Tan, Shay Kee and Ng, Kok Haur and Chan, Jennifer So-Kuen (2023) Predicting returns, volatilities and correlations of stock indices using multivariate conditional autoregressive Range and return models. Mathematics, 11 (1). ISSN 2227-7390, DOI https://doi.org/10.3390/math11010013.
Full text not available from this repository.Abstract
This paper extends the conditional autoregressive range (CARR) model to the multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson volatility measures using individual series and their pairwise sums of indices to the MCARR model to obtain the fitted volatilities. Then covariances are calculated to construct the fitted variance-covariance matrix of returns which are imputed into the stage-two return model to capture the heteroskedasticity of assets' returns. We investigate different choices of mean functions to describe the volatility dynamics. Empirical applications are based on the Standard and Poor 500, Dow Jones Industrial Average and Dow Jones United States Financial Service Indices. Results show that the stage-one MCARR models using asymmetric mean functions give better in-sample model fits than those based on symmetric mean functions
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Range-based volatility; Correlation; Multivariate CARR-return model; Value-at-risk; Conditional value-at-risk |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
| Divisions: | Faculty of Science Faculty of Science > Institute of Mathematical Sciences |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 04 Nov 2024 07:44 |
| Last Modified: | 04 Nov 2024 07:44 |
| URI: | http://eprints.um.edu.my/id/eprint/39121 |
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