smq/directoryAn evolving quant resource archive
Back to directoryResource
Paper / 2026SMQ / 208914159460306
Incremental SVD for Large-Scale Dynamic Matrices: Accuracy, Subspace Stability, Refresh Strategies, and Financial Factor-Based Risk Models
Stilyan Staykov
linear algebra / numerical methodsResource overview

Incremental SVD for Large-Scale Dynamic Matrices: Accuracy, Subspace Stability, Refresh Strategies, and Financial Factor-Based Risk Models

Paper2026

by Stilyan Staykov

Tags
linear algebranumerical methods

Access resource

Open original source Understand this paper

Continue on SelfMadeQuant

Understand this paper

Suggested foundations for optimisation and numerical methods and risk measurement and stress. Specialist prerequisites vary by paper.

Before you read

  1. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

  2. Constrained and Convex Optimisation

    Identify the objective, constraints and conditions for an optimum.

  3. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  4. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

Go further

  1. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

  2. Numerical Conditioning and Stability

    Separate model error from unstable numerical computation.

  3. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

Explore a related reading path

Lesson links open public previews. Access to full lessons depends on the topic and your membership.

Explore Linear Algebra, Optimisation and Numerical Methods Explore Quantitative Risk Management

Keep exploring

Related resources

More ideas from similar topics.

6