Description Usage Arguments Value Examples
Simulate prices to horizon based on fitted marginal distributions and copula - using Meucci's methods
1 2 3 4 | simulateDataToHorizon3(numSimulations, numPeriodsForward, vineCopulaFit,
signalMarginalFit, residualMarginalFit,
eigenVectors = diag((length(signalMarginalFit) +
length(residualMarginalFit))), corrIDX, arimaModel)
|
numSimulations |
is a scalar dictating number of simulations (rows) to conduct |
numPeriodsForward |
is a scalar dictating number of periods forward (columns) the investment horizon is |
vineCopulaFit |
is a vine copula object |
signalMarginalFit |
is a list of marginal distribution fits for the signal risk factors |
residualMarginalFit |
is a list of marginal distribution fits for the noisy risk factors |
eigenVectors |
is a matrix of eigenVectors used to convert the horizon risk factors back into invariants |
a matrix of invariants projected to the investment horizon - to then be used in the pricing function
1 | none
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.