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Quantitative Finance

Investment Management, Risk Management, Algorithmic Trading, Econometric Modeling, Pricing and Insurance

다음에 대한 결과: Time Series

CRISK: A Market‑Based Framework for Quantifying Climate Risk in Banking

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

Systemic Risk Modeling with MATLAB: Tools and Techniques for Central Banks

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… 더 읽어보기 >>

Refining Macroeconomic Forecasting with MATLAB Techniques

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)… 더 읽어보기 >>

Speeding Up Dynare Models: Practical Paths to Performance Gains

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… 더 읽어보기 >>

Pricing Special Purpose Vehicles with Physics‑Informed Neural Networks at Nasdaq Private Market

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… 더 읽어보기 >>

Deep Learning in Quantitative Finance: Transformer Networks for Time Series Prediction

The following blog was written by Owen Lloyd , a Penn State graduate who recently join the MathWorks Engineering Development program.
The code used to develop this example can be found on GitHub… 더 읽어보기 >>