Cardinale, M, Naik, N and Sharma, V (2021) Forecasting long-horizon volatility for strategic asset allocation. Journal of Portfolio Management, 47 (4). pp. 83-98. ISSN 0095-4918
Abstract
Long-term volatility is a key forecasting input for strategic asset allocation analysis, yet most studies on volatility models have focused on short horizons. The authors use a large sample of global equity and bond indexes since 1934 to test the predictive power of different long-horizon volatility models. Their findings suggest that the best approach to forecasting long-horizon volatility is to use a long historical window and capture both long-term mean reversion and short-term volatility clustering properties. The results show that the authors’ model specification does a better job of reducing forecasting errors than does a naïve model based on the simple extrapolation of historical volatility.
More Details
Item Type: | Article |
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Subject Areas: | Finance |
Additional Information: |
© 2021 Pageant Media Ltd
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Date Deposited: | 07 Apr 2021 11:22 |
Subjects: |
Portfolio investment Statistical evaluation |
Last Modified: | 12 Aug 2024 00:46 |
URI: | https://lbsresearch.london.edu/id/eprint/1747 |