[R-SIG-Finance] LPPL model for bubble burst forcasting

[Wind]

Thanks Brian.  You're always warm-hearted and very professional.
I will try my best following your detailed instructions.   It would be
a great improvement for myself if I could work out the final solution
with the help of the list.    Since I am just an independent
individual investor with only master degree in finance,  I guess it
would take some time.
Thanks again.   I will post the progress to the list if there is some
progress :)

wind

On Thu, Jul 16, 2009 at 9:25 PM, Brian G. Peterson<brian at braverock.com> wrote:
> 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 available.
>
> 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.
>
> Cheers,
>
>  - Brian
>
>
> --
> Brian G. Peterson
> http://braverock.com/brian/
> Ph: 773-459-4973
> IM: bgpbraverock
>
>
>
> Wind wrote:
>>
>> 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
>> private.
>>
>> Any suggestion would be highly appreciated.
>>
>
>
>