Autocallable Pricing with Machine Learning

Starting point

Valuing multi-underlying autocallables using Monte Carlo simulations is computationally intensive and time-consuming – especially with complex volatility models such as the Heston framework. Long runtimes make intraday valuations, scenario analyses and sensitivity calculations difficult. The goal was to combine classic numerical pricing methods with modern machine learning approaches to improve performance and scalability significantly.

Client benefits & business case

The focus was on reducing computation time while keeping model fidelity. Faster price calculations enable more efficient quoting, more precise risk analyses and better use of computing resources. Approximating complex pricing functions with deep neural networks also creates the basis for scalable intraday risk calculations and capital optimisation. The business case lies in higher competitiveness through speed, stability and cost reduction.

Opportunities & challenges

Combining Monte Carlo simulation and deep learning offered the chance to approximate high-dimensional price functions efficiently. Challenges lay in generating training data cleanly, in the stability of neural networks across different market regimes and in ensuring numerical consistency with classic valuation methods. Mapping stochastic volatility in the Heston model was particularly demanding.

Solutions / deliverables

A hybrid approach was developed: Monte Carlo simulations were used to generate high-quality training data, while deep neural networks served as the approximation engine for the pricing function. Models were validated, sensitivities analysed and performance benchmarks run. The result is a significantly accelerated price calculation with controlled deviation from the reference model.

Our contribution

We designed the quantitative model, implemented Monte Carlo and Heston-based pricing frameworks in Python and developed the deep learning architecture for price approximation. Alongside model validation and performance optimisation, numerical robustness was a focus.

Contact

If you want to value complex structured products faster while remaining model-true, we support you with quantitative expertise and modern machine learning capability – from the model idea to production-ready implementation.