Publicación: Forecasting realized densities: A comparison of historical, risk-neutral, risk-adjusted and sentiment-based transformations (Resumen)
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2022
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Atribución-NoComercial-SinDerivadas 4.0 Internacional
info:eu-repo/semantics/closedAccess
info:eu-repo/semantics/closedAccess
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Universidad Nacional de Educación a Distancia (España). Escuela Internacional de Doctorado. Programa de Doctorado en Ciencias
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This thesis deals with the mathematical models used to forecast future asset prices. Estimating asset prices is arguably one of the most relevant problems for risk managers, central bankers, and investors. Traditional statistical methods rely on point estimates or confidence intervals to estimate future realizations. However, when it comes to analyzing asset prices at a future date, obtaining the full price distribution significantly improves the information available for decision-making. This is particularly relevant in financial prices, which typically exhibit asymmetries, fat tails and other non-normal features. Consequently, estimation methods relying on mean-variance approximations do not appropriately reproduce the real-world characteristics of financial asset prices, leading to biased predictions and inappropriate model choices.
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Facultad de Ciencias
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Grupo de innovación
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Programa de doctorado en ciencias