| Summary: | 碩士 === 國立交通大學 === 經營管理研究所 === 102 === In modern portfolio theory (MPT), investors use minimum portfolio variance strategy to allocate their assets and optimize their portfolios, but MPT assumes portfolio variance never changes and uses the historical parameter “volatility” as a proxy for risk. We use range-based dynamic conditional correlation (DCC) and choose the coherent risk measure, Conditional Value-at-Risk (CVaR), as a portfolio risk management tool. We collected Standard &; Poor’s 500 Composite Index (S&;P 500) futures, 10-year U.S. Treasury bond (10-year T-bond) futures as our sample data. In our empirical study, we found that range-based DCC performance is superior to another two models, which are used as model comparison, in in-sample and out-of-sample comparison, and it can help investors construct optimal portfolio with profitable expected return and manageable portfolio risk. The empirical results support our main idea that we can develop promising dynamic investment strategies by using a range-based DCC model in portfolio optimization of conditional value-at-risk framework.
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