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Introduction to QuantLib Development with Luigi Ballabio June 29th - July 1st, London

Sentiment Analysis in Finance 

Numerical Algorithms Group's Blog

Professor Mike Powell F.R.S

April 29, 2015 Comments (0)

We are pained to pass on the sad news of the death on 19th April of Prof Mike Powell F.R.S, a brilliant numerical analyst specialising in numerical optimisation and approximation theory.   Mike touched many lives with his work. NAG and NAG's community benefitted greatly because he gave freely his code to NAG from the beginning of the NAG. The more mature may remember the routine E04DCF, an implementation of his hybrid method for unconstrained optimisation. More recently his...

Introducing the team: Craig Lucas, Senior Technical Consultant

April 15, 2015 Comments (0)

Craig, describe your role at NAG? I am a Senior Technical Consultant based in NAG’s Manchester Office. Here I manage part of NAG’s High Performance Computing group. I come from a numerical linear algebra background. For my MSc dissertation I looked at nearest correlation matrix problems, something I still work on today, and this year I have an MSc student looking at more new algorithms.  Another big interest of mine is shared memory parallelism with OpenMP, and in particular helping users...

Introducing the team: Mick Pont, NAG Principal Technical Consultant

April 9, 2015 Comments (0)

Over the next few months, scattered in between our technical blog posts, we are going to publish interviews with NAG colleagues. Our first interview is with Mick Pont. Mick, what is your role at NAG?  I'm a Principal Technical Consultant, and Deputy Manager of the Development Division. I'm involved in the development and peer review of new NAG Library software and documentation, and in the scheduling of software production in line with company targets. In "project management"...

Optimization, NAG and Me - 5 Years and Counting

April 9, 2015 Comments (0)

Authored by Jan Fiala, NAG Numerical Software Developer It feels almost like yesterday since I joined NAG so it is hard to believe that it has been 5 years already. Looking back, I see a long path I would like to tell you a bit about, just in case you were curious about one of the people answering your support queries. I was always a blend of a computer scientist and a mathematician. Computers and programming were my hobby but it did not feel quite right to choose either as the main subject...

Advanced Analytics on Apache Spark

April 9, 2015 Comments (0)

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. The NAG Library covers areas such...

Adding a Slider Widget to Implied Volatility

April 9, 2015 Comments (0)

In the last post on Implied Volatility, we downloaded real options data from the CBOE and calculated the volatility curves/surface. We saw the calculations of 30,000 implied volatilities in roughly 10 seconds.  In this post we concentrate on the speed of calculating implied volatility via a variety of different methods. We look at the volatility curve/surface using Python's Scipy, the NAG Library for Python, and the NAG C Library. In addition, we've added a slider widget to...

Problem needed for research on Bermudan Option Pricing Algorithms

October 2, 2014 Comments (0)

Introduction NAG together with Prof. Oosterlee and an MSc student from TU Delft are investigating the recent Stochastic Grid Bundling Method (SGBM) [1,2]. The objective is to compare the performance of SGBM to the well-known Longstaff-Schwartz (least squares method or LSM) in a non-academic setting, i.e. on the pricing of a Bermudan option, with underlying asset(s) driven by a realistic process such as Heston or LMM. We are looking for an interesting case to test these two methods. This...

Gaussian Mixture Model

August 21, 2014 Comments (0)

With the release of Mark 24 of the NAG C Library comes a plethora of new functionality including matrix functions, pricing Heston options w/term structure, best subset selection, and element-wise weightings for the nearest correlation matrix. Among the new routines I was excited to test out was the Gaussian mixture model (g03ga).This routines will take a set of data points and fit a mixture of Gaussians for a given (co)variance structure by maximizing the log-likelihood function. The...

Secrets of HPC Procurement

June 17, 2014 Comments (0)

Liked my article today in HPC Wire "Secrets of the Supercomputers"? I firmly poke fun at various elements of an imaginary supercomputer procurement process. However, I'm sure many readers will also see familiar and painfully serious aspects in the fictional story. As I mention at the bottom of that article, NAG can help make the process of buying HPC systems much better than the worrying case in the article. For example, the tutorial I ran at SC13 with Terry Hewitt titled "Effective...

Testing Matrix Function Algorithms Using Identities

April 10, 2014 Comments (0)

Edvin Deadman and Nick Higham (University of Manchester) write: In a previous blog post we explained how testing new algorithms is difficult. We discussed the forward error (how far from the actual solution are we?) and the backward error (what problem have we actually solved?) and how we'd like the backward error to be close to the unit roundoff, u. For matrix functions, we also mentioned the idea of using identities such as sin2A + cos2A = I to test algorithms. In...