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I will be joining a risk management firm in a few months, and I was wondering if some of you could help we with resources on certain methods.

Some of the things that I would be called upon to work on are calibrating models using simulated methods of moments or Kalman filters. I am also supposed to work on Bayesian modeling though the details havent been discussed yet.

While the theoretical foundations for all these methods and techniques can be found in books and on the web, I wanted to get an understanding of how these methods are applied in finance. So resources like data-sets or projects that implement these things or at least guide (some of the way, not completely) would be helpful.

A little bit about me: I am a recent PhD in physics and economics. So technical things are fine with me. I have sufficient programming experience in python and c++. Thus, I am really looking for practical applications, given that I have little to no experience in Finance.

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    $\begingroup$ Hi and welcome. Helpful (?) google search phrases that come to my mind here are "Markov Chain Monte Carlo Finance" (never really took off, though. but really interesting) or "Bayesian credit risk". Also, there's a introductory book by Rachev et al.: onlinelibrary.wiley.com/doi/book/10.1002/9781119202141. But maybe these suggestions are way off track? $\endgroup$ Jan 26 at 9:56
  • $\begingroup$ Thanks for the comment. I took a look at the contents of the book and it does seem a good place to start. I was wondering however whether there could be examples of situations (with data sets) where I could apply some of these ideas. $\endgroup$
    – bfg
    Jan 26 at 16:24
  • $\begingroup$ Hi: I would look at papers by Chib and or Greenberg. They usually take a data set and run some kind of example and their papers are well written. $\endgroup$
    – mark leeds
    Jan 26 at 17:20
  • $\begingroup$ @markleeds: Thanks a lot for the suggestion. What would be more general texts for risk management and quantitative portfolio management techniques ? $\endgroup$
    – bfg
    Jan 27 at 19:46
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    $\begingroup$ Hi: If you by that, you mean bayesian, I don't know. If you don't mean bayesian then the texts by grinold and kahn, rudd and clasing or stephen taylor would be good. First two are theory, last one is applied. I don't remember titles but if you can't find them, let me know. $\endgroup$
    – mark leeds
    Jan 28 at 15:14

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