Comments and Ratings

Hello Marcelo,
I am student in Turkey, at Istanbul Technical University.And I have a problem about to create mat-file . I must to use my excell data .How can I solve this problem. I think that "Creating Mat file in C "; but I can not solve my problem because I don't have enough source . Could you help me?? Thanks for your all attention.

Şahinder Kaya
İstanbul Technical University

Mathematics Engineering
kayasah@itu.edu.tr

Hi Michele, the benchmark asset is not downloaded. If you want it, just include the ticker in example_ticker.txt.

Marcelo.

Oi Felipe,

Agradeco pelo interesse no programa.
Favor entrar em contato pelo meu email.

Abraco.
Marcelo.

Great stuff, worked like a charm! thank you!!

Dear Marcelo,

This still does not solve the problem. I had a check on the probabilities: for k=2 I was checking if sum of the elements of the transition matrix adds up to 2, and it was not! well transposed or not transposed, rows or columns, does not matter now ...

For comment B decreasing search space based on my comment is agreed then.

Best of Wishes
MehdiHK

Dear Marcelo,

A) I am pretty sure that the probabilities do not sum up to the correct value. As a check: for a k=2 case, try to check if sum of all the elements in Coeff.p always is equal to 1 in all the cases!

B) About the search space, for a case of k=2, not that ONE parameters are more than enough, but that TWO are more than enough b/c P=[p11 p12; p21 p22] is identified if from each ROW exactly ONE element is know so 2 element is enough not 3. (you mentioned that 4 can be decreased by 1!!!)

C) ROWS should sum up to ONE not columns.

I closely looked into all of your codes. They are neat, but you have some mistakes that should be fixed.

Thanks a lot
Mehdi

Well, I decided to recode things from the beginning. I think that the p-values are wrong which is because of the problem I mentioned before with the way you enter the probabilities into the search space. This is not just a dimension problem. When you calculate a numerical gradient, for the probability param's for example (for k=2), you change p11 while p12 is constant! In this case even if p11+p12 was 1, it will not be one when feeding it to the likelihood function!!
Another point is that you need to make sure that sum of the probabilities for state j (j=1,2,...,k) is 1. This is not guaranteed in your program.

Hi gajd, fell free to contact me in my email.
Cheers.

Hi V,

Thanks for you comment.

Sorry for the late reply. I just realized you comment on file.

Yes, this would speed up the algorithm, but, it also degenerates the Qdist matrix with respect of the infomration I need to find the pairs with the pairs(:,1)=n; command. .

So, I employed a quick fix based on your idea. See new updated file for details.

Again, thanks.
Cheers.
Marcelo.

edited your "pairs.m" file. noticed that when you use the least-squares sum method, the matrix you get is symmetric. therefore, I calculated the lower triangular only, 'nan' on diagonals, and copied over the transpose cell pair. When I set ut very low, this speedup really shows:

for i=1:n2
    for j=1:(i-1)
        Qdist(i,j)=sum((x(:,i)-x(:,j)).^2);
        Qdist(j,i)=Qdist(i,j);
    end
    Qdist(i,i)=nan;
end

last note: i don't think you use pairs(:,2)=m'; anywhere in the parent scripts, maybe you could eliminate it.

Hi, I'm new in using matlab, but I would need to use a Markov switching model with time varying probabilities, evolving as logistic functions of the independent variables (as in Diebold and Weinbach 1994) . Can you, please, tell me how can I use your program in order to do that?
Thank you!

Hi Bob,

Thanks for you comment.

Sorry for the late reply (vacation was great!).

I agree with you that the way I normalized the time series of prices is not realistic. At the time I wrote the paper (which produced the matlab code) that seemed to be a good way of bringing all stock prices to the same unit but, today, I can see the drawbacks.

I cannot change the paper (it is already published) but I'm going to add another option of normalization to the algorithm.

Regards,
Marcelo.

thanks for the quick response. Generally speaking, the code is already very useful, good work.

Hi, what kind of parameter evaluation approach you are using in this package? is it the EM algorithm?

great!thanks your

Hi Anton,

I'm the author.
Thanks for your comments, I really apreciate it.

Regarding your question, no I haven't tried endogenous switching.

Cheers.
Marcelo.

Thanks!!!

Bugs where? Squash them! Fast, before they multiply!

I have to agree with the first commenter. What's the point? There are much more complete Kalman filter packages around (e.g., The SSM Toolbox). This merely does the same in a more limited way.
At the same time, the author is quoting Kim and Nelson. The only justification for using this package would be that it actually implements all of the Gauss codes from Kim-Nelson (i.e., SSMs with Regime Shift and heteroskedasticity, and the same in a Bayesian framework).

Unfortunately, there is a significant amount of work to be done (i.e., only about 20% of the total task has been completed).

thank you! it is useful to me!

Hey, Marcelo,
 this is a great program. And I am working on this topic too. I always have a question about the data. As Gatev mentioned in his paper, he used the data covering from 1962 to 2002. How did he(how did you)handle the missing values? There got to be some missing values(for example, trading closed for a particular stock). Thanks again for your grogram.

Hi Jon,

You're right. I wrote this function for using it in the ssrn paper. In there I didn't used the correlation method. And I never bother to check if it was working. The 'correlation' method is not even an option to the main algortihm. If you wanted to change it, you would had to go to the m file and write it.

Somehow the line corr(idx1,idx2)=0; doesn't take the diagonal out as it should be. Your approach will work, but I'm changing it to corr(corr==1)=0; which I think its a little more intuitive. The new files whould be up tomorrow.

Thanks for your message and rating. I really appreciate it.

Cheers.
Marcelo.

Excellent

Thank you Marcelo! It was indeed very helpful in understanding those papers.

Very useful. Thanks for sharing the codes.