NAGnews 129 | 5 March 2015

Posted on
5 Mar 2015

In this issue


NAG Toolbox for MATLAB®, Mark 24 now available for Apple Mac OSX


We are delighted to announce that the NAG Toolbox for MATLAB®, currently at Mark 24 is now available for users of the Apple Mac OS. The NAG Toolbox for MATLAB® is the largest and most comprehensive single numerical toolkit that both complements and enhances MATLAB®. The NAG Toolbox for MATLAB® contains over 1,500 functions that provide solutions to a vast range of mathematical and statistical problems. The functionality contained within this Toolbox gives a 'one-stop' solution to your numerical computational needs.

Creating a bivariate Gaussian Copula using the NAG Toolbox

Many existing NAG Library users are entitled to use the NAG Toolbox for MATLAB® because their existing software licence covers this product. If you think this could be you and would like to use the product, just email us at operations@nag.co.uk and we'll check for you.

For more general information on the NAG Toolbox for MATLAB® see our website and for specific platform availability click on the 'Product Availability' tab.


Portfolio Optimization using the NAG Library


In the first NAG White Paper of 2015 John Morrissey and Brian Spector give an introduction into the notion and techniques used in portfolio optimization.

Abstract

NAG Libraries have many powerful and reliable optimizers which can be used to solve large portfolio optimization and selection problems in the financial industry. Below is an introduction into the notation and techniques used in portfolio optimization. We discuss some sample problems and present help in choosing an appropriate NAG optimizer. Finally, there is a section on handling transaction cost for the portfolio optimization.

Read the paper here.


Win a Pass to Global Derivatives - 2015 Student "Direct Award" Prize


NAG 'Direct Award' prize for best financial maths project using NAG

If you're a student (BSc, MSc, PhD etc) studying mathematical finance or similar you can enter the NAG 'Direct Award' prize by sending through a summary of your research. Your work should include code that uses NAG software. Submit your work or contact us for more information by email. The prize includes a monetary contribution to travel and accommodation and a pass to the prestigious show, Global Derivatives Trading & Risk Management, 18-22 May 2015, Amsterdam. Please submit your work to rachel.foot@nag.co.uk.

Whilst we are not prescriptive of the exact format of the submission the entrant should bear in mind that their article should be suitable for publication on the NAG website and in NAGnews. Students may submit supporting material like their PhD or MSc thesis or published paper to help the competition judges. We recommend any submission includes the following:

  • A statement of the problem with some background putting it into context
  • Description of the computational aspects
  • How NAG software helped with these computational aspects
  • Results of the work
  • Possible further work
  • Copies of code
  • References

Previous Winners

The winner of the NAG Student Prize 'Direct Award' of 2014 was Christoph Auth of Warwick Business School, for his paper "Continuous Wavelet Transform and Wavelet Coherence - Implementation and Application to the Diversification Analysis of Hedge Fund Returns".

Our 2013 winner was Isabel Ehrlich for her thesis "Pricing Basket Options with Smile".


Advanced Analytics for Apache Spark


What follows is a snippet from a recent NAG Blog post by Brian Spector.

Developed in AMPLab at UC Berkeley, Apache Spark has become an increasingly popular platform to perform large scale analysis on Big Data. With run-times up to 100x faster than MapReduce, Spark is well suited for machine learning applications.

Spark is written in Scala but has APIs for Java and Python. As the NAG Library is accessible from both Java and Python, this allows Spark users access to over 1600 high quality mathematical routines.

Calling the NAG Library on Spark

The fundamental datatype used in Spark is the Resilient Distributed Dataset (RDD). A RDD acts as a pointer to your distributed data on the file system. This object has intuitive methods (count, sample, filter, map/reduce, etc) and lazy evaluation that allow for fast and easy manipulation of distributed data.

To read the full Blog post visit http://blog.nag.com/2015/02/advanced-analytics-on-apache-spark.html.


NAG's YouTube Channel


There are many informative videos on NAG's YouTube Channel, including technical 'How to' demonstrations and recordings of training courses. Recently uploaded are a series of training course recordings from Chicago where NAG experts presented to an audience of academics and professionals from the finance industry. The course subject was "Quant Finance: Using the NAG Library for Python and Using the NAG Library for C".


Training Courses & Events


NAG will be at the following exhibitions and conferences over the next few months.

NAG are once again sponsoring the following six-day course later this year:


We're Hiring! Developer in Mathematical Optimization


We are looking for a developer in Mathematical Optimization to join NAG's Optimization development team in either Oxford or Manchester, UK. For more information on this exciting role visit the 'Careers at NAG (link removed)' area of the NAG website.

Jan Fiala, NAG Numerical Software Developer has written a blog post about his time working at NAG in the Optimization Team.


NAGnews - Past Issues


We provide an online archive of past issues of NAGnews. For editions prior to 2010, please contact us.