NAG's Past Finance Events

Links to presentations, papers, and videos

NAG's involvement with the finance industry grows rapidly with NAG's library of algorithms for finance embedded within hundreds of finance applications around the world.

NAG's prominence as a supplier to the finance industry has evolved to such an extent that it regularly hosts events for finance professionals. The calibre of presenters attracted to NAG's events is testimony to our ongoing commitment to academia and industry. Links to previous presentations given at NAG finance events feature below.

If you would like to register your interest in attending the next NAG Finance event simply email us. Alternatively sign up to NAGnews, where we regularly feature event details.


14th Quantitative Finance Conference, Nice - 26-28 September 2018

Slides: CVA at Scale: Combining the NAG Library with dco/c++ and Origami by Justin Ware, NAG 


Kx Meet (Summer Kick-Off), London - 30 May 2018

Slides: Using the Time Series Database Kdb+ with Q and the NAG Library by John Holden, NAG 


Fixed Income Conference, Florence - 19 October 2017

Slides: Second Order Sensitivities: AAD Construction and use for CPU and GPU by Jacques du Toit, NAG and Chris Kenyon


Frankfurt Thalesians/QFGG - 6 March 2017

Slides: Innovations in Quant Finance from NAG - Adjoint Algorithmic Differentiation, HPC and PDEs by Jacques du Toit, NAG


City Lectures: 'Financial Time Series' and 'Mathematical Optimization', 8 February 2017, London and via webcast

Video: City Lectures 'Financial Time Series' and 'Mathematical Optimization'

Slides: Latest Innovations in Financial Time Series - Changepoints, structural breaks, segmentations and other stories by Dr Rebecca Killick, University of Lancaster

Slides: What's New in Mathematical Optimization from NAG by Jan Fiala and Benjamin Marteau, NAG


Global Derivatives Trading & Risk Management, 9-13 May 2016. Budapest

Handwritten Adjoints by Operator Overloading presented by Jacques du Toit of NAG. Co-authors Johannes Lotz, Klaus Leppkes (RWTH Aachen)


QuanTech Conference, 21-22 April 2016. London

Compile-Time Adjoints via Operator Overloading in C++: Applications to CPU and GPU presented by Jacques du Toit of NAG

Adjoint Code Design Patterns presented by Uwe Naumann of RWTH Aachen University


International Conference on Computational Finance, 14-18 December 2015

Dr Jacques Du Toit headlined at International Conference on Computational Finance (ICCF2015) – he talked about delivering Adjoints on GPU: C++11 takes the “hand” out of handwritten adjoints.


London CQF Institute, Evening Seminar, 12 August 2015

Using Numerical Libraries on Spark
To view the slides please click here
To view the recording please click here
Quant Finance in C++ with the NAG C Library & Quant Finance in Python with the NAG Library for Python
To view the recordings please click the following links:
Part 1
Part 2
Part 3
Part 4


Quant Finance in C++ and in Python with the NAG Library. September 2014, New York. Hosted by CQF Institute

Quant Finance in C++ with the NAG C Library: How to use the NAG C Library from within Visual Studio; Basic best practice for invoking the NAG C Library; Options for call-backs, why and how; Building custom solvers with the NAG Library; Explore user cases through examples.

Quant Finance in Python with the NAG Library: Installing and using Python wrappers; C - Python type conversions; Data analysis using NumPy/Pandas; Passing user data to NAG callbacks; Explore user cases through examples.

To view recordings of the training courses please click on the links below:

Part 1 - Introduction to NAG by Bob Gregg and Brian Spector plus background on NAG and the beginning of ‘Using the NAG C Library in Quant Finance’

Part 2 - Using the NAG C Library: Documentation, starting a project, and a Black Scholes example

Part 3 - Using the NAG C Library: Implied Volatility example

Part 4 - Using the NAG C Library: Continued Implied Volatility and a Monte Carlo Option example

Part 5 - Using the NAG C Library: Generating Random Numbers and a Basket Option example

Part 6 - Using the NAG Library for Python: An American Option Pricing example

Part 7 - Using the NAG Library for Python: Continued Option Pricing example using Matplotlib and Implied Volatility example

Part 8 - Using the NAG Library for Python: Continued Implied Volatility using real data and Python’s Pandas Library


NAG participated in Computation in Finance and Insurance, post-Napier conference. April 2014. Hosted by The Royal Society of Edinburgh (RSE).

The speakers John Holden and Jacques du Toit reflected on why major financial institutions use numerical libraries and the importance of writing robust code. The main part of the talk was focused on Algorithmic Differentiation (AD) which is a disruptive technology that is extremely powerful and they explained how to “do AD” with a realistic amount of effort on a large code base. The AD technology pairs well with traditional architectures as well as accelerators and this was explained with mini-case study featuring NVIDIA hardware.


NY Quantitative Python User Group. March 2014, New York

Brian Spector of NAG presented "Implied Volatility using Python's Pandas Library." Brian discussed a technique and script for calculating implied volatility for option prices in the Black-Sholes formula using Pandas and nag4py. He demonstrated fitting varying degrees of polynomials to the volatility curves, examined the volatility surface and its sensitivity with respect to the interest rate.

New Thinking in Finance.  February 2014. Lloyds & Willis Head Offices in the City of London.

Jacques Du Toit of NAG presented Algorithmic Differentiation of a GPU Accelerated Application


Implied Volatility using Python's Pandas Library London Thalesians Seminar. 15 January 2014

SPEAKER: Brian Spector is a Technical Consultant at Numerical Algorithms Group in Lisle, Illinois, USA.
ABSTRACT: 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. We present an example using NAG's Python bindings and the pandas library to calculate the implied volatility of options prices. Additionally we will fit varying degrees of polynomials to the curves, examine the volatility surface, and look at the limitations of numerical computing in Python.


Actuarial Matrix Computations with Fewer Tears- Rehabilitating Correlations, Avoiding Inversion, and Extracting Roots. March 2013.  Institute and Faculty of Actuaries, Staple Inn Hall, London.

John Holden talked briefly about:

  • Numerical software and tools for the actuarial community
  • NAG and its collaboration with the University of Manchester

Professor Nicholas Higham delivered the keynote talk on three topics:

  • How to compute the nearest correlation matrix to an "almost correlation matrix", one having some small negative eigenvalues due to incomplete underlying data.
  • Why matrix inversion can and should usually be avoided.
  • How to compute a p'th root of a matrix for some positive integer p. with particular attention to stochastic matrices.

Actuarial Teachers' and Researchers' Conference, The University of Leicester, UK, 11th September 2012

NAG’s John Holden and Jacques Du Toit gave the following presentation “Numerical software & tools for the actuarial community

NAG Quant Seminar, New York, 2012

The Numerical Algorithms Group (NAG) and finance publication Wilmott held an evening seminar.
Professor Uwe Naumann presented "Fast Greeks through Adjoint Algorithmic Differentiation - and Further Speed-up through Mathematical and Structural Insight"
Low Bandwidth Recording:
Part 1 - Uwe Naumann
Part 2 - Uwe Naumann
High Bandwidth Recording:
Part 1 - Uwe Naumann
Part 2 - Uwe Naumann

John Holden of NAG presented "Latest releases and news from NAG"
Low Bandwidth Recording:
John Holden
High Bandwidth Recording:
John Holden

The talk recordings have been split into half-hour sections for easier and quicker rendering, and for faster buffering while viewing.


NAG at Global Derivatives 2012, Barcelona

White-Box Adjoint Parameter Calibration Analysis of Structure


NAG Quant Event, New York 2011

Multi-Regime Factor Analysis - Paul G Hipes – Hipes Research, Inc (PDF)
Webinar Recording
Functions of Matrices and Nearest Correlation Matrices - Nicholas J Higham, The University of Manchester (PDF)
Webinar Recording


NAG Quant Event, London 2011

Ely Klepfish - HSBC Bank - Portfolio maximum entropy and sampling error control (PDF)
Webinar Recording
Craig Lucas - Numerical Algorithms Group - Matrix functions, correlation matrices, and news from NAG (PDF)
Webinar Recording
Uwe Naumann - RWTH Aachen University - Adjoint Parameter Calibration in Computational Finance (PDF)
Webinar Recording


NAG Seminar with Nick Higham, June 2011

Functions of Matrices and Nearest Correlation Matrices
Video Recording Part 1, Part 2 and Part 3


NAG Quant Day, House of Finance, University of Frankfurt, January 2010

Asset Prices in General Equilibrium with Transactions Costs and Recursive Utility - Adrian Buss, Raman Uppal and Grigory Vilkov (PDF)
Fast Greeks by Automating the generation of first- and higher-order adjoints / The Art of Differentiating Computer Programs - Algorithmic Differentiation - Why and How? - Uwe Naumann (PDF)

Manchester Finance Day, February 2009
Manchester Business School hosted the NAG Financial Mathematics Day in February 2009.


NAG Quant Event, London 2009

Computing a Nearest Correlation Matrix with Factor Structure - Nicholas J Higham, The University of Manchester (PDF)
Video Recording
Using GPU's for Computational Finance - Mike Giles, The Mathematical Institute, University of Oxford (PDF)
Video Recording
Recent developments from NAG - John Holden, NAG Ltd (PDF)
Video Recording


NAG & Wilmott Quant Day, London 2008

Interest rate models for asset liability management - Ser-Huang Poon - Manchester Business School, University of Manchester (PDF)
Risk modelling, portfolio optimisation and performance backtest - Ely Klepfish, UBS AG (PDF)
Multilevel Monte Carlo Path Simulation - Mike Giles - Mathematical Institute, University of Oxford (PDF)


NAG & Wilmott Quant Day, New York 2007

Numerical Software, Market Data and Extreme Events - Dr Robert Tong, NAG (PDF)
Multilevel Monte Carlo Path Simulation - Dr Mike Giles, Oxford University Computing Laboratory (PDF)
“Dynamic Portfolio Optimization using Decomposition and Finite-Element Methods - Dr John Birge, Co-Founder of Quantstar, University of Chigaco (PDF)


NAG & Wilmott Finance Seminar, London 2006

Singular perturbation problems arising in mathematical finance: fluid dynamics concepts in option pricing - Peter W Duck, University of Manchester (PDF)
This lecture is available as Audio/Visual or as MP3 Audio
Software issues in wavelet analysis of financial data - Dr Robert Tong, NAG Ltd (PDF)
This lecture is available as Audio/Visual or as MP3 Audio
Can you count on your correlation matrix? - Nicholas J Higham, University of Manchester (PDF)