Expert Contributor: Dr. Eduard Benet Cerdà

Edu is a Senior Application Engineer at MathWorks advising customers in the development and deployment of financial applications. His focus… 更多内容 >>

R2026a covers a lot of ground for economists—Bayesian state-space estimation, macro-scale forecasting, climate and physical risk mapping, symbolic dynamics, and AI-assisted model review, among… 更多内容 >>

Effective risk management increasingly requires understanding how climate‑related factors can influence market valuations and balance‑sheet resilience. CRISK provides a transparent, market‑based… 更多内容 >>

Systemic risk modeling is essential for central banks as financial systems grow more interconnected and vulnerable to sudden shocks. From market implied indicators to climate stress testing and… 更多内容 >>

Nonlinear confidence bands help you quantify forecast uncertainty in DSGE models, but they can be slow to compute. At the MathWorks Finance Conference, Kadir Tanyeri (International Monetary Fund)… 更多内容 >>

Every MATLAB release opens the door to new capabilities, better performance, and tighter integration with the platforms your team already uses. With two major releases a year and over 800… 更多内容 >>

“It [MATLAB] was used in Dynare in order to promote the accuracy and the ease of generating this model.”— Allan Wright, Manager, Central Bank of the Bahamas

Watch the Full… 更多内容 >>

In this technical session, Valerio Sperandeo, Senior Application Engineer, demonstrated how MATLAB can support financial institutions in building robust, transparent, and scalable risk models aligned… 更多内容 >>

Dynamic Stochastic General Equilibrium (DSGE) models are essential tools for policy analysis and forecasting, but estimation runs often exceed 24 hours—particularly for large-scale models or Bayesian… 更多内容 >>

Summary
Nasdaq Private Market (NPM) used MATLAB® to prototype and scale physics‑informed neural networks (PINNs) that price Special Purpose Vehicles (SPVs) with embedded carried interest and… 更多内容 >>