Document Type
Article
Publication Date
2025
DOI
10.3390/economies13030068
Publication Title
Economies
Volume
13
Issue
3
Pages
68 (1-28)
Abstract
The multivariate Vasicek model is commonly used to capture mean-reverting dynamics typical for short rates, asset price stochastic log-volatilities, etc. Reparametrizing the discretized problem as a VAR(1) model, the parameters are oftentimes estimated using the multivariate least squares (MLS) method, which can be susceptible to outliers. To account for potential model violations, a maximum trimmed likelihood estimation (MTLE) approach is utilized to derive a system of nonlinear estimating equations, and an iterative procedure is developed to solve the latter. In addition to robustness, our new technique allows for reliable recovery of the long-term mean, unlike existing methodologies. A set of simulation studies across multiple dimensions, sample sizes and robustness configurations are performed. MTLE outcomes are compared to those of multivariate least trimmed squares (MLTS), MLE and MLS. Empirical results suggest that MTLE not only maintains good relative efficiency for uncontaminated data but significantly improves overall estimation quality in the presence of data irregularities. Additionally, real data examples containing daily log-volatilities of six common assets (commodities and currencies) and US/Euro short rates are also analyzed. The results indicate that MTLE provides an attractive instrument for interest rate forecasting, stochastic volatility modeling, risk management and other applications requiring statistical robustness in complex economic and financial environments.
Rights
© 2025 by the authors.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Data Availability
Article states: "The short-rate data reported in this work are available at https://github.com/AndrewsJunior/Vasicek-MTLE (accessed on 25 February 2025). The dataset containing the six assets can be requested from the authors of the paper Chang and Shi (2024)."
Original Publication Citation
Fullerton, T. M., Pokojovy, M., Anum, A. T., & Nkum, E. (2025). Maximum trimmed likelihood estimation for discrete multivariate Vasicek processes. Economies, 13(3), 1-28, Article 68. https://doi.org/10.3390/economies13030068
ORCID
0000-0002-2122-2572 (Pokojovy)
Repository Citation
Fullerton, Thomas M. Jr.; Pokojovy, Michael; Anum, Andrews T.; and Nkum, Ebenezer, "Maximum Trimmed Likelihood Estimation for Discrete Multivariate Vasicek Processes" (2025). Mathematics & Statistics Faculty Publications. 285.
https://digitalcommons.odu.edu/mathstat_fac_pubs/285
Supporting Information
Included in
Applied Mathematics Commons, Econometrics Commons, Economic Policy Commons, Macroeconomics Commons
