Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5102239 | The North American Journal of Economics and Finance | 2017 | 20 Pages |
Abstract
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.
Related Topics
Social Sciences and Humanities
Economics, Econometrics and Finance
Economics and Econometrics
Authors
Leon Li,