], R.A. Gopinath, H. Guo, ''Introduction to Wavelets and Wavelet Transforms: A Primer'', Prentice-Hall, 1988,  ISBN 0-13-489600-9.
* http://cnx.org/content/m10486/latest/
* http://cm.bell-labs.com/cm/ms/who/wim/cascade/index.html
 
[[Category:Wavelets]]

Revision as of 21:28, 29 January 2014

In the mathematical topic of wavelet theory, the cascade algorithm is a numerical method for calculating function values of the basic scaling and wavelet functions of a discrete wavelet transform using an iterative algorithm. It starts from values on a coarse sequence of sampling points and produces values for successively more densely spaced sequences of sampling points. Because it applies the same operation over and over to the output of the previous application, it is known as the cascade algorithm.

Successive approximation

The iterative algorithm generate successive approximations to ψ(t) or φ(t) from {h} and {g} filter coefficients. If the algorithm converges to a fixed point, then that fixed point is the basic scaling function or wavelet.

The iterations are defined by

For the kth iteration, where an initial φ(0)(t) must be given.

The frequency domain estimates of the basic scaling function is given by

and the limit can be viewed as an infinite product in the form

If such a limit exists, the spectrum of the scaling function is

The limit does not depends on the initial shape assume for φ(0)(t). This algorithm converges reliably to φ(t), even if it is discontinuous.

From this scaling function, the wavelet can be generated from

Plots of the function at each iteration is shown in Figure 1.

Successive approximation can also be derived in the frequency domain.


References