Well, it's not hard to write the code for it, but if you know the secret way to accurately model "abnormal returns," you'll be a far richer man than I quite soon.
Less snidely, one needs to say quite a bit more about a distribution to specify it than "not Gaussian." Michael On Sat, Mar 10, 2012 at 12:46 PM, drsenne <[email protected]> wrote: > Hello > > This is my first post on this forum and I hope someone can help me out. > I have a datafile (weeklyR) with returns of +- 100 companies. > I acquired this computing the following code: > > library("tseries"); > tickers = c("GSPC" , "BP" , "TOT" , "ENI.MI" , "VOW.BE" , "CS.PA" , > "DAI.DE" , "ALV.DE" , "EOAN.DE" , "CA.PA" , "G.MI" , "DE" > , "EXR.MI" , > "MUV2.BE" , "UG.PA" , "PRU.L", "VOD.L" , "DPB.BE" , "REP.MC" , "RWE.BE" , > "AGN.AS" , "FTE.PA" , "EAD" , "LGEN.L" , "CNP.PA" , "ULVR.L" , "TKA.BE" , > "RIO.L" , "NOK" , "SGO.PA" , "RNO.PA" , "VIE.PA" , "BAYN.DE" , "SAN.PA" , > "DG.PA" , "SSE.L" , "GSK.L" , "EN.PA" , "LYB" , "MLSNP.PA" , "IBE.MC" , > "EURS.PA" , "AH.AS" , "VIV.PA" , "TIT.MI" , "VOLV-B.ST" , "ABI.BR" , > "LHA.DE" , "OML.L" , "CNA.L" , "CON.DE" , "PHG" , "AZN.L" , "SBRY.L" , > "BA.L" , "BT-A.L" , "AF.PA" , "430021.VI" , "SL.L" , "ERIC-A.ST" , "CDI.PA" > , "AAL.L" , "ALO.PA" , "DELB.BR" , "HOT.BE" , "GAS.MC" , "SU.PA" , "OR.PA" , > "FNC.MI" , "MRW.L" , "MAP.MC" , "ML.PA" , "IMT.L" , "EBK.DE" , "PP.PA" , > "ACN" , "BTI" , "CRG.IR" , "CPG.L" , "BN.PA" , "NG.L" , "T7L.BE" , "HEIA.AS" > , "ACS.MC" , "LG.PA" , "STAN.L" , "ALU.PA" , "FRE.MU" , "SW.PA" , "WOS.L" , > "AKZA.AS" , "HEN.MU") > for( series in tickers ){ > print(series) > close <- > get.hist.quote(instrument=series,retclass="zoo",quote="AdjClose",compression="d", > start="2000-1-1", end="2011-12-31",quiet=TRUE) > if(series==tickers[1]){ pricedata = close }else{ pricedata = merge( > pricedata , close ) } > } > colnames(pricedata) = tickers > # Avoid a missing because of trade halt for that stock > pricedata = na.approx(pricedata) > weeklyR = diff(log(pricedata)) > time(weeklyR) = as.Date(time(weeklyR)) > print(weeklyR) > save(weeklyR , file = "weeklyR.Rdata") > write.zoo(weeklyR,file="weeklyR.csv",quote=T,sep=",", na = "NA", dec = "." , > row.names = F,col.names = T) > > Now I need to make a market model in R so i can generate abnormal returns > from these stocks. As market index I would like to use the GSPC. I also need > to consider abnormal returns calculated over a sixty-trading-day window. > Can this be done in R? Is it difficult to write this code? > > Any help would be much appreciated! > > thanks > > drsenne > > > -- > View this message in context: > http://r.789695.n4.nabble.com/Generating-abnormal-returns-in-R-tp4462541p4462541.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
