This seems squarely in the "asking for statistical expertise" area, rather than the "how do I make R implement this theory I understand" area, so it is off topic on this mailing list. You might be in better company on Stack Exchange.
On August 22, 2019 5:55:43 AM PDT, Tommaso Ferrari <tomferr...@gmail.com> wrote: >Dear all, >I was analyzing and implementing the DCC model (Dynamic Conditional >Correlation) for the one-day forecast calculation of the >variance-covariance matrix of a system consisting of, approximately, >30 stocks. For each title I consider a historical series of logarithmic >daily returns of 250 samples. >In particular, I was interested in the simulation of this model using >the >t-Student distribution. >In this regard I was using the "rugarch" and "rmgarch" packages. >According to the examples found in the literature, I run my analysis in >the >following way: > >1) specification of the univariate garch model for each stock passing >the >number of degrees of freedom of the t-Student distribution as input >(parameter mshape) > >2) multifitting of the univariate garch models > >3) specification of DCC model with a multivariate t-Student >distribution >(degrees of freedom are not passed as input, in this case) > >4) fitting of the DCC model > >5) variance-covariance matrix forecasting > >I give an example of the code I'm running (qxts is the time series >data, >mshape is the number of degrees of freedom): > ># GARCH(1,1) specification >garch11.spec = ugarchspec(mean.model = list(armaOrder = c(0, 0)), >variance.model = list(garchOrder = c(1, 1), model = "sGARCH"), >distribution.model = "std", fixed.pars = list(shape = mshape)) ># replicate Garch(1,1) spec for both time series >uspec = multispec(replicate(ncol(qxts), garch11.spec)) ># Fit Garch models: hybrid -> in case of non convergence, all solvers >are >used >multf = multifit(uspec, qxts, solver = "hybrid", fit.control = >list(scale = >1)) ># Dcc model spec >dcc.garch11.spec = dccspec(uspec = uspec, dccOrder = c(1, 1), >distribution >= "mvt", model = "DCC") ># Fitting parameters of DCC >dcc.fit = dccfit(dcc.garch11.spec, qxts, fit = multf, fit.control = >list(scale = TRUE)) ># dcc.fit = dccfit(dcc.garch11.spec, qxts, fit = multf, fit.control = >list(scale = 1)) ># Forecast >dcc.fcst = dccforecast(dcc.fit, n.ahead = 1) >varmat = rcov(dcc.fcst)[[dt_cov]][,, 1] > >However, using a number of degrees of freedom of 2.5, I get the >following >error: > >Error in solve.default(A) : system is computationally singular: >reciprocal >condition number = 1.19994e-18 > >If, for example, I change the degrees of freedom from 2.5 to 2.6, the >error >no longer appears. >I would like to know if there is a way to allow the calculation to be >performed even using a number of degrees of freedom equals to 2.5, as I >cannot find any reference in the literature that addresses this >problem. > >I also tried the following idea. >I don't pass the number of degrees of freedom as input, but I make sure >that, for each title, the number of degrees of freedom is calculated >internally by the function multifit. >Obviously, I get different degrees of freedom depending on the stock >considered. >In this case, however, passing the calculated parameters of the >multifit to >the dccfunction, leads to the following error: >'data' must be of a vector type, was 'NULL' >even if the data sample I pass as input contains no null value. > >I would like to know if these problems are due solely to the numerical >values of the data passed as input or if I am conceptually wrong in the >implementation of the method. > >Thanks to all > > [[alternative HTML version deleted]] > >______________________________________________ >R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >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. -- Sent from my phone. Please excuse my brevity. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
