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Trading volume and realized volatility forecasting: evidence from the China stock market

The existing contradictory findings on the contribution of trading volume to volatility forecasting prompt us to seek new solutions to test the sequential information arrival hypothesis (SIAH). Departing from other empirical analyses that mainly focus on sophisticated testing methods, this research...

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Main Authors: Liu, Min, Choo, Wei-Chong, Lee, Chi-Chuan, Lee, Chien-Chiang
Format: Article
Published: John Wiley and Sons 2022
Online Access:http://psasir.upm.edu.my/id/eprint/108331/
https://onlinelibrary.wiley.com/doi/10.1002/for.2897
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spelling my.upm.eprints.1083312025-03-05T02:06:06Z http://psasir.upm.edu.my/id/eprint/108331/ Trading volume and realized volatility forecasting: evidence from the China stock market Liu, Min Choo, Wei-Chong Lee, Chi-Chuan Lee, Chien-Chiang The existing contradictory findings on the contribution of trading volume to volatility forecasting prompt us to seek new solutions to test the sequential information arrival hypothesis (SIAH). Departing from other empirical analyses that mainly focus on sophisticated testing methods, this research offers new insights into the volume-volatility nexus by decomposing and reconstructing the trading activity into short-run components that typically represent irregular information flow and long-run components that denote extreme information flow in the stock market. We are the first to attempt at incorporating an improved empirical mode decomposition (EMD) method to investigate the volatility forecasting ability of trading volume along with the Heterogeneous Autoregressive (HAR) model. Previous trading volume is used to obtain the decompositions to forecast the future volatility to ensure an ex ante forecast, and both the decomposition and forecasting processes are carried out by the rolling window scheme. Rather than trading volume by itself, the results show that the reconstructed components are also able to significantly improve out-of-sample realized volatility (RV) forecasts. This finding is robust both in one-step ahead and multiple-step ahead forecasting horizons under different estimation windows. We thus fill the gap in studies by (1) extending the literature on the volume-volatility linkage to EMD-HAR analysis and (2) providing a clear view on how trading volume helps improve RV forecasting accuracy. John Wiley and Sons 2022 Article PeerReviewed Liu, Min and Choo, Wei-Chong and Lee, Chi-Chuan and Lee, Chien-Chiang (2022) Trading volume and realized volatility forecasting: evidence from the China stock market. Journal of Forecasting, 42 (1). pp. 76-100. ISSN 0277-6693; eISSN: 1099-131X https://onlinelibrary.wiley.com/doi/10.1002/for.2897 10.1002/for.2897
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description The existing contradictory findings on the contribution of trading volume to volatility forecasting prompt us to seek new solutions to test the sequential information arrival hypothesis (SIAH). Departing from other empirical analyses that mainly focus on sophisticated testing methods, this research offers new insights into the volume-volatility nexus by decomposing and reconstructing the trading activity into short-run components that typically represent irregular information flow and long-run components that denote extreme information flow in the stock market. We are the first to attempt at incorporating an improved empirical mode decomposition (EMD) method to investigate the volatility forecasting ability of trading volume along with the Heterogeneous Autoregressive (HAR) model. Previous trading volume is used to obtain the decompositions to forecast the future volatility to ensure an ex ante forecast, and both the decomposition and forecasting processes are carried out by the rolling window scheme. Rather than trading volume by itself, the results show that the reconstructed components are also able to significantly improve out-of-sample realized volatility (RV) forecasts. This finding is robust both in one-step ahead and multiple-step ahead forecasting horizons under different estimation windows. We thus fill the gap in studies by (1) extending the literature on the volume-volatility linkage to EMD-HAR analysis and (2) providing a clear view on how trading volume helps improve RV forecasting accuracy.
format Article
author Liu, Min
Choo, Wei-Chong
Lee, Chi-Chuan
Lee, Chien-Chiang
spellingShingle Liu, Min
Choo, Wei-Chong
Lee, Chi-Chuan
Lee, Chien-Chiang
Trading volume and realized volatility forecasting: evidence from the China stock market
author_facet Liu, Min
Choo, Wei-Chong
Lee, Chi-Chuan
Lee, Chien-Chiang
author_sort Liu, Min
title Trading volume and realized volatility forecasting: evidence from the China stock market
title_short Trading volume and realized volatility forecasting: evidence from the China stock market
title_full Trading volume and realized volatility forecasting: evidence from the China stock market
title_fullStr Trading volume and realized volatility forecasting: evidence from the China stock market
title_full_unstemmed Trading volume and realized volatility forecasting: evidence from the China stock market
title_sort trading volume and realized volatility forecasting: evidence from the china stock market
publisher John Wiley and Sons
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/108331/
https://onlinelibrary.wiley.com/doi/10.1002/for.2897
_version_ 1825810700195332096
score 13.244413

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