Title: | Investment diversification: A study on emerging stock markets |
Author(s): | Hoang Minh Tri |
Advisor(s): | Prof. Dr. Su Dinh Thanh |
Keywords: | Emerging and frontier markets; Mean-variance optimization model; Multivariate time series forecasts; Portfolio risk optimization; Volatility forecasting |
Abstract: | This dissertation aims to contribute to the literature on portfolio diversification by investigating the diversification potentials of understudied regional stock markets, which comprise mostly emerging and frontier markets, using risk-based portfolio strategies. It first examines whether investors benefit from utilizing a risk-minimization strategy for international diversification in ASEAN stock markets, in a comparison with a benchmark, and if those perks have altered between pre and post-2008 financial crisis (period 1) and pre and during the Covid-19 pandemic (period 2). Utilizing the MSCI ASEAN index as a benchmark, this study demonstrates that the intra-regional advantages of diversification for investors in the ASEAN-6 markets have evolved. Also, the minimum variance portfolio outperforms the benchmark portfolio in periods 1 and 2. This paper suggests that an investor requires a far more concentrated portfolio and, thus, a greater degree of risk-taking than during period 2. If this study's findings are any indications, it has become more challenging for an investor to realize the benefits of diversification within ASEAN-6 economies. The second research objective is to investigate how optimum portfolio holdings for emerging markets in four areas (Asia, Europe, and Commonwealth of Independent States (Eastern + Central), Africa, and Latin America and the Caribbean) differ from the reference weights when using both the meanvariance framework and the popular portfolio risk-optimization. Portfolio weights are computed using historical variance (HV), global minimum variance (GMV), ȝ-fixed minimum variance (MV), and market timing (MT). Findings show that only the MV portfolio beats the MT portfolio whose returns are best in terms of stableness and positivity in 2019 and beyond. Volatility estimates using multivariate time series models can boost portfolio returns using quantitative investment strategies if the volatility modeling and portfolio strategy are in balance. Given the effective MT portfolio, this study has implications for risk-return researchers and fund managers. |
Issue Date: | 2024 |
Publisher: | Đại học Kinh tế Thành phố Hồ Chí Minh |
URI: | https://opac.ueh.edu.vn/record=b1036418~S4 https://digital.lib.ueh.edu.vn/handle/UEH/70567 |
Appears in Collections: | DISSERTATIONS
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