| 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 ) | |
| } |
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