Oct
23
The MySQL database server is very popular for its openness, robustness, and speed. Matlab is a wonderful commercial product for scientific and technical computing. Using them together is a great tool for quantitative data analysis. You can do this using the Matlab Database Toolbox, but it is more efficient to connect directly using the APIs for both products. This code implements that connection, with a fairly rich framework for handling data conversion, especially dates and times.
http://cims.nyu.edu/~almgren/mysql/
http://cims.nyu.edu/~almgren/mysql/
Oct
22
Open Source Software for Financial Engineering and Computational Finance
Rmetrics is the premier open source solution for teaching financial market analysis and valuation of financial instruments. With hundreds of functions build on modern methods Rmetrics combines explorative data analysis, statistical modeling and rapid model prototyping. The Rmetrics Packages are embedded in R building an environment which creates for students a first class system for applications in statistics and finance.
Download at http://cran.cnr.berkeley.edu/web/packages/fOptions/index.html
Rmetrics is the premier open source solution for teaching financial market analysis and valuation of financial instruments. With hundreds of functions build on modern methods Rmetrics combines explorative data analysis, statistical modeling and rapid model prototyping. The Rmetrics Packages are embedded in R building an environment which creates for students a first class system for applications in statistics and finance.
Download at http://cran.cnr.berkeley.edu/web/packages/fOptions/index.html
Oct
21
In linear algebra, the singular value decomposition (SVD) is an important factorization of a rectangular real or complex matrix, with several applications in signal processing and statistics. Applications which employ the SVD include computing the pseudoinverse, least squares fitting of data, matrix approximation, and determining the rank, range and null space of a matrix.
Singular Value Decomposition to solve ill conditioned square matrices.
Singular Value Decomposition to solve ill conditioned square matrices.
Oct
20
Quotation
The Heston Model is one of the most widely used stochastic volatility (SV) models today. Its attractiveness lies in the powerful duality of its tractability and robustness relative to other SV models.
This project initially begun as one that addressed the calibration problem of this model. Attempting to solve such a problem was an impossible task due to the lack of exposure to such ‘advanced’ models.
I, therefore, decided to take a slight digression into the world of Heston and stochastic volatility. Enroute I realised that fundamental information that one would require to gain an intuitive understanding of such a model was very disjoint and hence incomplete. This project, therefore, evolved into something that could fill this gap.
A practical approach has been adopted since the focus of calibration is quite practical itself. All the relevant tools are provided to facilitate this calibration process, including MATLAB code. This code has been confined to the appendix to keep the main body clutter free and ‘quick-to-read’.
paper and code can be downloaded at http://math.nyu.edu/~atm262/fall06/compmethods/a1/nimalinmoodley.pdf
Oct
17
These routines support the book "Risk and Asset Allocation" Springer Finance, by A. Meucci.
The routines include many new features:
- more uni-, multi- and matrix-variate distributions
- more copulas
- more graphical representations
- more analyses in terms of the location-dispersion ellipsoid.
- best replication / best factor selection
- FFT-based projection of a distribution to the investment horizon
- caveats about delta/gamma pricing
- step-by-step evaluation of a generic estimator
- non-parametric estimators
- multivariate elliptical maximum-likelihood estimators
- shrinkage estimators: Stein and Ledoit-Wolf, Bayesian classical equivalent
- robust estimators: Hubert M, high-breakdown minimum volume ellipsoid
- missing-data techniques: EM algorithm, uneven-series conditional estimation
- stochastic dominance
- extreme value theory for VaR
- Cornish-Fisher approximation for VaR
- kernel-based contribution to VaR and expected shortfall from different risk-factors
- mean-variance analysis and pitfalls (different horizons, compounded vs. linear returns, etc...)
- Bayesian estimation (multivariate analytical, Monte Carlo Markov Chains, priors for correlation matrices)
- estimation risk evaluation: opportunity cost of estimation-based allocations
- Black Litterman allocation
- robust optimization (calls SeDuMi to perform cone programming)
- robust Bayesian allocation
- more...
sample chapter and codes can be downloaded at http://www.symmys.com/AttilioMeucci/Book/Downloads/Downloads
The routines include many new features:
- more uni-, multi- and matrix-variate distributions
- more copulas
- more graphical representations
- more analyses in terms of the location-dispersion ellipsoid.
- best replication / best factor selection
- FFT-based projection of a distribution to the investment horizon
- caveats about delta/gamma pricing
- step-by-step evaluation of a generic estimator
- non-parametric estimators
- multivariate elliptical maximum-likelihood estimators
- shrinkage estimators: Stein and Ledoit-Wolf, Bayesian classical equivalent
- robust estimators: Hubert M, high-breakdown minimum volume ellipsoid
- missing-data techniques: EM algorithm, uneven-series conditional estimation
- stochastic dominance
- extreme value theory for VaR
- Cornish-Fisher approximation for VaR
- kernel-based contribution to VaR and expected shortfall from different risk-factors
- mean-variance analysis and pitfalls (different horizons, compounded vs. linear returns, etc...)
- Bayesian estimation (multivariate analytical, Monte Carlo Markov Chains, priors for correlation matrices)
- estimation risk evaluation: opportunity cost of estimation-based allocations
- Black Litterman allocation
- robust optimization (calls SeDuMi to perform cone programming)
- robust Bayesian allocation
- more...
sample chapter and codes can be downloaded at http://www.symmys.com/AttilioMeucci/Book/Downloads/Downloads
