 |
Download a pdf copy of this help file here |
|
Download a pdf copy of this help file here |
See also: Time series in ModelRisk
VoseTimeSeasonalGBM(Mu,Omega,{S1},P1,{S2},P2,LogReturn,LastValue)
Array
function that models a Seasonal
Geometric Brownian Motion time series model.
You can provide an array with seasonal indices (e.g. 7 values, one for each day of the week) that will be run through periodically, starting at position P1.
Optionally you can provide a second optional cycle within each period of the first cycle, useful for modelling, say, week/day or day/hour patterns.
Mu - the percentage drift.
Sigma- the percentile volatility.
{S1} - array of seasonality factors for the first (outer) cycle.
P1 - the starting index for cycle 1.
{S2} - array of seasonality factors for the second (inner) cycle.
P2 - the starting index for cycle 2.
LogReturn - Optional boolean parameter (TRUE/FALSE) specifying whether to return the actual time series (FALSE, default) or log returns (TRUE).
LastValue - last known historic value. The generated time series values will continue on from this value. Should only be provided if the LogReturn parameter is set to FALSE or omitted.
As the ModelRisk Time Series functions typically take a lot of parameters, we recommend for these in particular to use the Time Series window.
VoseTimeSeasonalGBM - generates an array of random values from this time series.
VoseTimeSeasonalGBMFit - generates an array of random values from this time series fitted to data.
VoseTimeSeasonalGBMFitP - returns the parameters of this time series fitted to data.