Monte Carlo methods in finance

By P. Jaeckel. 2002

326 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4478936c9d23d4a156b92f416834d70050809c14

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A comparison of biased simulation schemes for stochastic volatility models (2010, background): “Kloeden and Platen ( 1999 ), Jackel ( 2002 ) or Glasserman ( 2003 ).”
A spatial cross‐correlation model of spectral accelerations at multiple periods (2013, background): “It is possible to “fix” this matrix by merely changing its eigenvalues to make it positive definite ( [21]: section 6.”
Minimum variance importance sampling via Population Monte Carlo (2007, methodology): “Note however that a recent reference is the cross-entropy method of Rubinstein and Kroese (2004) where the parameter of a family of proposals is optimised, either directly or by an iterative process, to reach minimal variance or maximal entropy against the target |h(x)|π(x), the function h being of ”
Constructing Sobol Sequences with Better Two-Dimensional Projections (2008, background): “Of course, other ways of obtaining the direction numbers are also possible; see, for example, [4, 8, 11, 16, 18].”
Variance-Gamma and Monte Carlo (2007, methodology): “Other useful variance reduction techniques not discussed here that are effective in financial simulations include common random numbers (called “variate recycling” in [16]), conditional Monte Carlo, stratified sampling, and control variates; see [14] for more details.”
Monte Carlo and Quasi-Monte Carlo Sampling (2009, methodology): “Readers interested in a more extensive treatment of Monte Carlo simulation in finance are referred to [145, 202, 314].”
Construction and Comparison of High-Dimensional Sobol' Generators (2011, methodology): “Many practical studies have proven that the Sobol’ sequences are in many aspects superior to other LDS (see, e.g., Paskov and Traub, 1995; Kreinin et al., 1998; Jaeckel, 2002; L’Ecuyer and Lemieux, 2002), and this is why they are so widely used in finance.”
Valuing path dependent options in the variance-gamma model by Monte Carlo with a gamma bridge (2002, methodology): “A standard reference for applications of Monte Carlo methods in finance is Jäckel (02) [13].”
Fast strong approximation Monte Carlo schemes for stochastic volatility models (2006, background): “It is straightforward to extend the above results to the case when κ ( t ) and α ( t ) are functions of time (Jackel 2002 ).”