Search for dissertations about: "portfolio theory thesis"
Showing result 1 - 5 of 75 swedish dissertations containing the words portfolio theory thesis.
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1. Statistical Methods in Portfolio Theory
Abstract : In this thesis we develop new statistical theory and apply it to practical problems dealing with mean-variance optimal portfolio selection. More precisely, we derive an exact statistical test for the characterization of the location of the tangency portfolio (TP) on the efficient frontier. READ MORE
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2. Statistical Inference of Tangency Portfolio in Small and Large Dimension
Abstract : This thesis considers statistical test theory in portfolio theory. It analyses the asymptotic behavior of the considered tests in the high-dimensional setting, meaning k/n → c ∈ (0, ∞) as n → ∞, where k and n are portfolio size and sample size, respectively. READ MORE
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3. The Black-Litterman Model : Towards its use in practice
Abstract : The Black-Litterman model is analyzed in three steps seeking to investigate, develop and test the B-L model in an applied perspective. The first step mathematically derives the Black-Litterman model from a sampling theory approach generating a new interpretation of the model and an interpretable formula for the parameter weight-on-views. READ MORE
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4. The Black-Litterman Model : mathematical and behavioral finance approaches towards its use in practice
Abstract : The financial portfolio model often referred to as the Black-Litterman model is analyzed using two approaches; a mathematical and a behavioral finance approach. After a detailed description of its framework, the Black-Litterman model is derived mathematically using a sampling theoretical approach. READ MORE
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5. Modeling the covariance matrix of financial asset returns
Abstract : The covariance matrix of asset returns, which describes the fluctuation of asset prices, plays a crucial role in understanding and predicting financial markets and economic systems. In recent years, the concept of realized covariance measures has become a popular way to accurately estimate return covariance matrices using high-frequency data. READ MORE