GARCH Modeling using B-TREASURY Data

Overview

This project aims to practice GARCH modeling using data from B-TREASURY. GARCH stands for Generalized Autoregressive Conditional Heteroskedasticity, which is a statistical model used to estimate the volatility of financial markets.

Project Steps

The project involves the following steps:

1. Data preprocessing: The B-TREASURY data needs to be preprocessed to extract the relevant features and convert the data into a usable format.
2. GARCH modeling: The preprocessed data is used to fit a GARCH model, which estimates the volatility of the market.

Installation

1. Clone the repository: git clone [email protected]:DAKDL/GARCH-model-practice.git
2. Install the required dependencies: pip install -r requirements.txt

Notebook

garch.ipynb

Contributing

Contributions are welcome! If you would like to contribute, please create a pull request with your changes.

License

This project is licensed under the MIT License.

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Author:
Vitvara Varavithya