How do invariant transformations affect the calibration and optimization of the Kalman filtering algorithm used in the estimation of continuous-time affine term structure models?
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Publication:2033711
DOI10.1007/s10287-020-00380-7OpenAlexW3115592416MaRDI QIDQ2033711
Publication date: 17 June 2021
Published in: Computational Management Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10287-020-00380-7
optimizationstochastic processMonte Carlo simulationKalman filterinvariant transformationdynamic affine term structure models
Cites Work
- Identification and estimation of Gaussian affine term structure models
- The affine arbitrage-free class of Nelson-Siegel term structure models
- Staying at zero with affine processes: an application to term structure modelling
- Estimation of affine term structure models with spanned or unspanned stochastic volatility
- Performance Analysis of the Kalman Filter With Mismatched Noise Covariances
- A Concise Parametrization of Affine Transformation
- SOLVABLE AFFINE TERM STRUCTURE MODELS
- A NOTE ON THE DAI-SINGLETON CANONICAL REPRESENTATION OF AFFINE TERM STRUCTURE MODELS*
- An arbitrage‐free generalized Nelson–Siegel term structure model
- Numerical aspects of different Kalman filter implementations
- Exact Smooth Term-Structure Estimation
- Diffusion Strategies for Distributed Kalman Filtering and Smoothing
- Dynamic Term Structure Models Using Principal Components Analysis Near the Zero Lower Bound
- An equilibrium characterization of the term structure
- Worst-case Prediction Performance Analysis of the Kalman Filter
- Rotating 5D-Kaluza-Klein space-times from invariant transformations
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