Big Data Approach to Realised Volatility Forecasting Using HAR Model Augmented With Limit Order Book and News

Rahimikia, Eghbal; Poon, Ser‐Huang

doi:10.2139/ssrn.3684040

Cited by 7 publications

(12 citation statements)

References 56 publications

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“…Also, for the other losses except for the VaR 2.5% , it yields lower losses than the HAR O . This is in line with Rahimikia and Poon (2020a), who also find that the CHAR is performing best among the HAR family models. Apart from the CHAR model, the remaining benchmark models cannot perform better than any of the ANN models except those that only use the transformed measure.…”

Section: Resultssupporting

confidence: 88%

“…Our analysis thus directs to a new type of HAR model that augments the classical HAR by a non-linear transformation of the HF returns within a day. These results are in line with the findings of Rahimikia and Poon (2020a), who also find that their proposed HAR model augmented by HF limited order book and news sentiment data shows superior forecasting performance. However, the information we utilize for the augmentation does not stem from an auxiliary source such as news feeds but from the same information used to construct the RV estimator.…”

Section: Discussionsupporting

confidence: 90%

“…Finally, Rahimikia and Poon (2020a) and Rahimikia and Poon (2020b) propose a HAR model augmented by an ANN applied to HF limited order book information and news sentiment data. In both papers, the authors find a superior forecasting performance of their model compared to the HAR benchmark.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Neural Network Based Non-linear Transformation of High-Frequency Returns for Volatility Forecasting

Mücher

2022

Front. Artif. Intell.

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This paper uses Long Short Term Memory Recurrent Neural Networks to extract information from the intraday high-frequency returns to forecast daily volatility. Applied to the IBM stock, we find significant improvements in the forecasting performance of models that use this extracted information compared to the forecasts of models that omit the extracted information and some of the most popular alternative models. Furthermore, we find that extracting the information through Long Short Term Memory Recurrent Neural Networks is superior to two Mixed Data Sampling alternatives.

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Section: Resultssupporting

confidence: 88%

Section: Discussionsupporting

confidence: 90%

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Artificial Neural Network Based Non-linear Transformation of High-Frequency Returns for Volatility Forecasting

Mücher

2022

Front. Artif. Intell.

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“…Following Barndorff-Nielsen et al ( 2009); Rahimikia and Poon (2020a), we first sample the data with a fixed frequency T f . Since we focus on short-term volatility forecasting in this paper, we use a one second sampling frequency instead of 5 minutes used by Rahimikia and Poon (2020a) to forecast daily volatility. Our sampling strategy is as follows:…”

Section: Data Samplingmentioning

confidence: 99%

Multivariate Realized Volatility Forecasting with Graph Neural Network

Chen¹,

Robert²

2021

Preprint

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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.

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“…The further development of the HAR-family of models continued with HAR-J (HAR with jumps) and CHAR (continuous HAR) of Corsi and Reno (2009), SHAR (semivariance-HAR) of Patton and Sheppard (2015), and HARQ model of Bollerslev et al (2016). The study of Rahimikia and Poon (2020a) provides a valuable comparison of the HAR-family of models and shows that the CHAR model is the best performing model considering 23 NASDAQ stocks. It also supplements the CHAR model with limit order book (LOB) data and sentiment variables extracted from financial news.…”

Section: Introductionmentioning

confidence: 99%

Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

Rahimikia,

Zohren,

Poon

2021

Preprint

Self Cite

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We develop FinText, a novel, state-of-the-art, financial word embedding from Dow Jones Newswires Text News Feed Database. Incorporating this word embedding in a machine learning model produces a substantial increase in volatility forecasting performance on days with volatility jumps for 23 NASDAQ stocks from 27 July 2007 to 18 November 2016. A simple ensemble model, combining our word embedding and another machine learning model that uses limit order book data, provides the best forecasting performance for both normal and jump volatility days. Finally, we use Integrated Gradients and SHAP (SHapley Additive exPlanations) to make the results more 'explainable' and the model comparisons more transparent.

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Big Data Approach to Realised Volatility Forecasting Using HAR Model Augmented With Limit Order Book and News

Cited by 7 publications

References 56 publications

Artificial Neural Network Based Non-linear Transformation of High-Frequency Returns for Volatility Forecasting

Artificial Neural Network Based Non-linear Transformation of High-Frequency Returns for Volatility Forecasting

Multivariate Realized Volatility Forecasting with Graph Neural Network

Realised Volatility Forecasting: Machine Learning via Financial Word Embedding

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