20.11.2012 Views

recent developments in high frequency financial ... - Index of

recent developments in high frequency financial ... - Index of

recent developments in high frequency financial ... - Index of

SHOW MORE
SHOW LESS

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

216<br />

where σk is the standard deviations for the observations that occur with<strong>in</strong> the<br />

w<strong>in</strong>dow k. However, the optimal bandwidth for Silverman (1986) <strong>in</strong>volves a fitted<br />

function which is too smooth. In other words this optimal bandwidth places too<br />

much weight on prices far away from any given time t, <strong>in</strong>duc<strong>in</strong>g too much<br />

averag<strong>in</strong>g and discard<strong>in</strong>g valuable <strong>in</strong>formation <strong>in</strong> local price movements. Like Lo<br />

et al. (2000), through trial and error, we found that an acceptable solution to this<br />

problem is to use a bandwidth equal to 20% <strong>of</strong> hopt,k :<br />

B. Extrema detection methods<br />

h ¼ 0:2 hopt;k: (18)<br />

Technical details for both extrema detection methods and projection procedure<br />

are presented below:<br />

B.1. M1<br />

M1 is the extrema detection method us<strong>in</strong>g the close prices. After smooth<strong>in</strong>g the<br />

data by estimat<strong>in</strong>g the Nadaraya–Watson kernel function, ^mhðXPj;k Þ, we compute<br />

maxima and m<strong>in</strong>ima respectively noted by max ^mhðXP j;k Þ and m<strong>in</strong> ^mhðXP j;k Þ :<br />

max ^mh XPj;k ¼ ^mh XPj;k<br />

m<strong>in</strong> ^mh XPj;k ¼ ^mh XPj;k<br />

S ^m0 h<br />

S ^m0 h<br />

XPj;k ¼þ1; S ^m0<br />

h<br />

XPj;k ¼ 1; S ^m0<br />

h<br />

XPjþ1;k ¼ 1<br />

XPjþ1;k ¼þ1 ;<br />

where S(X) is the sign function, equal to +1 (−1) when the sign <strong>of</strong> X is positive<br />

(negative), and ^m 0 h XPj;k is the first derivative <strong>of</strong> the kernel function ^mh XPj;k .By<br />

construction we obta<strong>in</strong> alternate extrema. We denote respectively by tM ^mh XPj;k<br />

and tm ^mh XPj;k the moments correspondent to detected extrema such that:<br />

tM ^mh XPj;k ¼ j j 2 max n o<br />

n<br />

^mhðXP Þ j;k<br />

o<br />

(19)<br />

: (20)<br />

tm ^mh XPj;k ¼ j j 2 m<strong>in</strong> ^mhðXP j;k Þ<br />

After record<strong>in</strong>g the moments <strong>of</strong> the detected extrema we realize an orthogonal<br />

projection <strong>of</strong> selected extrema, from the smooth<strong>in</strong>g curve, to the orig<strong>in</strong>al one. We<br />

deduce the correspond<strong>in</strong>g extrema to construct the series <strong>in</strong>volv<strong>in</strong>g both maxima,<br />

and m<strong>in</strong>ima, m<strong>in</strong>Pj;k such that:<br />

maxPj;k<br />

W. B. Omrane, H. V. Oppens<br />

maxPj;k ¼ max P tM ð ^mhðXP j;k ÞÞ 1;k; P tM ð ^mhðXP j;k ÞÞ;k; P tM ð ^mhðXP j;k ÞÞþ1;k<br />

m<strong>in</strong>Pj;k ¼ m<strong>in</strong> P tmð ^mhðXP j;k ÞÞ 1;k; P tmð ^mhðXP j;k ÞÞ;k; P tmð ^mhðXP j;k ÞÞþ1;k :<br />

For each w<strong>in</strong>dow k we get alternate maxima and m<strong>in</strong>ima. This is assured by the<br />

bandwidth h which provide at least two time <strong>in</strong>tervals between two consecutive

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!