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Nov 9
Do not know whether you visited it before or not, I came across this site "Run My Code" today when I searched a paper. It is interesting and useful so I have no hesitation to share with you immediately.

Basically RunMyCode is a novel cloud-based platform that enables scientists to openly share the code and data that underlie their research publications. It has many files accompanying those published papers so you can easily replicate the results, which dramatically decreases your research efforts. You can choose to download the coding files directly, or upload your data and run it via the site's cloud platform. (I tried twice but failed for unknown reasons, so I recommend you to download the file and run on your own computer.)

The site is a newly established and is expanding, at the moment it includes 64 files under the following categories
run my code

A sample search in Finance returns you the codes.

It is free to use, quite nice, isn't it?
Jul 23
I have tested several R optimization functions before: nlm, optim(Nelder-Mead), optim(BFGS), optim(SANN), nlminb, optim (L-BFGS-B) for a eight-parameter Vasicek interest rate model, overall I find that for my setting, nlminb is the best and all R functions finish within seconds. For detail please read the old post at R optimization function test

Pat at Portfolio Probe recently had a wonderful test on some heuristic optimization methods, including simulated annealing, traditional genetic algorithm, evolutionary algorithms. By using R packages and functions - Rmalschains, GenSA, genopt, DEoptim, soma, rgenoud, GA, NMOF and SANN method of optim, he finds that the Rmalschains and GenSA packages are standing out. Nice one, original article is at A comparison of some heuristic optimization methods.
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Jun 27
This memo explains how to use the MATLAB code for estimating a Markov Regime Switching Model with time varying transition probabilities. The code is developed by Zhuanxin Ding based on the original code by Marcelo Perlin for estimating a Markov
Regime Switching Model
with constant transition probability matrix.

Click here for an introduction paper and Matlab codes are here.
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Jun 10
AirXCell is an online R application framework currently supporting a programmable spreadsheet, an R development environment and various financial calculation forms.

A new calculation form has been implemented recently within AirXCell for financial option pricing (option valuation). The option pricer within AirXCell enables the user to compute theoretical option prices. It already offers an extended set of basic and exotic models (about a dozen) than enables the user to price a wide range of option types:

American options,
European options,
Asian options,
Barrier options,
Binary options,
Currency translated options,
Lookback options,
Multiple assets options and
Multiple exercises options

Many more models are being implemented currently and will be added soon to AirXCell. In addition to the option pricing form, there are other forms especially useful in the same context that provides ways to load asset prices, visualize them, compute the theoretical and historical volatility.

This form is very valuable to quantitative researchers or any finance professional who needs to compute theoretical option prices easily and who is looking for a reliable option pricer.

The Option pricing form presents the user with an HTML form enabling her to set up the model with the required parameters values such as the underlying asset price, the strike price, the volatility of the underlying asset, etc.

For instance, the following form is presented to a user requesting the price of an european option using the Generalized Black Scholes model:

Again, there are many more models and option types coming soon as well as other forms for various other kind of calculations, still mostly oriented towards financial calculation.
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Apr 16
The last decade we have seen a significant increase in the demand for high frequency data. This is explained for a large part by an increased attention of the academic world in algoritmic trading. Moreover, as lot of papers suggest, the profitability has been shifted to an intra-daily format. In this segment, speed is what counts. For instance, Scholtus and Van Dijk (2012) state that strategies that yield a positive return when they experience no delay, a delay of 200 milliseconds is enough to lower their performance significanlty. Given the competition on the market from large institutions, such as JP Morgan and Morgan Stanley, a private investor has always a competative disadvantage due to its lack of the required technology. Nevertheless, there is always room for improvement in the modelling of stochastic intra-daily processes such as the VWAP and daily volatility.

A key ingredient in these research areas is proper and clean (historical and up-to-date) intra-daily data. On the web there are various resources available, but most of them require a relatively high fee. Other solutions require the use of a specific software. However, there are ways to retrieve intra-daily data for free using Google Finance and also without any software.

Using Matlab

If you are familiar with MatLab you can use parts of the package 'Volume Weighted Average Price from Intra-Daily Data' by Semin Ibisevic referenced at Qoppa Investment Society. This package allows you to
(1) retrieve intra-daily stock price data from Google Finance; (2) calculate the VWAP at the end of each trading day; and (3) transform intra-daily data to a daily format. It is a relatively flexible function as it only requires the user to input the ticker symbol and the exchange where the security is listed on. Additionally, the user can define the frequency of the data (1 second or higher) and the period (for instance past 10 days).

Without software

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