Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Monte Carlo Training - Optimising Financial Risks and Pricing training are: Advanced mastery of Python (NumPy, Pandas), stochastic probabilities, and financial models like Black-Scholes. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Monte Carlo Training - Optimising Financial Risks and Pricing training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Monte Carlo Training - Optimising Financial Risks… | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Monte Carlo Training - Optimising Financial Risks and Pricing training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Monte Carlo Training - Optimising Financial Risks and Pricing training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Monte Carlo Training - Optimising Financial Risks and Pricing training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master advanced Monte Carlo algorithms for evaluating professional financial instruments

Develop certifying risk simulations tailored to business needs

Implement variance reduction techniques to accelerate calculations

Optimise Monte Carlo performance on massive financial datasets

Design Monte Carlo tools for exotic pricing and portfolio management

Deploy simulations compliant with prudential regulations in the enterprise

Training program

Module 1Monte Carlo Fundamentals: Advanced Algorithms and Convergence (NumPy, SciPy)

Dive into high-quality random number generators with NumPy and SciPy, simulate stochastic processes like geometric Brownian motions, analyze the law of large numbers and convergence tests on real bond pricing cases, perform practical exercises to calibrate your first financial simulations, produce convergence graphs and an analysis report to validate professional accuracy.

Module 2Monte Carlo Option Pricing: Vanilla and Barrier Derivatives (Black-Scholes Models)

Apply Monte Carlo to pricing European and Asian options via Euler-Maruyama discretisation, integrate hybrid binomial trees for cross-validation, handle exotic payoffs with antithetic variates, code optimised Python scripts on historical volatility datasets, test on real market scenarios like Eurostoxx50, generate parameter-sensitive pricing reports and deliverables ready for the trading desk.

Module 3Monte Carlo Risks: VaR, ES, and Backtesting (Diversified Portfolios)

Calculate Value at Risk and Expected Shortfall at 99% via historical and parametric simulations, integrate copula correlations for multiple assets, implement Basel III regulatory backtesting with Pandas, simulate extreme market shocks on bond-equity portfolios, analyze loss curves, produce stress test tables and an interactive Matplotlib dashboard for enterprise risk management reporting.

Module 4Advanced Monte Carlo: Quasi-Monte Carlo and Importance Sampling (Variance Reduction)

Master Sobol and Halton sequences for ultra-precise Quasi-Monte Carlo, apply control variates and importance sampling to lookback options, reduce variance by 90% on massive simulations of 10^7 paths, optimise with NumPy vectorisation, test on concrete credit risk cases, generate performance comparisons and a reusable Python toolkit to accelerate daily financial analyses.

Module 5Enterprise Monte Carlo Applications: Optimisation and Deployment (Production Stress Testing)

Design simulations for Markowitz portfolio optimisation under stochastic constraints, integrate machine learning for dynamic calibration, deploy on AWS cloud with multiprocessing parallelisation, perform CCAR-like stress tests on bank balance sheets, validate FRTB compliance, produce a complete red thread project with Flask API, documentation, and production rollout plan for certified professional finance skills.