arbiter
JournalsAccount

Loss-Based Bayesian Sequential Prediction of Value-at-Risk with a Long-Memory and Non-Linear Realized Volatility Model

Rangika Peiris et al.

Journal of Financial Econometrics2025https://doi.org/10.1093/jjfinec/nbaf017article
AJG 3ABDC A*
Weight
0.37

Abstract

A long-memory and non-linear realized volatility model class is proposed for direct Value-at-Risk (VaR) forecasting. This model, referred to as RNN-HAR, extends the heterogeneous autoregressive (HAR) model, a framework known for efficiently capturing long memory in realized measures, by integrating a Recurrent Neural Network (RNN) to handle the non-linear dynamics. Quantile loss-based generalized Bayesian method with Sequential Monte Carlo is employed for model estimation and sequential prediction in RNN-HAR. The empirical analysis is conducted using daily closing prices and realized measures with around 12 years of data till 2022, covering 31 market indices. The proposed model’s one-step-ahead VaR forecasting performance is compared against a basic HAR model and its extensions. The results demonstrate that the proposed RNN-HAR model consistently outperforms all other models considered in the study. The implementation code of the HAR-RNN model is publicly available on GitHub: https://github.com/chaowang-usyd/RNN-HAR.

1 citation

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1093/jjfinec/nbaf017

Or copy a formatted citation

@article{rangika2025,
  title        = {{Loss-Based Bayesian Sequential Prediction of Value-at-Risk with a Long-Memory and Non-Linear Realized Volatility Model}},
  author       = {Rangika Peiris et al.},
  journal      = {Journal of Financial Econometrics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/jjfinec/nbaf017},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Loss-Based Bayesian Sequential Prediction of Value-at-Risk with a Long-Memory and Non-Linear Realized Volatility Model

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.