Forecasting financial volatility: An approach based on Parkinson volatility measure with long memory stochastic range model

Zhi De Khoo, Kok Haur Ng, You Beng Koh and Kooi Huat Ng

Journal of Empirical Finance, 2025, vol. 82, issue C

Abstract: This paper proposes a long memory stochastic range (LMSR) model to investigate the persistence of range-based volatility series. The latent variable in the LMSR model is derived from the established autoregressive fractionally integrated moving average process. To estimate the model parameters, there is no closed-form solution for the latent process. Hence, the parameters of the stochastic model are estimated by applying the quasi-maximum likelihood method via the Whittle approximation. A comprehensive simulation study assesses the method’s performance, with results showing that estimated parameters are close to true values and precision improves with longer simulated time series lengths. To demonstrate the applicability of the model, we conducted empirical studies based on four financial assets, and their volatilities are estimated directly using the range-based Parkinson (PK) volatility measure. The results show evidence of long memory in these volatility series using the rescaled range and Geweke-Porter-Hudak methods. We fit the resulting PK volatility estimates to the LMSR model and other competing volatility models, and their modelling performances are compared. Results indicate that all LMSR models outperform competitors according to the log-likelihood and Akaike information criterion as well as out-of-sample loss functions. Additionally, the estimated parameters of these LMSR models confirm the presence of long memory, while competing short memory models struggle to capture the persistent nature of volatility in financial markets.

Keywords: Long memory; Range-based volatility; Parkinson measure; Long memory stochastic range model; Stochastic volatility model (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927539825000398
Full text for ScienceDirect subscribers only

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:eee:empfin:v:82:y:2025:i:c:s0927539825000398

DOI: 10.1016/j.jempfin.2025.101617

Access Statistics for this article

Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff

More articles in Journal of Empirical Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().