/Graph-based-HAR

Code for Graph-based Methods for Forecasting Realized Covariances

Primary LanguagePythonMIT LicenseMIT

Graph-based Methods for Forecasting Realized Covariances

This is the README file for the project "Graph-based Methods for Forecasting Realized Covariances". It provides an overview of the project structure and instructions on how to use and contribute to the codebase.

Table of Contents

Project Structure

The project is organized as follows:

  • GHAR_Var.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.
  • GHAR_Corr.py: Linear models to forecast the realized correlation
  • GHAR_DRD.py: Combine the forecasted variance and correlation to get the forecasted covariance matrix
  • GMVP.py: Use forecasted covariance matrix to compute the GMVP portfolio, and record its performance
  • GMVP+.py: Use forecasted covariance matrix to compute the GMVP+ portfolio, and record its performance
  • MCS.py: Implementation of Econometrica Paper: "The model confidence set." by Hansen, Peter R., Asger Lunde, and James M. Nason.
  • Stats_MCS.py: Summarize the results of the forecast models, including the Euclidean, Frobenius, QLIKE, and the MCS tests.

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
    • numpy 1.22.3
    • pandas 2.0.3
    • scikit-learn 1.3.0
    • matplotlib 3.7.2