Predictive Decision Synthesis for Portfolios: Betting on Better Models

Autor:	Tallman, Emily, West, Mike
Rok vydání:	2024
Předmět:	Quantitative Finance - Portfolio Management Statistics - Applications Statistics - Methodology 62F15 62C05
Druh dokumentu:	Working Paper
Popis:	We discuss and develop Bayesian dynamic modelling and predictive decision synthesis for portfolio analysis. The context involves model uncertainty with a set of candidate models for financial time series with main foci in sequential learning, forecasting, and recursive decisions for portfolio reinvestments. The foundational perspective of Bayesian predictive decision synthesis (BPDS) defines novel, operational analysis and resulting predictive and decision outcomes. A detailed case study of BPDS in financial forecasting of international exchange rate time series and portfolio rebalancing, with resulting BPDS-based decision outcomes compared to traditional Bayesian analysis, exemplifies and highlights the practical advances achievable under the expanded, subjective Bayesian approach that BPDS defines. Comment: 25 pages, 10 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.01598 Zobrazit plný text záznamu View this record from Arxiv