Intuitively, Maximum Likelihood inference on high frequency data should be slow, because of the large data set size. I was wondering if anyone has experience with slow inference, I can make optimization algorithms to speed up the infrence then.
I tried this with Yacine Ait Sahalia work on estimating diffusion models, using his code, which (Unfortunately!) is pretty fast, even for large data set. Now does any one know any large slow high frequency financial econometric problem do let me know,
For large samples exact maximum likelihood can be approached reasonably well by faster estimation methods. But I do not understand why you want slow methods. As far as I know, Ait Sahalia code is good. Why do you say "(Unfortunately!)" ?
I wanted to contribute (publish papers) by making faster optimization algorithms that is why I said " Unfortunately ".
I tried his code, it was kinda slow for very very large data sets. Say you want to estimate the parameters using 10 years of tick data, which is a huge data set. For this dataset even Ait Sahalia's univariate case is too slow, and multivariate estimation is another problem. What do you say? or do you know any other problems in this area where I could contribute by making faster algorithms
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