![]() |
![]() | ||||
![]() | Actuary Menu | ![]() | ![]() | Hydrology Menu |
Activate the finance menu.
The following options and facilities are available: Finance Menu
Price Return Transformation
GARCH(p,q) process
Moving Estimates (Sample, Gaussian, GP)
Price Return Transformation
It is assumed that there is a data set of type Xtremes Multivariate
Data with the date (format: day-month-year) in the first three
columns and speculative prices in the fourth (and further) columns.
If necessary, apply
the Select Columns option. If
dates are included which are not trading days then indicate
the missing price by a period.
A single speculative asset:
Apply Price Return Transformation to transform
prices into log-returns r[t] = log(p[t]) - log(p[t-1]) of
prices for consecutive trading days. Gaps between trading days
(the weekend effect) are dealt with in the following manner:
GARCH(p,q) process
This facility allows the simulation of GARCH(p,q) series
for p >= 0 and q >= 1. Recollect that an
ARCH(p) series is a GARCH(p,0) series. The following
parameters must be specified in edit fields.
Sample Size positive integer
Filename Select a filename, and, optionally, a directory.
The stored data set is now the active one.
Moving Estimates (Sample) | Mean | Standard Deviation |
Variance | VaR | |
Moving Estimates (Gaussian) | Mean | Standard Deviation |
Variance | VaR | |
Moving Estimates (GP) | Scale Parameter | Shape Parameter Gamma |
VaR |
Moving Estimates (Sample Standard Deviation)
The sample standard deviation for the chosen time horizon is
plotted against time with "left-sided" as the default option. Moving Estimates (Sample Variance)
The sample variance for the chosen time horizon is plotted
against time with "left-sided" as the default option. Moving Estimates (Sample VaR)
The sample VaR at the level q for the chosen time horizon is
plotted against the time with "left-sided" as the default option.
Thus, one is evaluating the sample q-quantile.
Moving Estimates (Gaussian)
The MLEs within the Gaussian model are employed to estimate
the same parameters as those in the empirical approach.
Therefore, the estimates of the mean, standard deviation
and variance are closely related to those in the empirical
case.Moving Estimates (Gaussian Mean)
The sample mean (MLE in the Gaussian model) for the chosen
time horizon is plotted against time with "left-sided" as
the default option. Moving Estimates (Gaussian Standard Deviation)
The MLE for the scale parameter in the Gaussian model evaluated
for the chosen time horizon is plotted against time with
"left-sided" as the default option. Moving Estimates (Gaussian Variance)
The MLE for the variance in the Gaussian model evaluated
for the chosen time horizon is plotted against time with
"left-sided" as the default option. Moving Estimates (Gaussian VaR)
The MLE for the mean and variance in the Gaussian model
is evaluated for the chosen time horizon. The q-quantile
of estimated Gaussian distribution is plotted against
time. This approach enables an extrapolation beyond the
range of the data.Moving Estimates (GP)
The primary aim is to estimate the VaR over a moving window
(for the chosen time horizon) using the POT method.
Estimates of the scale and shape parameters are added.
Besides the time horizon choose the number of upper
order statistics. Select one of the estimators in
GP models. The estimation is carried out in the
gamma-parametrization. Moving Estimates (GP Scale Parameter)
The estimate of the scale parameter within the model
of truncated GP distribution evaluated for the chosen
time horizon is plotted against time with
"left-sided" as the default option. Moving Estimates (GP Shape Parameter Gamma)
The estimate of the shape parameter within the model
of (truncated) GP distribution evaluated for the chosen
time horizon is plotted against time with
"left-sided" as the default option.
![]() |