Alternative Data for Realised Volatility Forecasting: Limit Order Book and News Stories

36 Pages Posted: 12 Sep 2020 Last revised: 19 Dec 2022

See all articles by Eghbal Rahimikia

Eghbal Rahimikia

The University of Manchester - Alliance Manchester Business School

Ser-Huang Poon

Alliance Manchester Business School, University of Manchester; Alan Turing Institute

Date Written: September 1, 2020

Abstract

This paper tests if the limit order book (LOB) and news stories from 27 July 2007 to 27 January 2022 can help forecast realised volatility (RV) for stocks. The out-of-sample forecasting results indicate that the CHAR model outperformed all other HAR-family of models. For high volatility days, the simple news count outperformed more sophisticated news sentiments compiled following Loughran and McDonald (2011), while LOB depth outperformed the LOB slope. This impact is more marked for news count than depth during the COVID pandemic. Interestingly, for normal volatility days, the market seems to be driven by buying pressure, as the ask-side of the LOB is more price informative than the bid-side. The reverse is true for high volatility days. A series of forecasting evaluation tests and alternative model specifications confirm our results' robustness.

Keywords: Realised Volatility Forecasting, Heterogeneous AutoRegressive models, Limit Order Book Data, News Stories, Sentiment Measures

JEL Classification: C22, C51, C53, C55, C58

Suggested Citation

Rahimikia, Eghbal and Poon, Ser-Huang, Alternative Data for Realised Volatility Forecasting: Limit Order Book and News Stories (September 1, 2020). Available at SSRN: https://ssrn.com/abstract=3684040 or http://dx.doi.org/10.2139/ssrn.3684040

Eghbal Rahimikia (Contact Author)

The University of Manchester - Alliance Manchester Business School ( email )

Booth Street West
Manchester, M15 6PB
United Kingdom

HOME PAGE: http://www.rahimikia.com

Ser-Huang Poon

Alliance Manchester Business School, University of Manchester ( email )

Alliance Manchester Business School
Booth Street West
Manchester, Manchester M15 6PB
United Kingdom
+44 161 275 4031 (Phone)
+44 161 275 4023 (Fax)

HOME PAGE: http://www.manchester.ac.uk/research/Ser-huang.poon/

Alan Turing Institute ( email )

British Library, 96 Euston Road
96 Euston Road
London, NW12DB
United Kingdom

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