Forecasting Realized Volatility with Spillover Effects: Perspectives from Graph Neural Networks

This is the README file for the project Forecasting Realized Volatility with Spillover Effects: Perspectives from Graph Neural Networks, published in International Journal of Forecasting. It provides an overview of the project structure and instructions on how to use and contribute to the codebase.

Table of Contents

• Project Structure
• Usage
• Data
• Computing Environment

Project Structure

The project is organized as follows:

• data_subsample.py: Subsample the minutely data to 5 minutes and merge the data of all stocks in the stock list.
• compute_vol.py: Compute the daily variance from 5-min return data.
• GHAR.py: Linear models to forecast the realized volatility, including HAR and GHAR. HAR is a special case of GHAR, assuming the adjacency matrix is identity.
• GNNHAR.py: Proposed GNNHAR models to forecast the realized volatility.
• MCS.py: Implementation of Econometrica Paper: "The model confidence set." by Hansen, Peter R., Asger Lunde, and James M. Nason.
• Summary_Results.py: Summarize the results of the forecast models, including the MSE, QLIKE, and the MCS tests.
• Summary_Regime.py: Summarize the results of the forecast models, based on different regimes.
• BoxPlot_Error.py: Plot the boxplot of the forecast error and ratio for different models

Usage

To use the project, follow these steps:

1. Download LOBSTER data (minutely or higher freq) and save to your local path
2. Run data_subsample.py and compute_vol.py sequentially
3. Run GHAR.py to obtain the baseline forecasts from linear regressions
4. Run GNNHAR.py to obtain the forecasts for proposed GNNHAR models
5. Compare their forecasts by using Summary_Results.py and Summary_Regime.py
6. Generate plots by BoxPlot_Error.py

Data

The data used in this reproducibility check is LOBSTER (https://lobsterdata.com/), which needs to be purchased by users.

Computing Environment

To run the reproducibility check, the following computing environment and package(s) are required:

• Environment: These experiments were conducted on a system equipped with an Nvidia A100 GPU with 40 GB of GPU memory, an AMD EPYC 7713 64-Core Processor @ 1.80GHz with 128 cores, and 1.0TB of RAM, running Ubuntu 20.04.4 LTS.

• Package(s):

  • Python 3.8.18
  • PyTorch 2.0.1+cu117
  • numpy 1.22.3
  • pandas 2.0.3
  • scikit-learn 1.3.0
  • matplotlib 3.7.2