[Blotter-commits] r1028 - in pkg/RTAQ: . R inst/doc man

[noreply at r-forge.r-project.org]

Author: jonathan
Date: 2012-05-21 05:15:05 +0200 (Mon, 21 May 2012)
New Revision: 1028

Added:
   pkg/RTAQ/R/HAR_model.R
   pkg/RTAQ/inst/doc/RTAQ_vignette.Rnw
   pkg/RTAQ/inst/doc/RTAQ_vignette.pdf
   pkg/RTAQ/man/harModel.Rd
Modified:
   pkg/RTAQ/NAMESPACE
Log:
vignette added and first version of HARmodel implemented

Modified: pkg/RTAQ/NAMESPACE
===================================================================
--- pkg/RTAQ/NAMESPACE	2012-05-18 17:49:04 UTC (rev 1027)
+++ pkg/RTAQ/NAMESPACE	2012-05-21 03:15:05 UTC (rev 1028)
@@ -1,5 +1,4 @@
 useDynLib(RTAQ, .registration = TRUE);
-
 #export(anova.trls, anovalist.trls, correlogram, expcov, gaucov, Kaver,
 #       Kenvl, Kfn, plot.trls, ppgetregion, ppinit, pplik, ppregion,
 #       predict.trls, prmat, Psim, semat, sphercov, SSI, Strauss,
@@ -7,6 +6,7 @@
 
 export(
 #cleaning
+harModel,
 autoSelectExchangeQuotes, 
 autoSelectExchangeTrades, 
 exchangeHoursOnly,
@@ -70,7 +70,8 @@
 #maxvol                       #new
 )
 
-#importFrom(mvtnorm, dmvnorm);
-#importFrom(cubature, adaptIntegrate);
+S3method(print, harModel);
+S3method(summary, harModel);
+S3method(plot, harModel);
 
-#S3method(summary,trls)
+

Added: pkg/RTAQ/R/HAR_model.R
===================================================================
--- pkg/RTAQ/R/HAR_model.R	                        (rev 0)
+++ pkg/RTAQ/R/HAR_model.R	2012-05-21 03:15:05 UTC (rev 1028)
@@ -0,0 +1,220 @@
+ #  START implementation of paper:
+ #  ROUGHING IT UP: INCLUDING JUMP COMPONENTS IN THE MEASUREMENT, MODELING, AND FORECASTING OF RETURN VOLATILITY
+ #  Torben G. Andersen, Tim Bollerslev, and Francis X. Diebold
+ #  data: a xts object with the intraday data
+ #  periods: a vector with time periods to aggregate over, expressed in days
+ #  RVest: estimator for daily realized volatility, 
+ #  in case a vector is supplied, the first estimator is the unrobust estimator, the second is the robust estimator 
+ #  type: string defining the type of model
+ #  "HARRV" from "roughing paper"
+ #  "HARRVJ" from "roughing paper"
+ #  "HARRVCJ" from "roughing paper"
+ #  jumptest: function to calculate the jump test statistic which determines whether the daily jump contribution is significant
+ #  alpha: a value between zero and one to indicate what
+ #  h: integer, determining over how many periods the depend variable should be aggregated. The default is 1, i.e. no aggregation is done, just one day. 
+ #  TODO ADD extra argument: jump-periods??? for aggregated jumps in the model...
+ 
+ # Helpfunctions: 
+ TQfun = function(rdata){ #Calculate the realized tripower quarticity
+   returns = as.vector(as.numeric(rdata));
+   n = length(returns);
+   mu43 = 0.8308609; #    2^(2/3)*gamma(7/6) *gamma(1/2)^(-1)   
+   tq = n * ((mu43)^(-3)) *  sum( abs(returns[1:(n - 2)])^(4/3) *abs(returns[2:(n-1)])^(4/3) *abs(returns[3:n])^(4/3) );
+   return(tq);
+ } 
+ 
+ ABDJumptest = function(RV, BPV, TQ){ # Comput jump detection stat mentioned in roughing paper
+   mu1  = sqrt(2/pi);
+   n = length(RV);
+   zstat = ((1/n)^(-1/2))*((RV-BPV)/RV)*(  (mu1^(-4) + 2*(mu1^(-2))-5) * pmax( 1,TQ*(BPV^(-2)) )   )^(-1/2); 
+  return(zstat);
+ }
+
+ harModel = function(data, periods = c(1,5,22), RVest = c("RCov","RBPCov"), type="HARRV", jumptest="ABDJumptest",alpha=0.05,h=1,transform=NULL,...){  
+  nperiods = length(periods); # Number of periods to aggregate over
+  nest = length(RVest);       # Number of RV estimators
+  if( !is.null(transform) ){ Ftransform = match.fun(transform); }
+  if( !(type %in% c("HARRV","HARRVJ","HARRVCJ"))){ warning("Please provide a valid argument for type, see documentation.")  }    
+  
+  if( sum(data<0) != 0 ){ #If it are returns as input
+   # Get the daily RMs (in a non-robust and robust way)
+   RV1 = match.fun(  RVest[1]);
+   RM1 = apply.daily( data, RV1 );
+   #save dates:
+   alldates = index(RM1)
+   if( nest == 2 ){ 
+    RV2 = match.fun( RVest[2]); 
+    RM2 = apply.daily( data, RV2 ); }
+  } 
+  
+  
+  if( sum(data<0) == 0 ){ #The input is most likely already realized measures
+     dimdata = dim(data)[2]; 
+     alldates = index(data);
+     RM1 = data[,1];
+     if( dimdata > 1 ){ RM2 = data[,2]; } 
+     if( type != "HARRV" ){ warning("Please provide returns as input for the type of model you want to estimate. All your returns are positive which is quite unlikely honestly. Only for the HAR-RV model you can input realized measures.") }
+     }
+ 
+    # Get the matrix for estimation of linear model
+    maxp      = max(periods); #Number of aggregation levels
+    n         = length(RM1);  #Number of Days
+    RVmatrix1 = matrix(nrow=n,ncol=nperiods);
+
+  for(i in 1:nperiods){ 
+   if(periods[i]==1){ RVmatrix1[,i] = RM1 
+   }else{ RVmatrix1[(periods[i]:n),i] = rollmean(x=RM1,k=periods[i],align="left")  }
+  } #end loop over periods for standard RV estimator
+  colnames(RVmatrix1) = paste("RV",periods,sep="");
+  
+  # Aggregate and subselect y
+  if( h == 1 ){  y  = RM1[(maxp+1):n]; }
+  if( h != 1 ){ 
+      y = matrix( nrow=length(RM1), ncol=1 ); colnames(y) = "y";
+      y[(h:n),] = rollmean(x=RM1,k=h,align="left");
+      y = matrix(y[((maxp+h):n),],ncol=1); y=as.data.frame(y) }  
+  
+  # Only keep useful parts:
+  x1 = RVmatrix1[(maxp:(n-h)),];
+  
+ # TODO: add transformations here (srqr,log,..) see paper             
+ if(nest==2){ # In case a jumprobust estimator is supplied
+   RVmatrix2 = matrix(nrow=n,ncol=nperiods);
+   for(i in 1:nperiods){ 
+     if(periods[i]==1){ RVmatrix2[,i] = RM2; 
+     }else{ RVmatrix2[(periods[i]:n),i] = rollmean(x=RM2,k=periods[i],align="left")  }
+     colnames(RVmatrix2) = paste("RV",periods,sep=""); 
+     x2 = RVmatrix2[(maxp:(n-h)),];
+   } #end loop over periods for robust RV estimator  
+ }  
+
+  # Estimate the model parameters, according to type of model : 
+  # First model type: traditional HAR-RV: 
+  if( type == "HARRV" ){ 
+    if(!is.null(transform)){ y = Ftransform(y); x1 = Ftransform(x1) }                             
+      model     = estimhar(y=y,x=x1); 
+      model$transform = transform; model$h = h; model$type = "HARRV"; model$dates = alldates[(maxp+h):n];
+      class(model) = c("harModel","lm"); 
+      return( model )
+   } #End HAR-RV if cond
+
+  if( type == "HARRVJ" ){    
+      J = pmax( RM1 - RM2,0 );      #Jump contributions should be positive
+      J = matrix(J[(maxp:(n-h)),]); colnames(J) = "J"; 
+      x = cbind(x1,J);              # bind jumps to RV data 
+      if(!is.null(transform)){ y = Ftransform(y); x = Ftransform(x); }       
+      model        = estimhar(y=y,x=x); 
+      model$transform = transform; model$h = h; model$type = "HARRVJ"; model$dates = alldates[(maxp+h):n];
+      class(model) = c("harModel","lm"); 
+      return( model )    
+  }#End HAR-RV-J if cond
+  
+  if( type == "HARRVCJ" ){
+      # Get the jumps:
+      J = pmax( RM1 - RM2,0 );      # Jump contributions should be positive
+      # Are the jumps significant? if not set to zero:
+      if( jumptest=="ABDJumptest" ){ 
+        
+      TQ = apply.daily(data, TQfun); 
+      teststats    = ABDJumptest(RV=RM1,BPV=RM2,TQ=TQ ); 
+      }else{ jtest = match.fun(jumptest); teststats = jtest(data,...) }  
+      Jindicators  = teststats > qnorm(1-alpha); 
+      J[!Jindicators] = 0; 
+      J = matrix(J[(maxp:(n-h)),]); colnames(J) = "J"; Jindicators = Jindicators[(maxp:(n-h))];
+      # Get continuus components if necessary RV measures if necessary:
+      Cmatrix = matrix( nrow = dim(x1)[1], ncol = dim(x1)[2] ); 
+      Cmatrix[Jindicators,]    = x2[Jindicators,];      #Fill with robust one in case of jump
+      Cmatrix[(!Jindicators),] = x1[(!Jindicators),];   #Fill with non-robust one in case of no-jump  
+      colnames(Cmatrix) = paste("C",periods,sep="");
+      
+      x = cbind(Cmatrix,J);               # bind jumps to RV data
+      if(!is.null(transform)){ y = Ftransform(y); x = Ftransform(x); }       
+      model = estimhar( y=y, x=x ); 
+      model$transform = transform; model$h = h; model$type = "HARRVCJ"; model$dates = alldates[(maxp+h):n];      
+      class(model) = c("harModel","lm");
+      return(model)
+      } 
+
+} #End function harModel
+ 
+ estimhar = function(y, x){ #Potentially add stuff here
+   colnames(y)="y";
+   output = lm( formula(y~x), data=cbind(y,x));
+ }
+ 
+ # Help function to get nicely formatted formula's for print/summary methods..
+ getHarmodelformula = function(x){
+  modelnames = colnames(x$model$x);
+  if(!is.null(x$transform)){ 
+    
+   modelnames = paste(x$transform,"(",modelnames,")",sep=""); } #Added visual tingie for plotting transformed RV
+   betas      = paste("beta",(1:length(modelnames)),"",sep="")
+   betas2     = paste(" + ",betas,"*")
+   rightside  = paste(betas2, modelnames,collapse="");
+   h = x$h;
+   left = paste("RV",h,sep="");
+   if(!is.null(x$transform)){  left = paste(x$transform,"(",left,")",sep="" ) }
+   modeldescription = paste(left,"= beta0",rightside);
+  return(list(modeldescription,betas))  
+ }
+ 
+ # Print method for harmodel:  
+ print.harModel = function(x, digits = max(3, getOption("digits") - 3), ...){ 
+   formula = getHarmodelformula(x); modeldescription = formula[[1]]; betas = formula[[2]];
+   
+   cat("\nModel:\n", paste(modeldescription, sep = "\n", collapse = "\n"), 
+       "\n\n", sep = "")
+   
+   coefs = coef(x);
+   names(coefs)  = c("beta0",betas)
+   
+   if (length(coef(x))){
+     cat("Coefficients:\n")
+     print.default(format(coefs, digits = digits), print.gap = 2,quote = FALSE);
+     cat("\n\n");
+     Rs = summary(x)[c("r.squared", "adj.r.squared")]
+     zz = c(Rs$r.squared,Rs$adj.r.squared);
+     names(zz) = c("r.squared","adj.r.squared")
+     print.default((format(zz,digits=digits) ),print.gap = 2,quote=FALSE)
+   }
+   else cat("No coefficients\n")
+   cat("\n")
+   invisible(x)
+ } 
+  
+ summary.harModel = function(object, correlation = FALSE, symbolic.cor = FALSE,...){
+   x=object; 
+   dd = summary.lm(x);
+   formula = getHarmodelformula(x); modeldescription = formula[[1]]; betas = formula[[2]];
+   dd$call = modeldescription;
+   rownames(dd$coefficients) = c("beta0",betas);
+   return(dd)
+ }
+
+ plot.harModel = function(x, which = c(1L:3L, 5L), caption = list("Residuals vs Fitted", 
+                                                                  "Normal Q-Q", "Scale-Location", "Cook's distance", "Residuals vs Leverage", 
+                                                                  expression("Cook's dist vs Leverage  " * h[ii]/(1 - h[ii]))), 
+                          panel = if (add.smooth) panel.smooth else points, sub.caption = NULL, 
+                          main = "", ask = prod(par("mfcol")) < length(which) && dev.interactive(), 
+                          ..., id.n = 3, labels.id = names(residuals(x)), cex.id = 0.75, 
+                          qqline = TRUE, cook.levels = c(0.5, 1), add.smooth = getOption("add.smooth"), 
+                          label.pos = c(4, 2), cex.caption = 1){ 
+  observed = x$model$y;
+  fitted   = x$fitted.values;
+  dates    = x$dates;
+  observed = xts(observed, order.by=dates);
+  fitted   = xts(fitted, order.by=dates);
+  type     = x$type;
+  
+  g_range = range(fitted,observed)
+  g_range[1] = 0.95*g_range[1]; g_range[2]= 1.05 * g_range[2]; 
+  #ind = seq(1,length(fitted),length.out=5);
+  title = paste("Observed and forecasted RV based on HAR Model:",type);
+  plot.zoo(observed,col="red",lwd=2,main=title, ylim=g_range,xlab="Time",ylab="Realized Volatility"); 
+  #  axis(1,time(b)[ind], format(time(b)[ind],), las=2, cex.axis=0.8); not used anymore
+  #  axis(2);
+  lines(fitted,col="blue",lwd=2);
+  legend("topleft", c("Observed RV","Forecasted RV"), cex=0.8, col=c("red","blue"),lty=1, lwd=2, bty="n"); 
+}
+ 
+ 
\ No newline at end of file

Added: pkg/RTAQ/inst/doc/RTAQ_vignette.Rnw
===================================================================
--- pkg/RTAQ/inst/doc/RTAQ_vignette.Rnw	                        (rev 0)
+++ pkg/RTAQ/inst/doc/RTAQ_vignette.Rnw	2012-05-21 03:15:05 UTC (rev 1028)
@@ -0,0 +1,4 @@
+%\VignetteIndexEntry{RTAQ_vignette}
+\documentclass{article}
+\begin{document}
+\end{document}
\ No newline at end of file

Added: pkg/RTAQ/inst/doc/RTAQ_vignette.pdf
===================================================================
--- pkg/RTAQ/inst/doc/RTAQ_vignette.pdf	                        (rev 0)
+++ pkg/RTAQ/inst/doc/RTAQ_vignette.pdf	2012-05-21 03:15:05 UTC (rev 1028)
@@ -0,0 +1,3139 @@
+%PDF-1.4
+%ÐÔÅØ
+4 0 obj
+<< /S /GoTo /D (Section.0.General\040information.1) >>
+endobj
+7 0 obj
+(General information)
+endobj
+8 0 obj
+<< /S /GoTo /D (Section.1.TAQ\040data\040management\040essentials.1) >>
+endobj
+11 0 obj
+(TAQ data management essentials)
+endobj
+12 0 obj
+<< /S /GoTo /D (Subsection.2.0.Raw\040data\040conversion.2) >>
+endobj
+15 0 obj
+(Raw data conversion)
+endobj
+16 0 obj
+<< /S /GoTo /D (Subsection.2.1.Trades\040and\040quotes\040data\040description.2) >>
+endobj
+19 0 obj
+(Trades and quotes data description)
+endobj
+20 0 obj
+<< /S /GoTo /D (Subsection.2.2.Data\040cleaning.2) >>
+endobj
+23 0 obj
+(Data cleaning)
+endobj
+24 0 obj
+<< /S /GoTo /D (Section.2.Liquidity.1) >>
+endobj
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+(Liquidity)
+endobj
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+endobj
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+endobj
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+<< /S /GoTo /D (Subsection.3.2.Liquidity\040measures.2) >>
+endobj
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+(Liquidity measures)
+endobj
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+<< /S /GoTo /D (Section.3.Aggregation\040and\040volatility.1) >>
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[TRUNCATED]

To get the complete diff run:
    svnlook diff /svnroot/blotter -r 1028