Course Feature: Intensive

Introduction to QuantLib Development

What do you learn?

  • The overall design of the QuantLib library
  • The rationale of its design and implementation
  • The correct use of the main classes in the library
  • The design and use of some of its framework, such as the tree and Monte Carlo frameworks
Day 1—Overall Design
  • The Instrument class
    Interface and rationale / Tracking market changes: the Quote class / Responding to market changes / Examples
  • Pricing engines
    Shortcomings of the original Instrument class / The PricingEngine class / Examples / Exercises
  • Term structures
    The TermStructure class / Yield term structures / Curve bootstrap/ Examples / Exercises
Day 2—The Monte Carlo Framework
  • Path generation
    Random-number generation / The StochasticProcess class / Implementing a stochastic process / Path generation / Examples / Exercises
  • Path pricers
    The PathPricer class / Implementing a path pricer / Possible extensions / Exercises
  • Putting it all together
    Monte Carlo traits / Monte Carlo simulations / Implementing a Monte Carlo engine / Examples / Exercises
Day 3—The Tree Framework
  • Pricing on a lattice
    The Lattice class / The DiscretizedAsset class / Their interplay/ Examples
  • Tree-based lattices
    The Tree class / Binomial and trinomial trees/ Tree-based lattices / Short-rate models
  • Tree-based engines
    Implementing a discretized asset / Choosing a tree-based model / Calibration / Examples / Ex-ercises

Machine Learning for Option Pricing, Calibration and Hedging

The goal of this two-day workshop is to provide a detailed overview of machine learning techniques applied for finance. We offer insights into the latest techniques for modelling financial markets and focus on option pricing and calibration.

We not only tackle the theory but give practical guidance and live demonstrations of the computational methods involved. After introducing the subject we cover Gaussian Process Regression and Artificial Neural Networks and show how such methods can be applied to solve option pricing problems, speed up the calculation of xVAs or apply them for hedging.

We further show how to use existing pricing libraries to interact with machine learning environments often set up in Python.

We explain how to set up the methods mainly in Python using Keras, Tensorflow or SciKit Learn. We give many examples which are directly related to financial mathematics and can be explored further after the course. All the material is available as Jupyther notebooks. For Gaussian Processes we use Matlab and Python examples.

This workshop covers the fundamentals and it illustrates the application of state-of-the-art machine learning applications for application to Mathematical Finance.

The workshop is designed as a 2-day event.