[R-SIG-Finance] LPPL model for bubble burst forcasting
Brian G. Peterson
brian at braverock.com
Thu Jul 16 15:25:12 CEST 2009
So first, using your real name and ideally your professional identity,
ask for the python code. Better yet, get an academic buddy to do it.
Usually getting access to the code isn't too tough. Mention things like
"repeatable research" and "collaboration" in your email. Two of the
authors publish their email addresses in one of the papers you
reference, so contacting them should be easy.
Next port the python code to R.
If you can't do that, then replicate the model in R "from scratch". A
trivial scan of the paper in question lends several techniques that are
well covered in R: AR, GARCH, power laws, linear regression, stochastic
discount factor, Ornstein-Uhlenbeck, etc.
There are volumes of information available on these topics from within
R, in numerous books, and in the archives of this mailing list and r-help.
You're going to have to do your replication in pieces, probably starting
with their implementation of the log periodic power law (LPPL), for
which I do not believe there is an existing direct analogue in R though
all the component parts necessary to replicate it should be readily
As you work on each step of the replication, share your code with this
list and the problems you are having with a particular step. Ask
specific, directed questions with code to back them up. Someone will
likely help you solve the specific problem.
In R generally, it is not necessary that you be able to *do* the math
(think pencil and paper), but if you plan to replicate published work,
it will be necessary to *understand* at least some of how the math
works, and to be able to pick out the names of techniques that you can
search for an utilize.
Basically, I'm recommending that you (specifically) and others (more
generally) should share the process of replicating a technique like
this, as well as the final product, to give all the rest of us who are
likely to be helping "you" get all this done. quid pro quo.
Brian G. Peterson
> Prof. Sornette has spent years forcasting bubble burst with
> "log-periodic power law". The latest paper gives "a
> self-consistent model for explosive financial bubbles, which combines
> a mean-reverting volatility process and a stochastic conditional
> return which reflects nonlinear positive feedbacks and continuous
> updates of the investors' beliefs and sentiments."
> And his latest predicting is the burst of Chinese equity bubble at
> the end of July. http://arxiv.org/abs/0907.1827
> While waiting to see the result, I wonder whether it is possible to
> replicate the forcast with R. The model is in the page 10 of the "A
> Consistent Model of `Explosive' Financial Bubbles With Mean-Reversing
> Residuals", http://arxiv.org/abs/0905.0128 . The output chart is
> in the page 3 of "The Chinese Equity Bubble: Ready to Burst",
> http://arxiv.org/abs/0907.1827 . I guess the authors of the latter
> paper use the same model as described in the first paper.
> Because statistics is still challenging for me though I could use R
> for basic data manipulations, I wonder which package or function
> would be necessary to implement the model in the paper. The model
> seems more complicated than the models in the R tutorials for me.
> By the way, the author of the paper used Python and the codes are
> Any suggestion would be highly appreciated.
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