<r>QuoteOriginally posted by: spursfanwhy are you humouring this guy? - garbage in, garbage outboth piterbarg and Cuchulainn have pointed to the Leif Andersen paper (and indeed I've written some VBA code in one of my Wilmott columns)He is just being nice to a student. We should be pleased to have su...

<t>QuoteOriginally posted by: CuchulainnNo problem. The more posts the merrier You are saying that if the underlying becomes negative in path generation then make it == 0? If so, this is anathema to a numerical analyst. Ugh!Many use Euler, that's true. But there is more... If you look at my recent p...

<t>QuoteOriginally posted by: CuchulainnQuoteOriginally posted by: quartzThat looks like an Euler discretization... Without using the exact solution (which would take quite some more effort to code) it would then be really odd to obtain the same result for different step refinements. Just histogram ...

<t>QuoteOriginally posted by: AlanMaybe some kind of sector/industry model would be better; certainly more feasible.What are you planning to do with these 4000 time series if you could generate them?Alan,My application is purely time series. I have fitted a model (with a certain correlation structur...

<r>QuoteOriginally posted by: PolterI think it's simple to just use Cholesky decomposition for this. Check here for example in R:<URL url="http://astrostatistics.psu.edu/su07/R/mv.htmlStill">http://astrostatistics.psu.edu/su07/R/mv.htmlStill</URL>, R has plenty of packages which have this functional...

My goal is to use the decomposition to generate a multivariate Gaussian random vector with my X matrix as its covariance. Unless I am mistaken, this is the standard way to do this.BTW, I tried the symmetric=TRUE part...however, my computer crashed..so I guess its not gonna work!

<t>Thanks for the suggestion. I am indeed using eigen(X)..I haven't tried symmetric=TRUE. The R help thingie says:symmetric: if 'TRUE', the matrix is assumed to be symmetric (or Hermitian if complex) and only its lower triangle is used. If 'symmetric' is not specified, the matrix is inspected for sy...

<t>I have a variance covariance matrix which is of a high dimension...4000x4000. I am using R and trying to do a spectral decomposition..however, due to the size of the matrix R reports memory issues and is unable to do it. I was wondering whether there is a way for me to do this.. perhaps by breaki...

The standard reference for long memory processes is the book by Beran. Check out Brockwell and Davis (Time Series: Theory and Methods) as well. They have a section devoted to this as well....as far as my memory takes me.

<t>QuoteOriginally posted by: mizhaelQuoteOriginally posted by: JediQuantApologies for the intrusion..here's a related question from me...:I have recently acquired myself Javaher's book, 'Inside Volatility Arbitrage'. I must say the book has proved to be really useful and is quite beautifully writte...

<t>Many thanks for your comments Rez! I agree with what you said about particle filters probably will turn out to be as good/crap as anything else with randomly chosen starting values. In fact, Javaheri does do alot of simulation exercises and does demonstrate that one doesn't always get parameters ...

<t>Apologies for the intrusion..here's a related question from me...:I have recently acquired myself Javaher's book, 'Inside Volatility Arbitrage'. I must say the book has proved to be really useful and is quite beautifully written.My question is whether anyone here has implemented any of the partic...

Hey Yoda! What up dude?

<t>Hey folks!I have recently acquired myself Javaher's book, 'Inside Volatility Arbitrage'. I must say the book has proved to be really useful and is quite beautifully written. My question is whether anyone here has implemented any of the particle filters he gives for estimating jump diffusions with...

QuoteOriginally posted by: dj99bDepends on where you are in the world !Yep, my bad! I forgot! lol. I am in London :-)Thanks alot for your input guys. I have already spent money on Joshi's book...so, I think as a start I will set a timetable to work through this in my spare time. JQ.