Anzeige
Erschienen in:
2006 | OriginalPaper | Buchkapitel
Portfolio Optimization Under Partial Information and Convex Constraints in a Hidden Markov Model
verfasst von : Jörn Sass
Erschienen in: Operations Research Proceedings 2005
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In a continuous-time hidden Markov model (HMM) for stock returns we consider an investor who wishes to maximize the expected utility of terminal wealth. As a means to deal with the resulting highly risky strategies we impose convex constraints on the trading strategies covering e.g. short selling restrictions. Based on HMM filtering methods we show how to reformulate this model with partial information as a model with full information. Then results on portfolio optimization under constraints are used to give a verification result. By its application an optimal trading strategy can be computed. Numerical results are provided.