Testing and comparing the performance of dynamic variance and correlation models in value-at-risk estimation
Leon Li ()
The North American Journal of Economics and Finance, 2017, vol. 40, issue C, 116-135
This study addresses and examines certain advanced approaches for value-at-risk (VaR) estimation. In particular, we employ a multivariate generalized autoregressive conditionally heteroskedastic (MVGARCH) model involving time-varying settings and multivariate Markov switching autoregressive conditionally heteroskedastic (MVSWARCH) model with regime-switching techniques and compare them with a conventional linear regression-based (LRB) model. Our empirical findings are as follows: First, while the LRB VaR model behaves reasonably well in tranquil periods, it significantly underestimates actual risk during unstable periods. Second, in comparison with the LRB VaR model, MVGARCH- and MVSWARCH-based VaR models do better under unusual conditions, whereas better models are needed to estimate VaR. Third, dynamic variance settings improve the accuracy of VaR estimates. However, the effect of dynamic correlation designs on VaR is marginal.
Keywords: Value at risk; Stochastic volatility; Dynamic conditional correlation (search for similar items in EconPapers)
JEL-codes: G15 G32 C53 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:40:y:2017:i:c:p:116-135
Access Statistics for this article
The North American Journal of Economics and Finance is currently edited by Hamid Beladi
More articles in The North American Journal of Economics and Finance from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().