Existing research suggests that the economic gains from volatility timing stem from covariance forecasting and portfolio construction, yet their relative importance remains unclear. This paper disentangles the contributions of these two channels by jointly evaluating three co-variance models, the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model (DCC), the range based Multiplicative Error Model (MEM), and the Multivariate Stochastic Volatility model (MSV), together with three portfolio construction strategies, minimum variance (MIN), maximum return (MAX), and volatility managed (VM). Our results show that the MSV model consistently outperforms DCC and MEM, highlighting the role of stochastic volatility in improving covariance estimation. From a portfolio perspective, both MAX and VM deliver higher economic value than MIN. However, there is no clear evidence that either MAX or VM consistently dominates the other, suggesting that volatility managed strategies do not systematically outperform the standard mean–variance framework.
- 9926853437701891
- The economic value of forecasting and strategy gains in volatility timing
- Wen XuPakorn AschakulpornJin E. Zhang
- Accountancy and Finance
- Finance research letters, Vol.99, 109831
- Elsevier
- 27/03/2026
- University of Otago Doctoral Scholarship; University of Otago Publication Bursary; University of Otago establishment grant
- Copyright © The Author(s) 2026. This work was first published in Finance Research Letters (Elsevier). This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://www.creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, provided that the original work is properly attributed to the creator(s) and the source, a link to the Creative Commons license is provided, and any changes made are indicated.
- English
- Journal article