Graph Signal Processing for Global Stock Market Realized Volatility Forecasting (2410.22706v2)
Abstract: This paper introduces an innovative realized volatility (RV) forecasting framework that extends the conventional Heterogeneous Auto-Regressive (HAR) model via integrating the Graph Signal Processing (GSP) technique. The volatility spillover effect is embedded and modeled in the proposed framework, which employs the graph Fourier transformation method to effectively analyze the global stock market dynamics in the spectral domain. In addition, convolution filters with learnable weights are applied to capture the historical mid-term and long-term volatility patterns. The empirical study is conducted with RV data of $24$ global stock market indices with around $3500$ common trading days from May 2002 to June 2022. The proposed model's short-term, middle-term and long-term RV forecasting performance is compared with various HAR type models and the graph neural network based HAR model. The results show that the proposed model consistently outperforms all other models considered in the study, demonstrating the effectiveness of integrating the GSP technique into the HAR model for RV forecasting.
- Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review 39(4), 885–905.
- Dynamic connectedness of uncertainty across developed economies: A time-varying approach. Economics Letters 166, 63–75.
- Risk Everywhere: Modeling and Managing Volatility. The Review of Financial Studies 31(7), 2729–2773.
- Volatility transmission in emerging european foreign exchange markets. Journal of Banking & Finance 35(11), 2829–2841.
- Discrete signal processing on graphs: Sampling theory. IEEE Transactions on Signal Processing 63(24), 6510–6523.
- Cheung, Y.-W. and K. S. Lai (1995). Lag order and critical values of the augmented dickey–fuller test. Journal of Business & Economic Statistics 13(3), 277–280.
- A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics 7(2), 174–196.
- Diebold, F. X. and R. S. Mariano (2002). Comparing predictive accuracy. Journal of Business & Economic Statistics 20(1), 134–144.
- Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. The Economic Journal 119(534), 158–171.
- Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting 28(1), 57–66. Special Section 1: The Predictability of Financial Markets Special Section 2: Credit Risk Modelling and Forecasting.
- No contagion, only interdependence: Measuring stock market comovements. The Journal of Finance 57(5), 2223–2261.
- Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432–441.
- Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method. International Review of Financial Analysis 76, 101790.
- The model confidence set. Econometrica 79(2), 453–497.
- Kanas, A. (2000). Volatility spillovers between stock returns and exchange rate changes: International evidence. Journal of Business Finance & Accounting 27(3-4), 447–467.
- Is implied volatility more informative for forecasting realized volatility: An international perspective. Journal of Forecasting 39(8), 1253–1276.
- A magnetic framelet-based convolutional neural network for directed graphs. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE.
- MacKinnon, J. G. (1994). Approximate asymptotic distribution functions for unit-root and cointegration tests. Journal of Business & Economic Statistics 12(2), 167–176.
- Forecasting volatility in financial markets: A review. Journal of Economic Literature 41(2), 478–539.
- A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32(1), 4–24.
- Quantitative easing and volatility spillovers across countries and asset classes. Management Science 63(2), 333–354.
- Graph neural networks for forecasting multivariate realized volatility with spillover effects.
- Magnet: A neural network for directed graphs. Advances in Neural Information Processing Systems 34, 27003–27015.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.