MathWorks FINANCE CONFERENCE 2024

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.

The RISE Toolbox is 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.

This presentation employs threshold VAR models to investigate the asymmetric impact of oil supply news shocks, analyzing variations in both the size and direction of the shocks. Our findings reveal that large and adverse oil shocks exert a stronger effect on real activity, labor market indicators, and risk variables than small and favorable shocks. Interestingly, we observe no asymmetry in the response of prices and monetary policy to oil shocks of different magnitudes and signs. Supported by a theoretical framework integrating search and matching frictions with Epstein-Zin preferences, the study utilizes Dynare, MATLAB^®, and MATLAB Parallel Server™ to enhance model estimation and provide valuable insights into the nonlinear dynamics of oil shocks.

Building resilience to natural disasters is imperative due to the rising threats posed by climate change, particularly for vulnerable developing economies. The DIGNAD (Debt-Investment-Growth and Natural Disasters) model was developed to analyze macro-fiscal implications of climate shocks and the role of economic policies in mitigating associated risks. DIGNAD is a small open economy model that allows users to assess debt sustainability risks linked to major natural disasters while explicitly considering the need to rebuild public infrastructure. Its rich general equilibrium structure allows for the construction of counterfactuals as well as broader scenario analysis involving ex-ante policies such as investing in resilient infrastructure, increasing fiscal buffers, and improving public investment efficiency. The recently developed DIGNAD toolkit offers a user-friendly interface, making the model accessible to users with limited experience coding with Dynare.

This session examines whether global decarbonization efforts are progressing by analyzing the latest trends using ICE's climate transition finance data. Monika Sabolova and Girish Narula from ICE, together with Marshall Alphonso from MathWorks, will present key findings on carbon emissions, intensities, and net-zero alignment across corporate portfolios. Learn how this data is effectively processed and visualized using MATLAB^®, providing actionable insights for professionals focused on advancing sustainability initiatives.

The financial industry is increasingly using machine learning and AI to enhance its methodologies. Among these advancements, physics-informed neural networks (PINNs) offer a way to incorporate domain-specific knowledge into neural network training, making them effective for solving partial differential equations (PDEs) in finance.

This presentation demonstrates how PINNs can be applied to the Black-Scholes model for option pricing. Using MATLAB® and Deep Learning Toolbox™, we will show how to set up neural networks interactively and customize loss functions to address financial problems with greater precision.