This project focuses on calibrating the Vasicek interest rate model using two statistical methods: Generalized Method of Moments (GMM) and Maximum Likelihood Estimation (MLE). The aim is to compare the effectiveness and accuracy of these techniques in estimating the model's parameters.
- Implementation of the Vasicek model, a mean-reverting stochastic process for interest rates.
- Calibration using:
- GMM: Leveraging moment conditions to estimate parameters.
- MLE: Maximizing the likelihood function to fit the model.
- Comparative analysis of parameter estimates and model performance.
- Visualization of calibration results and interest rate simulations.
- Python for numerical computations and modeling.
- Libraries:
numpy,scipy,pandas,matplotlib, andstatsmodels.
It is done during our second year at ENPC.
Link to the LaTeX report : https://www.overleaf.com/read/xjbmxcbvgvnf#9583d1
Link to the LaTeX code for the presentation of the professional development day on 04/11/2025 dedicated to the project : https://www.overleaf.com/read/xnzhrhfvxgkm#7870eb
Link to the LaTeX poster : https://www.overleaf.com/read/kbsfsqfcrtxr#62776d
Link to the LaTeX presentation : https://www.overleaf.com/read/szxrxwhwycps#9777db