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Quantitative Finance

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

Posts 1 - 10 of 13

다음에 대한 결과: Risk Management

Responding to SR 26-2: From Strategic Interpretation to Practical Evidence | Part 3 of 4

Under SR 26-2, Misclassification Is the New Model Risk
When materiality determines rigor, classification becomes a first-order governance issue.

Series introduction
The first two blogs in this… 더 읽어보기 >>

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

Where SR 26-2 Creates Flexibility—and Where It Does Not
The opportunity is real, but it is narrower, more conditional, and more defensible-on-paper than a first read suggests.

Series… 더 읽어보기 >>

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 >>

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 >>

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 >>

Credit and Market Risk Management: From Risk Modeling to Regulatory Compliance

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… 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 >>

Navigating FRTB: Standardized vs Internal Models – and the Role of Scriptable Risk Engines

The Fundamental Review of the Trading Book (FRTB) is reshaping how banks measure and manage market risk. Beyond replacing Value at Risk (VaR) with Expected Shortfall (ES) to better capture tail risk… 더 읽어보기 >>

Highlights from the MathWorks Finance Conference 2024

The 2024 MathWorks Finance Conference brought together industry leaders to explore the evolving landscape of finance technology, with a focus on MATLAB applications. Across two days, participants… 더 읽어보기 >>

Posts 1 - 10 of 13