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Uncertainty in the Black-Litterman model : a practical note

Fuhrer, Adrian; Hock, Thorsten - 2019 - Version: July 15, 2019

Deriving an optimal asset allocation for institutional investors hinges crucially on the quality of inputs used in the optimization. If the mean vector and the covariance matrix are known with certainty, the classical mean-variance optimization of Markowitz (1952) produces optimal portfolios....

Persistent link: https://www.econbiz.de/10012042184

Have risk premia vanished?

Smith, Simon C.; Timmermann, Allan - 2021

Persistent link: https://www.econbiz.de/10012508216

Asset pricing model uncertainty and portfolio choice

Carrasco, Ignacio; Hansen, Erwin - In: Finance research letters 45 (2022), pp. 1-7

Persistent link: https://www.econbiz.de/10014576820

Dealing with drift uncertainty : a Bayesian learning approach

De Franco, Carmine; Nicolle, Johann; Pham, Huyên - In: Risks : open access journal 7 (2019) 1/5, pp. 1-18

One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated with a certain...

Persistent link: https://www.econbiz.de/10012018698

Bayesian risk forecasting for long horizons

Borowska, Agnieszka; Hoogerheide, Lennart; Koopman, Siem Jan - 2019

We present an accurate and efficient method for Bayesian forecasting of two financial risk measures, Value-at-Risk and …-year-ahead. The latter has recently attracted considerable attention due to the different properties of short term risk and long run … risk. The key insight behind our importance sampling based approach is the sequential construction of marginal and …

Persistent link: https://www.econbiz.de/10011979983