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Derivatives Pricing, Artificial Intelligence and Quantum Algorithms





Intensive and advanced course on the valuation of derivative products of variable income, fixed income, exchange rate and credit using traditional models, artificial intelligence and quantum computing.


The course shows strategies and hedging with options, advanced pricing models for interest rate options, implicit, local and stochastic volatility models and the Jump Diffusion Model.


For the valuation of interest rate options, there is a module that deals with the construction of the Yield Curve because it is extremely important for the valuation of interest rate derivative models. The Libor transition and the creation of the SOFR yield curve have been updated, which will impact the pricing of derivatives and XVA.

Innovatively, the use of powerful machine learning tools is exposed, particularly neural networks and deep learning, for the valuation of derivatives, calibration of stochastic differential equations, estimation of implicit volatility and creation of the yield curve.

The valuation of first and second generation exotic options is explained, using machine learning models.


Numerical methods are exposed, such as partial differential equations, PDEs, trinomial trees and traditional Monte Carlo simulation, in addition to increasing calculation speed, pricing of options is explained through Fourier transformations and improvements in Monte Carlo Simulation. To significantly speed up the estimation of the Greeks, the Adjoint of Automatic Differentiation algorithm has been included.


The course has a significant number of exercises in Julia, Python and R as well as case studies that enhance learning. The advantages and disadvantages of each language for the valuation of derivatives are addressed. We have introduced a new language, called Julia, which is powerful, simple and very fast. All exercises in the JupyterLab environment.

Monte Carlo simulation is the most widely used technique to value derivatives. However, banks are turning to machine learning in an attempt to give their pricing models a boost. The technique consists of training deep neural networks to approximate the results to Monte Carlo models without having to run millions of simulations. The neural network approximates the price of your portfolio when you run a gigantic and complicated XVA.

XVA valuation adjustment methodologies are exposed, among other models: estimation of credit exposure, Credit Value Adjustment CVA, debit value adjustment DVA, Funding Value Adjustment, CoLVA, MVA and KVA, exposing traditional methodologies and deep learning models.

Pricing is a computationally demanding task that is traditionally solved using Monte Carlo simulation techniques. However, Quantum Accelerated Monte Carlo (QAMC) quantum computing techniques promise quadratic acceleration over Monte Carlo (MC) simulation, this quantum advantage of increasing computational speed would bring substantial benefits, particularly for the huge volume of derivative products what is on the market.

The quantum advantage of QAMC over MC simulation and the advantage of quantum machine learning over the traditional one will be exposed.

Didactic course, clearly presented, with the experience and quality of Fermac Risk, seeking to make the participant's learning the most important thing. The course is practical to apply what is learned immediately at work.



People who work in the following departments: Investment Management, Treasury, Credit Risk Analysis, Portfolio Modeling, Model Validation, Quantitative Research, Structuring, Pricing, Market Risk, methodologies, financial management, financial controllers and Portfolios managers. From banks, brokerage firms, corporations, insurance, stockbrokers, brokerage firms and stockbrokers.


The course does not address abstract mathematics or complex theory. Nevertheless, the mathematical models are seriously explained. The student will know not only the theory but also practical exercises. It is advisable to master some programming language.





Price: 8.900 €



  • Europe: Mon-Fri, CEST 16-20 h


  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h






Level: Advanced


Duration: 40 h




  • Presentations PDF

  • Exercises in: Excel, R, Python and Jupyterlab 



Derivatives Pricing, Artificial Intelligence and Quantum Algorithms


Anchor 10

Modular Agenda 

Artificial Intelligence in Finance

​Module -2: AI in Finance (optional)

  • AI artificial intelligence in finance

  • Definition of Machine Learning

  • Machine Learning Methodology

    • Data Storage

    • Abstraction

    • Generalization

    • Assessment

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

  • deep learning

  • Typology of Machine Learning algorithms

  • Steps to Implement an Algorithm

    • Information collection

    • Exploratory Analysis

    • Model Training

    • Model Evaluation

    • Model improvements

  • Machine Learning in Finance

  • Applications in the valuation of options, projections, asset management and trading


Module -1: Deep Learning (optional)

  • Definition and concept of deep learning

  • Why now the use of deep learning?

  • Neural network architectures

  • Activation function

    • Sigmoidal

    • Rectified linear unit

    • Hypertangent

    • Softmax

  • Feedforward network

  • Multilayer Perceptron

  • Using Tensorflow

  • Using Tensorboard

  • R deep learning

  • Python deep learning

  • Convolutional Neural Networks

  • Use of deep learning in image classification

  • cost function

  • Gradient descending optimization

  • Using deep learning for credit scoring

    • How many hidden layers?

    • How many neurons, 100, 1000?

    • How many times and size of the batch size?

    • What is the best activation function?

  • Deep Learning Software: Caffe, H20, Keras, Microsoft, Matlab, etc.

  • Deployment software: Nvidia and Cuda

  • Hardware, CPU, GPU and cloud environments

  • Advantages and disadvantages of deep learning

  • Feedforward neural network

  • Multilayer Perceptron

  • Convolutional Neural Networks

  • Use of deep learning in image classification

  • recurrent neural networks

  • Temporal series

  • Long Short Term Memory

  • Exercise 1: Deep Learning feedforward perceptron neural network

Module 0: Quantum computing and algorithms (Optional)

  • Future of quantum computing in banking

  • Is it necessary to know quantum mechanics?

  • QIS Hardware and Apps

  • Quantum operations

  • Qubit representation

  • Complex numbers

  • Measurement

  • Overlap

  • Matrix multiplication

  • Qubit operations

  • Multiple Quantum Circuits

  • Entanglement

  • Deutsch Algorithm

  • Quantum Fourier transform and search algorithms

  • Hybrid quantum-classical algorithms

  • Quantum annealing, simulation and optimization of algorithms

  • Quantum machine learning algorithms

  • Quantum limitation

  • Quantum computers

  • Exercise 2: Quantum computing operations

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