Peter Verhoog of Verhoog Consultancy and Techila Technologies have published a paper that demonstrates, how to speed up the calculation of Value-at-Risk measures using scalable distributed computing. The MATLAB and R codes have also been made open and available for the financial community.
Prof. Kanniainen’s paper demonstrates how financial models can be re-calibrated in real time after a market shock. He shows how to cut the time from more than 7 hours to just a couple of minutes. The MATLAB code open and available for the financial community.
Watch a 1-minute intro on YouTube QuantBros.com has published a tutorial where Dakota Wixom from QuantBros shows how to generate random portfolios in R programming language on a large scale. Watch the full QuantBros.com tutorial on YouTube. In the tutorial, QuantBros uses S&P 500 sector ETFs, calculates an efficient frontier or random portfolios with […]
Read the article in LinkedIn Pulse An accurate Simulink model performs as expected and produces the desired results. But the added complexity along with the constantly growing data requirements can cause unacceptable simulation times. How to improve simulation performance, without breaking the bank? Teppo Tammisto‘s article in LinkedIn Pulse looks at how to speed […]
Watch the CSC and Techila tutorial on YouTube. Are you a R user? Would you like to have faster simulation and analysis? This free CSC IT Center for Science webinar was recorded on 17 May 2016. The webinar is designed for users of R programming language, who want faster simulation and analysis, without the […]
Read the article in LinkedIn Pulse Can You Distill High Frequency Financial Data Efficiently Using MATLAB? Tuomas Eerola’s article in LinkedIn Pulse introduces a solution for turning high frequency financial data efficiently into a competitive advantage using MATLAB. Computing volatility measures requires high quality Trade and Quote (TAQ) data. This is big data. To […]
Watch Techila Command Line Interface Tutorial on YouTube. Techila Technologies has published a new video tutorial, which presents the easiest way to integrate distributed computing speed to an existing business application: The Techila Command Line Interface. Techila Command Line Interface can be used to data parallel an application to which we have don’t have […]
Download the Discussion Paper from Bank of Finland The Bank of Finland publishes a paper “Kiss me deadly: From Finnish great depression to great recession”, which discusses the causes of the Finnish Great Depression, 1990-1993. The simulations benefited of self-managing distributed computing, which was enabled by Techila Distributed Computing Engine. In the project, the […]
Read the report ANSYS In Cloud – Scaling Up Computational Capacity Techila Technologies and Cargotec‘s MacGregor have investigated how to optimize the cost of ANSYS HPC system ownership with cloud services integrated to the enterprise IT. This report looks at computing clouds as a platform for engineering simulation, and how to manage the software […]
Watch Techila with MATLAB Tutorials on YouTube Marko Koskinen from Techila Technologies’ Support Team delivers a series of three short tutorial videos on how to use Techila Distributed Computing Engine to speed up MATLAB applications without MPI or CUDA programming skills. In the first part of the series Marko covers how to access the […]