Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecasts
Employing both the mean-variance framework and the common portfolio risk-optimization, this study adds to the investment research by examining how ideal holdings for emerging and frontier markets (EFM) of the four global regions (Asian, Europe, and Commonwealth of Independent States (Eastern + Centr...
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| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Cogent Economics & Finance |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23322039.2022.2114163 |
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| author | Tri M. Hoang |
| author_facet | Tri M. Hoang |
| author_sort | Tri M. Hoang |
| collection | DOAJ |
| description | Employing both the mean-variance framework and the common portfolio risk-optimization, this study adds to the investment research by examining how ideal holdings for emerging and frontier markets (EFM) of the four global regions (Asian, Europe, and Commonwealth of Independent States (Eastern + Central), Africa, and Latin America and the Caribbean) differ from the benchmark (MSCI EFM (World) index) weights. MSCI previously stood for Morgan Stanley Capital International. The optimal weights were computed for the MSCI Asia, MSCI Europe, MSCI Africa, MSCI Latin America, and MSCI Caribbean for four unique schemes: historical variance (HV), global-minimum variance (GMV), μ-fixed minimum variance (MV), and market timing (MT). The portfolio study shows that the market timing (MT) portfolio performs well, with only the fixed minimum variance (MV) portfolio outperforming it overall. In terms of steady and positive returns in 2019 and beyond, the MT portfolio emerges as the best. Also, the volatility forecasts generated from multivariate time series models can be successfully converted into higher portfolio returns using quantitative investment approaches if the right balance of volatility modelling and portfolio strategy is determined. Given the well-functioning MT portfolio, this study offers some implications for scholars and funding managers based on the risk-return trade-off. |
| format | Article |
| id | doaj-art-8a8dc17ff8da41779afeaac2ceb8ff4c |
| institution | OA Journals |
| issn | 2332-2039 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Economics & Finance |
| spelling | doaj-art-8a8dc17ff8da41779afeaac2ceb8ff4c2025-08-20T02:34:20ZengTaylor & Francis GroupCogent Economics & Finance2332-20392022-12-0110110.1080/23322039.2022.2114163Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecastsTri M. Hoang0School of Finance, University of Economics Ho Chi Minh City (UEH), Ho Chi Minh City, VietnamEmploying both the mean-variance framework and the common portfolio risk-optimization, this study adds to the investment research by examining how ideal holdings for emerging and frontier markets (EFM) of the four global regions (Asian, Europe, and Commonwealth of Independent States (Eastern + Central), Africa, and Latin America and the Caribbean) differ from the benchmark (MSCI EFM (World) index) weights. MSCI previously stood for Morgan Stanley Capital International. The optimal weights were computed for the MSCI Asia, MSCI Europe, MSCI Africa, MSCI Latin America, and MSCI Caribbean for four unique schemes: historical variance (HV), global-minimum variance (GMV), μ-fixed minimum variance (MV), and market timing (MT). The portfolio study shows that the market timing (MT) portfolio performs well, with only the fixed minimum variance (MV) portfolio outperforming it overall. In terms of steady and positive returns in 2019 and beyond, the MT portfolio emerges as the best. Also, the volatility forecasts generated from multivariate time series models can be successfully converted into higher portfolio returns using quantitative investment approaches if the right balance of volatility modelling and portfolio strategy is determined. Given the well-functioning MT portfolio, this study offers some implications for scholars and funding managers based on the risk-return trade-off.https://www.tandfonline.com/doi/10.1080/23322039.2022.2114163emerging and frontier marketsmean-variance optimisation modelmultivariate time series forecastsportfolio risk-optimisationvolatility forecastingG11 |
| spellingShingle | Tri M. Hoang Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecasts Cogent Economics & Finance emerging and frontier markets mean-variance optimisation model multivariate time series forecasts portfolio risk-optimisation volatility forecasting G11 |
| title | Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecasts |
| title_full | Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecasts |
| title_fullStr | Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecasts |
| title_full_unstemmed | Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecasts |
| title_short | Active portfolio management for the emerging and frontier markets: the use of multivariate time series forecasts |
| title_sort | active portfolio management for the emerging and frontier markets the use of multivariate time series forecasts |
| topic | emerging and frontier markets mean-variance optimisation model multivariate time series forecasts portfolio risk-optimisation volatility forecasting G11 |
| url | https://www.tandfonline.com/doi/10.1080/23322039.2022.2114163 |
| work_keys_str_mv | AT trimhoang activeportfoliomanagementfortheemergingandfrontiermarketstheuseofmultivariatetimeseriesforecasts |