By Christoph Hackl
The Libor industry version (LMM) is a mathematical version for pricing and possibility administration of rate of interest derivatives and has been equipped at the framework of modelling ahead premiums. For the conceptual figuring out of the version a robust heritage within the fields of arithmetic, statistics, finance and particularly for implementation, computing device technology is critical. The ebook offers the ne cessary foundation to appreciate the LMM and grants a framework to enforce a operating version the place attainable calibration and parameterization equipment for volatility and correlation are defined. precise emphasis lies additionally at the exchange off of velocity and correctness the place modifications in making a choice on random quantity turbines and some great benefits of issue relief are proven.
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Extra info for Calibration and Parameterization Methods for the Libor Market Model (BestMasters)
Gatarek, P. Bachert, and R. Maksymiuk. The LIBOR market model in practice. John Wiley & Sons, 2006. M. Gilli, S. Große, and E. Schumann. Calibrating the nelson-siegel-svensson model. P. Glasserman. Monte Carlo methods in financial engineering, volume 53. Springer Verlag, 2004. F. Jamshidian. Libor and swap market models and measures. Finance and Stochastics, 1(4):293–330, 1997. 57 C. 1007/978-3-658-04688-0, © Springer Fachmedien Wiesbaden 2014 7. S. Joshi. The concepts and practice of mathematical finance, volume 2.
3. , m. How much factors are necessary depends on the correlation structure, which is especially important for products like Bermudan swaptions where several swaption exercise dates are available. So far the model has as many factors as forward rates, which is not the most efficient solution when computation time is an important factor. The reduction of factors to the most important factors, which are in the case of the LMM not more than 4 factors, see Joshi  who writes that there is no more improvement in increasing the factors to more than 3 or 4 factors, would therefore be advisable.
Monte Carlo methods in financial engineering, volume 53. Springer Verlag, 2004. F. Jamshidian. Libor and swap market models and measures. Finance and Stochastics, 1(4):293–330, 1997. 57 C. 1007/978-3-658-04688-0, © Springer Fachmedien Wiesbaden 2014 7. S. Joshi. The concepts and practice of mathematical finance, volume 2. Cambridge University Press, 2008. S. Joshi. More mathematical finance, volume 1. Bluewhale, 2011. G. Levy. An introduction to quasi-random numbers. , http://www. nag. co. uk/IndustryArticles/introduction to quasi random numbers.