Autocorrelation function plots
plotacfPlot.Rd
Returns plots of autocorrelations including
the autocorrelation function ACF, the partial
ACF, the lagged ACF, and the Taylor effect plot.
The functions to display stylized facts are:
acfPlot  autocorrelation function plot, 
pacfPlot  partial autocorrelation function plot, 
lacfPlot  lagged autocorrelation function plot, 
teffectPlot  Taylor effect plot. 
Arguments
 x

an uni or multivariate return series of class
timeSeries
or any other object which can be transformed by the functionas.timeSeries()
into an object of classtimeSeries
.  labels

a logical value. Whether or not x and yaxes should be automatically labeled and a default main title should be added to the plot. By default
TRUE
.  n
an integer value, the number of lags.
 lag.max

maximum lag for which the autocorrelation should be calculated, an integer.
 type

a character string which specifies the type of the input series, either "returns" or series "values". In the case of a return series as input, the required value series is computed by cumulating the financial returns:
exp(colCumsums(x))
 deltas

the exponents, a numeric vector, by default ranging from 0.2 to 3.0 in steps of 0.2.
 ymax

maximum yaxis value on plot. If
NA
, then the value is selected automatically.  standardize

a logical value. Should the vector
x
be standardized?  ...
arguments to be passed.
Details
Autocorrelation Functions:
The functions acfPlot
and pacfPlot
, plot and estimate
autocorrelation and partial autocorrelation function. The functions
allow to get a first view on correlations within the time series.
The functions are synonym function calls for R's acf
and
pacf
from the the ts
package.
Taylor Effect:
The "Taylor Effect" describes the fact that absolute returns of
speculative assets have significant serial correlation over long
lags. Even more, autocorrelations of absolute returns are
typically greater than those of squared returns. From these
observations the Taylor effect states, that that the autocorrelations
of absolute returns to the the power of delta
,
abs(xmean(x))^delta
reach their maximum at delta=1
.
The function teffect
explores this behaviour. A plot is
created which shows for each lag (from 1 to max.lag
) the
autocorrelations as a function of the exponent delta
.
In the case that the above formulated hypothesis is supported,
all the curves should peak at the same value around delta=1
.
Value
for acfPlot
and pacfplot
,
an object of class "acf"
, see acf
;
for teffectPlot
, a numeric matrix
of order deltas
by max.lag
with
the values of the autocorrelations;
for lacfPlot
, a list with the following two elements:
 Rho
the autocorrelation function,
 lagged
the lagged correlations.