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The relaxed investor with partial information

Bäuerle, Nicole ORCID iD icon 1; Urban, S. 1; Veraart, L. A. M.
1 Institut für Stochastik (STOCH), Karlsruher Institut für Technologie (KIT)

Abstract:

We consider an investor in a financial market consisting of a riskless bond and several risky assets. The price processes of the risky assets are geometric Brownian motions where either the drifts are modelled as random variables assuming a constant volatility matrix or the volatility matrix is considered random and drifts are assumed to be constant. The investor is only able to observe the asset prices but not all the model parameters and hence information is only partial. A Bayesian approach is used with known prior distributions for the random model parameters.
We assume that the investor can only trade at discrete time points which are multiples of h > 0 and investigate the loss in expected utility of terminal wealth which is due to the fact that the investor cannot trade and observe continuously. It turns out that in general a discretization gap appears, i.e., for h ! 0 the expected utility of the h-investor does not converge to the expected utility of the continuous investor. This is in contrast to results under full information in (Rogers, L.C.G. 2001. The relaxed investor and parameter uncertainty. Finance and Stochastics, 5(2), 131-154).
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Volltext §
DOI: 10.5445/IR/1000032855
Originalveröffentlichung
DOI: 10.1137/100813646
Scopus
Zitationen: 8
Dimensions
Zitationen: 13
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Stochastik (STOCH)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2012
Sprache Englisch
Identifikator ISSN: 1945-497X
urn:nbn:de:swb:90-328559
KITopen-ID: 1000032855
Erschienen in SIAM Journal of Financial Mathematics
Verlag Society for Industrial and Applied Mathematics (SIAM)
Band 3
Seiten 304-327
Schlagwörter Optimal investment, partial information, Bayesian approach, Markov, decision problem, discrete versus continuous trading, discretization gap
Nachgewiesen in Dimensions
Scopus
Web of Science
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