Algorithmic Differentiation training

I will present a training session on AD in December.
Location: Warsaw, Poland
Date: 13 December 2017 (full day)
Organizer: CEETA - Tomasz Dendura

Algorithmic Differentiation in Finance

Algorithmic Differentiation (AD) has been used in engineering and computer science for a long time. The term Algorithmic Differentiation can be explained as the art of calculating the differentiation of functions with a computer. AD is now also a standard tool in quantitative finance.

The workshop presents AD from a practical point of view and targets quantitative analyst, risk manager and developers working in finance. The focus is on the foundation of the method and the idiosyncrasies of the applications in finance. Different implementation alternatives are presented, allowing each participant to adapt the general method to his needs.

  • Introduction
    • AD is magic (or not)!
    • Exact derivatives
    • Finite difference
    • Development time v running time
  • The Principles of Algorithmic Differentiation
    • Derivative
    • Composition
    • Algorithm: assignment
    • Algorithm: branches
    • Algorithm: loops
  • Application to Finance
    • Basics: Black formula and SABR
    • Interest rate sensitivities
    • Monte Carlo
  • Automatic Algorithmic Differentiation
    • Standard Algorithmic Differentiation by Operator Overloading
    • Adjoint Algorithmic Differentiation by Operator Overloading
    • Automatic Algorithmic Differentiation applied to finance
    • Mixed Algorithmic Differentiation implementations
  • Application to Finance (2)
    • Automatic Algorithmic Differentiation applied to finance
    • Non-derivatives with respect to inputs - sticky smile
  • Calibration
    • Curve calibration
    • Model calibration and implicit function theorem: exact calibration
    • Model calibration: least-square

The lecture notes of the workshop are provided in the form of the recently published book Algorithmic Differentiation in Finance Explained, Palgrave (2017).

The workshop is backed by open source code (freely available on Github). On one side, the code is composed of tutorials of increased complexity. Those tutorials present the different fundamental principles of AD and propose several implementation for each of them. Another side of the code used is a full production grade quantitative finance library using AD as one of its tools. That library is used in production by hedge funds, banks and clearing houses.

Learning outcomes:

The mathematical foundations of Algorithmic Differentiation methods.
The effective application and use of AD in finance.
Beyond vanilla implementation: further efficiency gains specific to finance.

I'm also propose the course as in-house training. Don't hesitate to contact me for more details.

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