Volatility forecasting for stock market incorporating media reports, investors' sentiment, and attention based on MTGNN model
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Forecasting"
DOI: 10.1002/for.3101
Abstract: In this paper, the self‐monitoring learning model FinBERT is used to identify text emotions, and the sliding time window time‐lagged cross‐correlation (WTLCC) method is utilized to screen Baidu Index keywords for the Shanghai Stock Exchange… read more here.
Keywords: attention; model; stock; volatility forecasting ... See more keywords
Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Forecasting"
DOI: 10.1002/for.3146
Abstract: Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high‐frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5‐min high‐frequency data… read more here.
Keywords: positive negative; volatility forecasting; forecasting incorporating; volatility ... See more keywords
The Information Content of Overnight Information for Volatility Forecasting: Evidence From China's Stock Market
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DOI: 10.1002/for.70011
Abstract: Using overnight volatility as the proxy for overnight information, this paper models future Chinese stock market realized range–based volatility (RRV) within a class of heterogeneous autoregressive models augmented by this proxy. We confirm the important… read more here.
Keywords: information; market; volatility; overnight information ... See more keywords
Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?
Sign Up to like & getrecommendations! Published in 2021 at "Annals of Operations Research"
DOI: 10.1007/s10479-021-04116-x
Abstract: This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency… read more here.
Keywords: volatility; cryptocurrency volatility; volatility forecasting; cryptocurrency ... See more keywords
Volatility forecasting in the Chinese commodity futures market with intraday data
Sign Up to like & getrecommendations! Published in 2017 at "Review of Quantitative Finance and Accounting"
DOI: 10.1007/s11156-016-0570-4
Abstract: Given the unique institutional regulations in the Chinese commodity futures market as well as the characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid contract series, to systematically… read more here.
Keywords: volatility; chinese commodity; volatility forecasting; commodity futures ... See more keywords
Volatility forecasting accuracy for Bitcoin
Sign Up to like & getrecommendations! Published in 2020 at "Economics Letters"
DOI: 10.1016/j.econlet.2019.108836
Abstract: Abstract We analyze the quality of Bitcoin volatility forecasting of GARCH-type models applying different volatility proxies and loss functions. We construct model confidence sets and find them to be systematically smaller for asymmetric loss functions… read more here.
Keywords: bitcoin volatility; volatility; forecasting accuracy; accuracy bitcoin ... See more keywords
Implied correlation indices and volatility forecasting
Sign Up to like & getrecommendations! Published in 2017 at "Applied Economics Letters"
DOI: 10.1080/13504851.2016.1213357
Abstract: ABSTRACT Implied volatility indices are an important measure for ‘market fear’ and well-known in academia and practice. Correlation is still paid less attention even though the CBOE started to calculate implied correlation indices for the… read more here.
Keywords: correlation; volatility forecasting; indices volatility; implied correlation ... See more keywords
A MS SHARV-MIDAS model: a new regime-switching model for volatility forecasting
Sign Up to like & getrecommendations! Published in 2024 at "Applied Economics Letters"
DOI: 10.1080/13504851.2024.2356003
Abstract: ABSTRACT The recent SHARV and SHARV-MIDAS models incorporate current returns information for volatility forecasting. However, these models fail to capture the intricate transition process of volatility states due to structural breaks caused by extreme events… read more here.
Keywords: sharv midas; volatility; midas model; volatility forecasting ... See more keywords
A Continuous Volatility Forecasting Model Based on Neural Differential Equations and Scale-Similarity
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2024.3376530
Abstract: Volatility forecasting is a problem in finance that attracts the attention of both academia and industry. While existing approaches typically utilize a discrete-time latent process that governs the volatility to forecast its future level, volatility… read more here.
Keywords: forecasting model; neural differential; volatility; continuous volatility ... See more keywords
A novel hybrid neural network-based volatility forecasting of agricultural commodity prices: empirical evidence from India
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Big Data"
DOI: 10.1186/s40537-025-01131-8
Abstract: This study presents a comprehensive analysis of agricultural price volatility forecasting using a hybrid long short-term memory (LSTM)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Agricultural price volatility poses critical challenges for food security, economic stability, and… read more here.
Keywords: agricultural commodity; india; volatility; price ... See more keywords
Historical Perspectives in Volatility Forecasting Methods with Machine Learning
Sign Up to like & getrecommendations! Published in 2025 at "Risks"
DOI: 10.3390/risks13050098
Abstract: Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks… read more here.
Keywords: perspectives volatility; machine learning; volatility; volatility forecasting ... See more keywords