Risk and Asset Allocation
A toolbox for risk and asset allocation from Attilio Meucci that allows for advanced risk and portfolio management.
These routines support the book “Risk and Asset Allocation” Springer Finance, by A. Meucci, see http://www.symmys.com
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…
In addition to these MATLAB routines, at www.symmys.com the reader can find other freely downloadable complementary materials:
- - the “Technical Appendices”, a booklet with the proofs of the results presented in the books and used in the routines
- - the “Slides”, a set of presentations that walk the reader through the whole book
- - the “Errata”, a few typos in the first two reprints of the book
- - the “Sample”, an excerpt of the book.
Any feedback on the above materials is highly appreciated: please refer to www.symmys.com to contact the author.
Popularity: 2% [?]
CompEcon Toolbox for Matlab
CompEcon is a set of MATLAB functions for solving a variety of problems in economics and finance. The library functions include rootfinding and optimization solvers, a integrated set of routines for function approximation using polynomial, splines and other functional families, a set of numerical integration routines for general functions and for common probability distributions, general solvers for Ordinary Differential Equations (both initial and boundary value problems), routines for solving discrete and continuous time dynamic programming problems, and a general solver for financial derivatives (bonds, futures, options).
The CompEcon Toolbox was developed to accompany:
Applied Computational Economics and Finance, Mario J. Miranda & Paul L. Fackler, MIT Press
MATLAB code for all of the examples in the text is supplied with the CompEcon Toolbox.
The CompEcon Toolbox runs only any MATLAB version 5 or higher.
Popularity: 2% [?]
Linear Predictive Coding for Stock Market Forecasting
Linear predictive coding (LPC) is a tool used mostly in audio signal processing and speech processing for representing the spectral envelope of a digital signal of speech in compressed form, using the information of a linear predictive model. It is one of the most powerful speech analysis techniques, and one of the most useful methods for encoding good quality speech at a low bit rate and provides extremely accurate estimates of speech parameters. LPC is generally used for speech analysis and resynthesis. It is used as a form of voice compression by phone companies, for example in the GSM standard. It is also used for secure wireless, where voice must be digitized, encrypted and sent over a narrow voice channel, an early example of this is the US government’s Navajo I.
We have developed an advanced approach based on LPC for stock market forecasting. Code, given input data, is able to say if next closing price will be higher or lower than last closing price. A buy signal is given if next closing price will be higher than current price. A sell signal is given if next closing price will be equal or lower than current price. This algorithm has been tested on Italian securities (more than 320 securities). Data have been imported from Yahoo. Half of data have been used for training and the remaining ones for testing, with no overlapping between training and testing data. We have obtained an excellent recognition rate of 56.02%.
Index Terms: Matlab, source, code, lpc, linear, predictive, coding, stock, market, forecasting, forecast, security.
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Figure 1. Forecasting financial data |
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A simple and effective source code for Advanced LPC Trading System. |
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Demo code (protected P-files) available for performance evaluation. Matlab Signal Processing Toolbox is required.
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Release
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Date
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Major features
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1.0
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2009.07.08 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Advanced LPC Trading System. Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 1700 EUROS (less than 2380 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Advanced LPC Trading System. Alternatively, you can bestow using our banking coordinates:
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The authors are not commodity trading advisors. The information on this site is for trading education only. There are no trading recommendations for any one individual made on this site and this information is paper trades for trading education. All trades are extremely risky and only risk capital should be used when trading. The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Signal Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 2% [?]
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