Maximum likelihood estimation of the Cox-Ingersoll-Ross model using particle filters
From MaRDI portal
Publication:977000
DOI10.1007/s10614-010-9208-0zbMath1231.91485OpenAlexW2032789865MaRDI QIDQ977000
Publication date: 16 June 2010
Published in: Computational Economics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10614-010-9208-0
Inference from stochastic processes and prediction (62M20) Statistical methods; risk measures (91G70) Monte Carlo methods (65C05) Interest rates, asset pricing, etc. (stochastic models) (91G30)
Related Items
CALIBRATION OF THE UNI-VARIATE COX–INGERSOLL–ROSS MODEL AND PARAMETERS SELECTION THROUGH THE KULLBACK–LEIBLER DIVERGENCE ⋮ Parameter estimation in CKLS model by continuous observations ⋮ Two methods of estimation of the drift parameters of the Cox–Ingersoll–Ross process: Continuous observations ⋮ Estimation for incomplete information stochastic systems from discrete observations ⋮ Least squares estimation for discretely observed stochastic Lotka-Volterra model driven by small \(\alpha \)-stable noises ⋮ Estimation of parameters in mean-reverting stochastic systems ⋮ Estimation for a second-order jump diffusion model from discrete observations: application to stock market returns ⋮ How to maximize the likelihood function for a DSGE model
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- The multifactor nature of the volatility of futures markets
- The volatility structure of the fixed income market under the HJM framework: a nonlinear filtering approach
- Parameter estimation in general state-space models using particle methods
- A Theory of the Term Structure of Interest Rates
- Filtering via Simulation: Auxiliary Particle Filters
- Pricing Interest-Rate-Derivative Securities
- Monte Carlo Smoothing for Nonlinear Time Series
- A Monte Carlo filtering approach for estimating the term structure of interest rates