Oct
16
Share two code search portal today, one is search Quant code, where people can search code relative to quantitative finance, for instance, Code Search example: Black Scholes matlab; the other one is R-project search engine, specifically for R language programming users. Enjoy.
http://www.finmath.cn/
http://www.rseek.org/
http://www.finmath.cn/
http://www.rseek.org/
Oct
15
Matlab implementation of a method to select the 'best' copula among a subset of copula families.
Based on theory published in : Huard, D., G. Évin, A.-C. Favre (2006), Bayesian Copula Selection, Computational Statistics and Data Analysis, COMSTA3137, vol. 51 (2), 809-822.
http://code.google.com/p/copula/
Based on theory published in : Huard, D., G. Évin, A.-C. Favre (2006), Bayesian Copula Selection, Computational Statistics and Data Analysis, COMSTA3137, vol. 51 (2), 809-822.
http://code.google.com/p/copula/
Oct
14
Most derivative pricing problems have finally come to solve integration numerically, by Simpson, Monte Carlo simulation, etc., however, multi-dimensional integration is time-consuming and prone to error, here I share a Cuba library which offers a choice of four independent routines for multidimensional numerical integration: Vegas, Suave, Divonne, and Cuhre.
[quote]Vegas is the simplest of the four. It uses importance sampling for variance reduction, but is only in some cases competitive in terms of the number of samples needed to reach a prescribed accuracy. Nevertheless, it has a few improvements over the original algorithm and comes in handy for cross-checking the results of other methods.
Suave is a new algorithm which combines the advantages of two popular methods: importance sampling as done by Vegas and subregion sampling in a manner similar to Miser. By dividing into subregions, Suave manages to a certain extent to get around Vegas' difficulty to adapt its weight function to structures not aligned with the coordinate axes.
Divonne is a further development of the CERNLIB routine D151. Divonne works by stratified sampling, where the partitioning of the integration region is aided by methods from numerical optimization. A number of improvements have been added to this algorithm, the most significant being the possibility to supply knowledge about the integrand. Narrow peaks in particular are difficult to find without sampling very many points, especially in high dimensions. Often the exact or approximate location of such peaks is known from analytic considerations, however, and with such hints the desired accuracy can be reached with far fewer points.
[quote]Vegas is the simplest of the four. It uses importance sampling for variance reduction, but is only in some cases competitive in terms of the number of samples needed to reach a prescribed accuracy. Nevertheless, it has a few improvements over the original algorithm and comes in handy for cross-checking the results of other methods.
Suave is a new algorithm which combines the advantages of two popular methods: importance sampling as done by Vegas and subregion sampling in a manner similar to Miser. By dividing into subregions, Suave manages to a certain extent to get around Vegas' difficulty to adapt its weight function to structures not aligned with the coordinate axes.
Divonne is a further development of the CERNLIB routine D151. Divonne works by stratified sampling, where the partitioning of the integration region is aided by methods from numerical optimization. A number of improvements have been added to this algorithm, the most significant being the possibility to supply knowledge about the integrand. Narrow peaks in particular are difficult to find without sampling very many points, especially in high dimensions. Often the exact or approximate location of such peaks is known from analytic considerations, however, and with such hints the desired accuracy can be reached with far fewer points.
Oct
13
Quotation
Library of econometric functions for performance and risk analysis of financial portfolios. This library aims to aid practitioners and researchers in using the latest research in analysis of both normal and non-normal return streams.
We created this library to include functionality that has been appearing in the academic literature on performance analysis and risk over the past several years, but had no functional equivalent in R. In doing so, we also found it valuable to have wrapper functions for functionality easily replicated in R, so that we could access that functionality using a function with defaults and naming consistent with common usage in the finance literature. The following sections cover Performance Analysis, Risk Analysis (with a separate treatment of VaR), Summary Tables of related statistics, Charts and Graphs, a variety of Wrappers and Utility functions, and some thoughts on work yet to be done.
We created this library to include functionality that has been appearing in the academic literature on performance analysis and risk over the past several years, but had no functional equivalent in R. In doing so, we also found it valuable to have wrapper functions for functionality easily replicated in R, so that we could access that functionality using a function with defaults and naming consistent with common usage in the finance literature. The following sections cover Performance Analysis, Risk Analysis (with a separate treatment of VaR), Summary Tables of related statistics, Charts and Graphs, a variety of Wrappers and Utility functions, and some thoughts on work yet to be done.
http://braverock.com/brian/R/PerformanceAnalytics/html/PerformanceAnalytics-package.html
Oct
10
Spreadsheet for the calculation of:
- the diagonal decomposition MDM^-1
- the generating matrix A of the ratings process
- the time-dependent transition matrix P(t)
http://www.schonbucher.de/risk/index.html
spreadsheet http://www.schonbucher.de/risk/rating_case.xls
- the diagonal decomposition MDM^-1
- the generating matrix A of the ratings process
- the time-dependent transition matrix P(t)
http://www.schonbucher.de/risk/index.html
spreadsheet http://www.schonbucher.de/risk/rating_case.xls
