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Numerical Algorithms Group's Blog

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...

The Wilkinson Prize for Numerical Software

March 21, 2014 Comments (0)

In honour of the outstanding contributions of James Hardy Wilkinson to the field of numerical software, Argonne National Laboratory, the National Physical Laboratory, and the Numerical Algorithms Group award the Wilkinson Prize for Numerical Software (US $3000). The 2015 prize will be awarded at the International Conference in Industrial and Applied Mathematics (ICIAM) in Beijing, China, August 2015. Entries must be received by July 1, 2014. Additional details on the Wilkinson Prize for...

C++ wrappers for the NAG C Library

February 6, 2014 Comments (0)

Motivation Occasionally, we recieve requests to make the NAG C Library easier to call from C++. In the past, we found it difficult to build something that would work across all of the code our C++ users write. With the advent of the C++11 standard, many of the key features of the widely used Boost library have been incorporated into the STL, and finally provide a standardized way to address many of the difficulties we've encountered (the code we describe here works with Visual Studios 2010 and...

Out and about this week – The London Thalesians Seminar

January 16, 2014 Comments (0)

NAG’s Brian Spector gave a great talk to a packed audience of finance professionals in London this week. The Thalesians describe themselves as a “think tank of dedicated professionals with an interest in quantitative finance, economics, mathematics, physics and computer science”. Brian was delighted to present "Implied Volatility using Pythons Pandas Library" at their recent London Seminar on Wednesday 15 January 2014.  Brian Spector presenting "Implied Volatility using Python's...

Using the NAG Compiler with the NAG Fortran Library (Mark 24) on Windows

November 28, 2013 Comments (0)

Blog written by David Sayers, NAG Principal Technical Consultant  Introduction The NAG Fortran Compiler is an excellent compiler for checking and running your Fortran code. We use it extensively here at NAG to ensure that our code for the library complies with the current Fortran standards. Personally whenever I have a user problem report that I can’t resolve by inspection my first instinct is to request run the users code with the Compiler. Frequently this identifies the error...

NAG at SC13

November 26, 2013 Comments (0)

A short summary of news from or about NAG at SC13. Quick fact: NAG is one of very few organizations that have been at every SC since the series started. Attacking the HPC skills need: NAG Supercomputing Support Service passes milestone of 2000 course attendees NAG announces the latest milestone in addressing the skills needs of the scientific computing community with the skills they require to effectively exploit HPC systems - over 2000 attendees have now benefitted from NAG's highly rated...

Implied Volatility using Python's Pandas Library

October 15, 2013 Comments (0)

Python has some nice packages such as numpy, scipy, and matplotlib for numerical computing and data visualization. Another package that deserves a mention that we have seen increasingly is Python's pandas library. Pandas has fast and efficient data analysis tools to store and process large amounts of data. Additionally, pandas has numpy and ctypes built into it which allow easy integration with NAG's nag4py package. Below is an example using nag4py and the pandas library to calculate the...