Machine Learning for Realised Volatility Forecasting

Rahimikia, Eghbal; Poon, Ser‐Huang

doi:10.2139/ssrn.3707796

Cited by 23 publications

(34 citation statements)

References 47 publications

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“…We select the entries concerning all the stocks included in the S&P 500 index 3 as our universe. Compared with other researches (5 stocks in Mäkinen et al (2019), 23 stocks in Rahimikia and Poon (2020b)), this large selection of around 500 stocks also covers some less liquid stocks. We will show that it is more difficult to have a good prediction on less liquid stocks in Section 5.5.1.…”

Section: Lob Datamentioning

confidence: 87%

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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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Section: Lob Datamentioning

confidence: 87%

“…(LOB) data, showing that the use of LOB data can lead to better predicting results. More recently, Rahimikia and Poon (2020b) and Zhang and Rosenbaum (2020) use machine learning techniques such as Recurrent Neural Network (RNN) to improve such predictions.…”

Section: Introduce Several Simple Volatility Estimators Based On Limi...mentioning

confidence: 99%

Multivariate Realized Volatility Forecasting with Graph Neural Network

Chen¹,

Robert²

2021

Preprint

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“…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%

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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“…Recently, Xiong et al [60] have applied LSTMs to forecast S&P 500 volatility, with Google domestic trends as predictors, and Bucci [13] has demonstrated that RNNs are able to outperform all the traditional econometric methods in forecasting monthly volatility of the S&P index. More recently, Rahimikia and Poon [55] have compared machine learning models with HAR models for forecasting daily realized volatility by using variables extracted from limit order books and news. Li and Tang [48] have proposed a simple average ensemble model combining multiple machine learning algorithms for forecasting daily (and monthly) realized volatility, and Christensen et al [19] have examined the performance of machine learning models in forecasting one-day-ahead realized volatility with firm-specific characteristics and macroeconomic indicators.…”

Section: Related Literaturementioning

confidence: 99%

“…To assess the predictive performance for RV forecasts, we compute the following metrics 7 on the out-of-sample data (see [52,29,51,11,13,55]).…”

Section: Performance Evaluationmentioning

confidence: 99%

Volatility forecasting with machine learning and intraday commonality

Zhang¹,

Zhang²,

Cucuringu³

et al. 2022

Preprint

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We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting one-day-ahead RVs using past intraday RVs as predictors, and highlight interesting diurnal effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs.

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Machine Learning for Realised Volatility Forecasting

Cited by 23 publications

References 47 publications

Multivariate Realized Volatility Forecasting with Graph Neural Network

Multivariate Realized Volatility Forecasting with Graph Neural Network

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

Volatility forecasting with machine learning and intraday commonality

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