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 stoch. volatility? I have tried to, but, I notice it takes so much time! My dataset has 4,000 values and plus with a 1,000 particles...each function evaluation takes too too much time! I am using the Nelder-Meade algorithm for optimisation, however, that runs into problems! What I normally tend to do with MLE is use many different random starting values and then select the set which gives the highest likelihood. However, in the particle filtering method, evaluating the function take so so much time that it seems quite absurd for me to use multiple starting values. In fact, in Javaher's book, the examples he uses...he tends to use starting parameter values which are quite close to the actual ones (he plays with simulated data). Any thoughts on this? Are there any faster algorithms? Any references people can point me towards. Many thanks!

Greetings, young Jedi. Long time ago, train you I did. Remember, do you?

Hey Yoda! What up dude?

hi JediQuantI was able to speed up the function considerably in matlab using mex calls. I used the genetic algo/pattern search toolbox along with fminsearch and for the default model the results are acceptable and reasonably fast. The ML approach doesnt seem to work too well when i change the model to have a gaussian jump size. Did you have any ideas on this?thanks.(ps: tried it in R too with vectorization, but for me it was considerably slower than matlab)