Modelling and forecasting stock volatility and return: a new approach based on quantile Rogers–Satchell volatility measure with asymmetric bilinear CARR model

Tan Shay Kee, Chan Jennifer So Kuen () and Kok Haur Ng
Additional contact information
Tan Shay Kee: Institute of Mathematical Sciences, Faculty of Science, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia
Chan Jennifer So Kuen: Institute of Mathematical Sciences, Faculty of Science, University of Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia

Studies in Nonlinear Dynamics & Econometrics, 2022, vol. 26, issue 3, 437-474

Abstract: This paper proposes quantile Rogers–Satchell (QRS) measure to ensure robustness to intraday extreme prices. We add an efficient term to correct the downward bias of Rogers–Satchell (RS) measure and provide scaling factors for different interquantile range levels to ensure unbiasedness of QRS. Simulation studies confirm the efficiency of QRS measure relative to the intraday squared returns and RS measures in the presence of extreme prices. To smooth out noises, QRS measures are fitted to the CARR model with different asymmetric mean functions and error distributions. By comparing to two realised volatility measures as proxies for the unobserved true volatility, results from Standard and Poor 500 and Dow Jones Industrial Average indices show that QRS estimates using asymmetric bilinear mean function provide the best in-sample model fit based on two robust loss functions with heavier penalty for under-prediction. These fitted volatilities are then incorporated into return models to capture the heteroskedasticity of returns. Model with a constant mean, Student-t errors and QRS estimates gives the best in-sample fit. Different value-at-risk (VaR) and conditional VaR forecasts are provided based on this best return model. Performance measures including Kupiec test for VaRs are evaluated to confirm the accuracy of the VaR forecasts.

Keywords: CARR model; quantile Rogers–Satchell; range-based; value-at-risk; volatility (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1515/snde-2019-0101 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sndecm:v:26:y:2022:i:3:p:437-474:n:6

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/snde/html

DOI: 10.1515/snde-2019-0101

Access Statistics for this article

Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach

More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().