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Guberti, Davide (2017) Strategic asset allocation in a low interest rate environment. [Magistrali biennali]

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Abstract

This work examines asset allocation problems in the long run, with a focus on how different assumption on the allocation problem can change the resulting optimal portfolios. The starting point is to assume constant expected returns, this specification is then relaxed in order to allow for return predictability. For an investor whose objective is to maximize its terminal wealth with i.i.d. returns, when parameter uncertainty is not accounted for, the allocation toward the risky component of the portfolio is constant with respect to the investment horizon. Conversely, when the investor incorporates parameter uncertainty the allocation is decreasing as the time horizon increases, even in a constant expected returns environment. This confirms that not including parameter uncertainty in the analysis lead to a non negligible over-allocation. These findings are confirmed with different asset classes and are present even when a multiplicity of risky securities are involved in the analysis. Furthermore when the assumption on return predictability is included, optimal allocation toward the risky component of the portfolio become increasing as the investment horizon grows. This work is complemented with various sensitivity analysis, which allows us to conclude that this over/under allocation phenomena is strongly dependent also from other assumption like investment horizon, risk free rate and risk aversion coefficients

Item Type:Magistrali biennali
Corsi di Diploma di Laurea:Scuola di Economia e Scienze Politiche > Economics and Finance
Uncontrolled Keywords:Asset; Allocation; financial; econometrics; portfolio theory
Subjects:Area 13 - Scienze economiche e statistiche > SECS-P/14 Economia
Codice ID:56715
Relatore:Caporin, Massimiliano
Data della tesi:29 August 2017
Biblioteca:Polo di Scienze Sociali > Biblioteca del Dipartimento di Scienze Economiche"Marco Fanno"
Tipo di fruizione per il documento:on-line per i full-text

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