@article {Cardinalejpm.2021.1.212, author = {Mirko Cardinale and Narayan Y. Naik and Varun Sharma}, title = {Forecasting Long-Horizon Volatility for Strategic Asset Allocation}, elocation-id = {jpm.2021.1.212}, year = {2021}, doi = {10.3905/jpm.2021.1.212}, publisher = {Institutional Investor Journals Umbrella}, 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{\textquoteright} model specification does a better job of reducing forecasting errors than does a na{\"\i}ve model based on the simple extrapolation of historical volatility.TOPICS: Portfolio construction, volatility measures, quantitative methods, statistical methods, performance measurementKey Findings▪ This study tests the predictive power of different long-horizon volatility models using a large sample of global equity and bond indexes since 1934.▪ 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 proposed model specification does a better job of reducing forecasting errors than does a na{\"\i}ve model based on the simple extrapolation of historical volatility.}, issn = {0095-4918}, URL = {https://jpm.pm-research.com/content/early/2021/01/28/jpm.2021.1.212}, eprint = {https://jpm.pm-research.com/content/early/2021/01/28/jpm.2021.1.212.full.pdf}, journal = {The Journal of Portfolio Management} }