Multivariate Realized Volatility Forecasting with Graph Neural Network

Abstract

The existing publications demonstrate that the limit order book data is useful in predicting short-term volatility in stock markets. Since stocks are not independent, changes on one stock can also impact other related stocks. In this paper, we are interested in forecasting short-term realized volatility in a multivariate approach based on limit order book data and relational data. To achieve this goal, we introduce Graph Transformer Network for Volatility Forecasting. The model allows to combine limit order book features and an unlimited number of temporal and cross-sectional relations from different sources. Through experiments based on about 500 stocks from S&P 500 index, we find a better performance for our model than for other benchmarks.

Deepvol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

“…This has given rise to an increasing usage of volatility conditional portfolios (Harvey et al 2018), with different studies reporting an overall gain in their Sharpe ratio (Moreira and Muir 2017), as well as a reduction of the likelihood of observing extreme heavy-tailed returns in volatility scaled portfolios (Harvey et al 2018). The development of volatility forecasting models has consequently attracted broad research efforts, but most of the models used by practitioners are based on classic methodologies such as the GARCH model (Bollerslev poller 2020), Graph Neural Networks (GNN) (Chen and Robert 2021), Transformer models (Ramos-Pérez, Alonso-González, and Núñez-Velázquez 2021), and NLP-based word embedding techniques (Rahimikia and Poon 2020;Rahimikia, Zohren, and Poon 2021). Furthermore, models combining traditional volatility forecasting methods with deep-learning techniques can be found in the literature (Kim and Won 2018;Mademlis and Dritsakis 2021), as well as other approaches using DNN as calibration methods for implying volatility surfaces (Horvath, Muguruza, and Tomas 2019), proving how neural network-based approaches work as complex pricing function approximators.…”