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Quantitative Finance

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

Posts 1 - 10 of 52

Responding to SR 26-2: From Strategic Interpretation to Practical Evidence | Part 1 of 4

SR 26-2 Did Not Lighten the Load. It Moved the Burden of Proof.
The real change is not the shorter guidance. It is the centrality of defensible judgment.

Series introduction
This is the first in a… 더 읽어보기 >>

Version Control for Economic Models

A Practical Guide to Git in MATLAB
 
The Problem You Already Have
You know the folder. Somewhere on your machine there is a directory that looks like this:
 
A familiar sight: version history encoded… 더 읽어보기 >>

From EViews to MATLAB in One Line: Reading Workfiles Directly

Economists often keep years of work in EViews workfiles: macroeconomic series, model estimates, and curated panel data. The MATLAB Reader for EViews Workfile reads .wf1 and .wf2 files into MATLAB,… 더 읽어보기 >>

Portfolio Optimization with Target Factor Exposures

A practical MATLAB walkthrough comparing tracking error and exact exposure approaches.

When you build a factor-based portfolio, the central design choice is how strictly to enforce your factor… 더 읽어보기 >>

Prototype Time-Series Forecasts with Deep Learning—Without Writing Code

Expert Contributor: Dr. Yuchen Dong

Yuchen is a Senior Application Engineer at MathWorks focusing on customers in the financial services industry. His focus areas are financial instruments,… 더 읽어보기 >>

Run Dynare at Scale on Databricks with Interactive MATLAB

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… 더 읽어보기 >>

What’s New in MATLAB R2026a for Economists

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… 더 읽어보기 >>

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 >>

Posts 1 - 10 of 52

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