Today is the first day of G20 summit, is also the strongest protest in the last fews days, which will last at least till tomorrow. Company suggests us to have dress down to avoid unnecessary conflict with protestors, (for those of you who have no idea what's happened or how seriou this protest is in London recently, what I can tell you is the slogan of a protestor group being "Burn a banker"), I do enjoy dress down, wearing jeans, t-shirt and looking relaxed (although not).

Protest itself is fair enough and welcomed, everyone has the right to speak out his or her own views, this is a kind of freedom we should cherish, but freedom does not mean you can do anything you want. I fully understand the feeling of losing job (I myself will be one of them soon), however, this should never be an excuse of blaming other people, nothing hurts us, as foreigners, worse than hearing "fucking foreigners" in the broad street. Fortunately, those people are only a few, shit happens everywhere.

Oxford MFE package was shared there, here is a new MFE Matlab toolbox accompanying the book "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach", which is grouped into the following seven categories:

1. Time series,
2. Distributions,
3. Tests and goodness-of-fit functions,
4. Markov regime switching (MRS) models,
5. GUI functions,
6. Demos,
7. Data files.

Selected functions are:

armaacvf - Autocovariance function of an ARMA process.
average - Weighted average.
decompB - Moving average with rolling volatility daily data decomposition.
rollingvol - Annual rolling volatility.
empcdf - Empirical cumulative distribution function (cdf).
hypest - Estimate parameters of the hyperbolic distribution.
nigcdf - NIG cumulative distribution function (cdf).
nigest - Estimate parameters of the NIG distribution.
nigloglik - NIG log-likelihood function.
nigpdf - NIG probability density function (pdf).
stabcdf - (Alpha-)stable cumulative distribution function (cdf).
stabcull - Quantile parameter estimates of a stable distribution.
stabreg - Regression parameter estimates of a stable distribution.
edftests - Empirical distribution function (edf) goodness-of-fit statistics (Kolmogorov and Anderson-Darling).

Downloading the toolbox at: http://www.im.pwr.wroc.pl/~rweron/MFE.html
More about the book at: http://www.riskey.cn/2009/04/modeling-and-forecasting-electricity-loads-and-prices-a-statistical-approach-the-wiley-finance-series-hardcover/

Financial data is the center of quantitative finance research, undoubtedly. Here is a list of free financial data for downloading, for more please check pages http://www.quantnet.org/forum/showthread.php?t=2159 and http://www.wilmott.com/messageview.cfm?catid=19&threadid=14748, enjoy.

For instance:
1. ADVFN offer FREE streaming stocks and shares data form around the world. SEE MORE
2. Historical FX Rates: http://oanda.com/convert/fxhistory
3. Historical Stock Prices: http://finance.yahoo.com/q/hp?s=yhoo
4. Recent LIBOR rates: BBA - British Bankers' Association - BBA LIBOR
5. Some Implied Volatilities: http://www.ivolatility.com
6. Delayed Commodities: http://www.liffe-commodities.com.
7. US Fundamentals: http://www.sec.gov

The FastICA package is a free (GPL) MATLAB program that implements the fast independent component analysis.  

Independent component analysis (ICA) or blind source separation is a modern signal processing technique to multivariate financial time series such as a portfolio of stocks to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). This can be viewed as a factorization of the portfolio since joint probabilities become simple products in the coordinate system of the ICs.

The major difference between Independent component analysis and more familiar principal component analysis (PCA) is in the type of components obtained. The goal of PCA is to obtain principal components which are uncorrelated. Moreover, PCA gives projections of the data in the direction of the maximum variance. The principal components (PCs) are ordered in terms of their variances: the first PC defines the direction that captures the maximum variance possible, the second PC defines (in the remaining orthogonal subspace) the direction of maximum variance, and so forth. In ICA however, the aim is to obtain statistically independent components. What's more, PCA algorithms use only second order statistical information (variance dominates). On the other hand, ICA algorithms may use higher order2 statistical information for separating the signals.

To download FastICA and more about the book Independent Component Analysis check http://www.cis.hut.fi/projects/ica/fastica/, the book can be bought at Amazon through: Independent Component Analysis

I bought a Gphone several months ago, its main attraction to me is Gmail everywhere as long as there is signal since I can't use Gmail box with my PC at company (my boss won't read my blog). Another shining point of Gphone is its Android platform and Market, where people can publish applications on entertainment, finance, news, weather, etc.

Yesterday I downloaded a free application named Equity option calculator, a simple equity options pricer for European style no dividend calls and puts using your own inputs under Black Scholes model framework. Alternatively enter a ticker and let the market data calibrator fill in pricing parameters. solve for the option value or implied volatility. The pricer also calculates option sensitivities (Greeks).

Although the supported options are limited, it is fun to play a derivative calculator wherever as you go, isn't it?  the code is written in Java that I am not familiar with, but have downloaded The Android SDK for developers to see if I am able to build an application covering more options like Matlab-GUI equity derivative calculator does.

if you happen to own a Gphone, this option pricer can be found by typing "equity option calculator" in Market. Have fun.

Publisher's blog: http://jwdevg1.blogspot.com/