MathWorks FINANCE CONFERENCE 2024

Join Thierry Roncalli, head of quantitative research at Amundi, as he explores the practical applications of MATLAB^® in teaching sustainable finance. This presentation covers key topics like carbon taxes, portfolio decarbonization, and stress testing. Thierry will also highlight exciting results from his recent research and provide a sneak peek into his forthcoming book, Handbook of Sustainable Finance (March 2025).

In this presentation learn about the RISE Toolbox, designed for solving, estimating, and analyzing nonlinear regime-switching DSGE models. Regime-switching models are essential for addressing the complexities of economic environments, such as the zero lower bound, financial crises, and high inflation periods. See how RISE accommodates various macroeconomic models, including DSGE VARs, panel VARs, and optimal policy frameworks. Gain insights into solving these models using RISE, the benefits of regime-switching for economic forecasting, and real-world applications in policy analysis and simulation.

Financial modeling stands as the bedrock of decision making in the finance industry, yet it is undergoing transformative changes in today's fast-paced and complex environment. Rapid advancements in mathematical methods, heightened regulatory demands, the transformative potential of cloud computing, the explosion of accessible data sources, and the rise of (generative) AI are revolutionizing the field. These developments make the work of financial professionals both exhilarating and challenging.

Modern modeling tools must harness these technologies in a user-friendly, transparent, and customizable manner. In this presentation, see how practitioners leverage MATLAB^® to tap into a comprehensive ecosystem of data and tools for swift and robust modeling. Explore compelling examples, including econometric modeling tools, climate scenarios, geophysical data sources, and collaboration in multilanguage setups. Discover how MATLAB empowers financial professionals to stay ahead in an ever-evolving landscape.

In this presentation, learn about a nonlinear model aggregation approach for forecasting Canadian core inflation that is based on a random forest model, which takes as input a collection of forecasts and macroeconomic data.

The forecasts are produced by a variety of submodels, including structural, semistructural, statistical, and machine learning models. Canadian data is used to train and evaluate both the submodels and the aggregate model. The aggregate model has greater predictive power than any of the individual models over the evaluation data, and better predictive power than other model aggregation methodologies. Additionally, the nonlinear aggregate model responds more rapidly than other models to changes in the data, such as regime shifts.

The internal model of an insurance company models stochastic cash flows to predict figures such as required solvency capital for a one-year time horizon. Growing over the past twenty years from rather simple to more complex and sophisticated, Hannover Re’s internal model was based on numerous manual processes until 2018, when they started an automation project. As MATLAB^® usage has steadily increased over the years, it was only natural to build an automated version based on MATLAB as a programming language. In this presentation, learn how Hannover Re managed to keep flexibility, reduce run time and manual steps, avoid redundancies, and meet regulatory and compliance requirements.

In this presentation, discover a novel approach to counterparty risk assessment using a two-step process: Monte Carlo simulations and parallel computing. This method leverages Monte Carlo simulations to model and quantify risk under various scenarios, followed by parallel computing to efficiently process and analyze large datasets. Learn how this integrated approach enhances the accuracy and speed of risk assessments, enabling more robust financial decision making. See practical examples and insights drawn from extensive experience in quantitative analysis and financial modeling.

Explore the latest advancements for accelerating the delivery of financial services, and discover how cloud-connected and cloud-hosted software, including private and public cloud options, can bring AI-powered financial services to market. In the midst of the hype, AI, deep learning, and large language models are rarely acceptable for production use, unless their development and implementation has been carried out in a transparent, explainable and reproducible manner.

See how a ModelOps platform with data governance, continuous integration, and testing is built from reference architectures to leverage the growing plethora of technologies in a coherent, reproducible, and transparent workflow. This will include:

• MATLAB^® in JupyterHub and data science platforms.
• Verification, documentation, and management of deep learning networks.
• Deployment and monitoring of containerized models.