Fitting ARMA GARCH Models to many time series and saving the results

AutoFitting.R

library(TSA)
library(forecast)
library(tseries)

garchAutoTryFit = function(
  ll,
  data,
  trace=FALSE,
  forecast.length=1,
  with.forecast=TRUE,
  ic="AIC",
  garch.model="garch" )
{
  formula = as.formula( paste( sep="",
                               "~ arma(", ll$order[1], ",", ll$order[2], ")+",
                               garch.model,
                               "(", ll$order[3], ",", ll$order[4], ")" ) )
  fit = tryCatch( garchFit( formula=formula,
                            data=data,
                            trace=FALSE,
                            cond.dist=ll$dist ),
                  error=function( err ) TRUE,
                  warning=function( warn ) FALSE )
  
  pp = NULL
  
  if( !is.logical( fit ) ) {
    if( with.forecast ) {
      pp = tryCatch( predict( fit,
                              n.ahead=forecast.length,
                              doplot=FALSE ),
                     error=function( err ) FALSE,
                     warning=function( warn ) FALSE )
      if( is.logical( pp ) ) {
        fit = NULL
      }
    }
  } else {
    fit = NULL
  }
  
  if( trace ) {
    if( is.null( fit ) ) {
      cat( paste( sep="",
                  "   Analyzing (", ll$order[1], ",", ll$order[2],
                  ",", ll$order[3], ",", ll$order[4], ") with ",
                  ll$dist, " distribution done.",
                  "Bad model.\n" ) )
    } else {
      if( with.forecast ) {
        cat( paste( sep="",
                    "   Analyzing (", ll$order[1], ",", ll$order[2], ",",
                    ll$order[3], ",", ll$order[4], ") with ",
                    ll$dist, " distribution done.",
                    "Good model. ", ic, " = ", round(fit@fit$ics[[ic]],6),
                    ", forecast: ",
                    paste( collapse=",", round(pp[,1],4) ), "\n" ) )
      } else {
        cat( paste( sep="",
                    "   Analyzing (", ll[1], ",", ll[2], ",", ll[3], ",", ll[4], ") with ",
                    ll$dist, " distribution done.",
                    "Good model. ", ic, " = ", round(fit@fit$ics[[ic]],6), "\n" ) )
      }
    }
  }
  
  return( fit )
}

garchAuto = function(
  xx,
  min.order=c(0,0,1,1),
  max.order=c(5,5,1,1),
  trace=FALSE,
  cond.dists="sged",
  with.forecast=TRUE,
  forecast.length=1,
  arma.sum=c(0,1e9),
  cores=1,
  ic="AIC",
  garch.model="garch" )
{
  require( fGarch )
  require( parallel )
  
  len = NROW( xx )
  
  models = list( )
  
  for( dist in cond.dists )
    for( p in min.order[1]:max.order[1] )
      for( q in min.order[2]:max.order[2] )
        for( r in min.order[3]:max.order[3] )
          for( s in min.order[4]:max.order[4] )
          {
            pq.sum = p + q
            if( pq.sum <= arma.sum[2] && pq.sum >= arma.sum[1] )
            {
              models[[length( models ) + 1]] = list( order=c( p, q, r, s ), dist=dist )
            }
          }
  
  res = mclapply( models,
                  garchAutoTryFit,
                  data=xx,
                  trace=trace,
                  ic=ic,
                  garch.model=garch.model,
                  forecast.length=forecast.length,
                  with.forecast=TRUE,
                  mc.cores=cores )
  
  best.fit = NULL
  
  best.ic = 1e9
  for( rr in res )
  {
    if( !is.null( rr ) )
    {
      current.ic = rr@fit$ics[[ic]]
      if( current.ic < best.ic )
      {
        best.ic = current.ic
        best.fit = rr
      }
    }
  }
  
  if( best.ic < 1e9 )
  {
    return( best.fit )
  }
  
  return( NULL )
}

returns_data = read.csv(file='./train_set.csv')
fund_names = colnames(returns_data)

all_results = data.frame()
# n_funds = ncol(returns_data)
n_funds = 4

for(i in 2:ncol(returns_data)) {
  fund = unlist(returns_data[i], use.names=FALSE)
  res = garchAuto(fund, min.order=c(0,0,1,1), max.order=c(4,4,2,2))
  
  if (typeof(res) == typeof(NULL)) {
    print(paste0("Failed for fund: ", fund_names[i]))
  } else {
    coeffs = res@fit$par
    se = res@fit$se.coef
    fund_name = fund_names[i]
    coeff_frame = data.frame(coeffs, se, fund_name)
  
    all_results = rbind(coeff_frame, all_results)
    print(paste0("Completed for fund: ", fund_names[i]))
  }
}

write.csv(all_results, './500_mutual_fund_returns_params.csv')