transForecast_svm: Forecast - using Support Vector Machines

In RTransProb: Analyze and Forecast Credit Migrations

Description

This model implements a forecasting method using Support Vector Machines.

Usage

transForecast_svm(data, histData, predData_svm, startDate, endDate,
                    method, interval, snapshots, defind, depVar, indVars,  ratingCat, 
                    pct, tuning, kernelType, cost, cost.weights, gamma, gamma.weights)

Arguments

data	a table containing historical credit ratings data (i.e., credit migration data). A dataframe of size nRecords x 3 where each row contains an ID (column 1), a date (column 2), and a credit rating (column 3); The credit rating is the rating assigned to the corresponding ID on the corresponding date.
histData	historical macroeconomic,financial and non-financial data.
predData_svm	forecasting data.
startDate	start date of the estimation time window, in string or numeric format.
endDate	end date of the estimation time window, in string or numeric format.
method	estimation algorithm, in string format. Valid values are 'duration' or 'cohort'.
interval	the length of the transition interval under consideration, in years. The default value is 1, i.e., 1-year transition probabilities are estimated.
snapshots	integer indicating the number of credit-rating snapshots per year to be considered for the estimation. Valid values are 1, 4, or 12. The default value is 1, i.e., one snapshot per year. This parameter is only used in the 'cohort' algorithm.
defind	Default Indicator
depVar	dependent variable, as a string.
indVars	list containing the independent variables.
ratingCat	list containing the unique rating caetgories
pct	percent of data used in training dataset.
tuning	perform tuning. If tuning='TRUE' tuning is perform. If tuning='FALSE' tuning is not performed
kernelType	the kernel used in training and predicting (see Package e1071 for more detail)
cost	cost of constraints violation (default: 1) it is the 'C' constant of the regularization term in the Lagrange formulation.
cost.weights	vector containing tuning parameters for cost
gamma	parameter needed for all kernels except linear (default: 1/(data dimension))
gamma.weights	vector containing tuning parameters for gamma

Value

The output consists of a forecasted transition matrix using SVM.

Author(s)

Abdoulaye (Ab) N'Diaye

Examples

## Not run: 
library(dplyr)
library(plyr)
library(Matrix)
library(tictoc)



for (i in c(24, 25, 26)) {
 print(paste("RUN-",i,sep=""))
 data <- data
 
 histData <- histData.normz
 
 
 predData_svm2 <- predData_svm_Baseline
 predData_svm2 <- subset(
   predData_svm2,
   X == i,
   select = c(Market.Volatility.Index..Level..normz

   )
 )
 
 indVars   = c("Market.Volatility.Index..Level..normz"

 )
 
 
 startDate = "1991-08-16"
 endDate   = "2007-08-16"
 
 depVar <- c("end_rating")
 
 pct <- 1
 wgt <-  "mCount"
 ratingCat <- c("A", "B", "C", "D", "E", "F", "G")
 defind    <- "G"
 lstCategoricalVars <- c("end_rating")
 tuning <- "FALSE"
 cost <- 0.01
 gamma <- 0.01
 cost.weights <-  c(0.01, 0.05, 0.1, 0.25, 10, 50, 100)
 gamma.weights <- c(0.01, 0.05, 0.1, 0.25, 10, 50, 100)
 kernelType <- "sigmoid"
 method    = "cohort"
 snapshots = 1
 interval  = 1
 
 
 svm_TM <-
   transForecast_svm(
     data,
     histData,
     predData_svm2,
     startDate,
     endDate,
     method,
     interval,
     snapshots,
     defind,
     depVar,
     indVars,
     ratingCat,
     pct,
     tuning,
     kernelType,
     cost,
     cost.weights,
     gamma,
     gamma.weights
   )
 print(svm_TM)
 
}

## End(Not run)

RTransProb documentation built on May 2, 2019, 6:49 a.m.