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dc.contributor.author Fan, Melanie
dc.contributor.author Yuan, Kate Xiaoxiao
dc.date.accessioned 2012-04-16T14:26:18Z
dc.date.available 2012-04-16T14:26:18Z
dc.date.issued 2012-04-16
dc.identifier.uri http://hdl.handle.net/10161/5135
dc.description Honors thesis en_US
dc.description.abstract Reliable estimates of volatility and correlation are crucial in asset allocation and risk management. This paper investigates Static, RiskMetrics, and Dynamic Conditional Correlation (DCC) models for estimating volatility and correlation by testing them in an asset allocation context. Optimal allocation weights for one year found using estimates from each model are carried to the subsequent year and the realized Sharpe ratio is computed to assess portfolio performance. We also study cumulative risk-adjusted returns over the entire sample period. Our findings indicate that DCC does not consistently have an advantage over the other two models, although it is optimal in certain scenarios. en_US
dc.language.iso en_US en_US
dc.subject Equities en_US
dc.subject Emerging Markets en_US
dc.subject Asset Allocation en_US
dc.subject Dynamic Correlation en_US
dc.subject Volatility en_US
dc.title Volatility and Correlation Modeling for Sector Allocation in International Equity Markets en_US
dc.department Economics en_US

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