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An example based approach to all aspects of quantitative finance including sample code and worked examples.

Tutorial on Quasi-Monte Carlo Simulation

Nice introduction and article on derivatives

Nice overview on interest rate options - Cap/floors, swaps, bermudan option, cms

Nice tutorial on how to do matrix operations in Excel - by William Sharpe!

by Peter Carr

Dmitrii Silvestrov
Henrik J

Several papers published by Derman

Michael Johannes and Nicholas Polson
This chapter develops Markov Chain Monte Carlo (MCMC) methods for Bayesian inference in continuous-time asset pricing models. The Bayesian solution to the inference problem is the distribution of parameters and latent variables conditional on observed data, and MCMC methods provide a tool for exploring these high-dimensional, complex distributions. We first provide a description of the foundations and mechanics of MCMC algorithms. This includes a discussion of the Clifford-Hammersley theorem, the Gibbs sampler, the Metropolis-Hastings algorithm, and theoretical convergence properties of MCMC algorithms. We next provide a tutorial on building CMC algorithms for a range of continuous-time asset pricing models. We include detailed examples for equity price models, option pricing models, term structure models, and regime-switching models. Finally, we discuss the issue of sequential Bayesian