This repository contains a comprehensive suite of econometric and statistical tools for analyzing the relationship between Exchange Rate Volatility and Stock Market Performance. Using advanced time-series techniques, this project aims to uncover lead-lag relationships, volatility clustering, and long-term co-integration.
The project is structured around several high-level analytical frameworks:
GARCH and wavelet 2.py: Implementation of Generalized Autoregressive Conditional Heteroskedasticity models to capture volatility clustering.GARCH residual diagnostic.py: Statistical testing (ARCH-LM, Ljung-Box) to ensure model fitness and white noise residuals.
wavelet power spectrum.py: Decomposing time series into different frequency components to observe how the exchange-stock relationship changes across different time horizons (short-term vs. long-term).fourier and wavelet practice.py: Comparative spectral analysis.
ardl model.py: Autoregressive Distributed Lag (ARDL) models for identifying long-run and short-run dynamics between variables.var and irf.py: Vector Autoregression (VAR) and Impulse Response Functions (IRF) to simulate how a shock in exchange rates affects the stock market.
| Script | Purpose |
|---|---|
stationarity test...py |
Unit root testing via ADF, KPSS, and Phillips-Perron (PP) tests. |
robustness checks.py |
Sensitivity analysis to validate the consistency of the econometric results. |
final excrf.py |
The primary execution script for the final exchange rate framework. |
To run these scripts, you will need Python 3.x and the following quantitative libraries:
pip install pandas numpy statsmodels arch pywavelets matplotlib seaborn- Stationarity First: Run the ADF/KPSS scripts to ensure your data is integrated of the same order.
- Model Selection: Use the ARDL or GARCH scripts based on your research objectives.
- Visualization: Use the Wavelet Power Spectrum to generate heatmaps of volatility across time scales.
This project is licensed under the MIT License - see the file for details.
This repository is part of a broader research project into financial market interdependencies. If you find these scripts useful for your own academic work, feel free to fork the repo or submit a Pull Request.
Maintained by Vipeen Kumar
Do you have a specific dataset or country (like India) that this analysis focuses on? I can add a "Data" section if you do!