srvar-toolkit: A Python Implementation of Shadow-Rate Vector Autoregressions with Stochastic Volatility
Published:Dec 22, 2025 17:15
•1 min read
•ArXiv
Analysis
This article announces the release of a Python toolkit for implementing Shadow-Rate Vector Autoregressions with Stochastic Volatility. The focus is on providing a practical tool for researchers and practitioners in finance and econometrics to model and analyze financial time series data, particularly those involving shadow interest rates and volatility. The toolkit's availability on ArXiv suggests it's a pre-print or working paper, indicating ongoing research and development.
Key Takeaways
- •A Python toolkit is available for Shadow-Rate Vector Autoregressions with Stochastic Volatility.
- •The toolkit is aimed at researchers and practitioners in finance and econometrics.
- •The project is likely in active development, as indicated by its ArXiv publication.
Reference
“”