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Titolo:
Optimal portfolio in partially observed stochastic volatility models
Autore:
Pham, H; Quenez, MC;
Indirizzi:
Univ Paris 07, Lab Probabil & Modeles Aleatoires, CNRS UMR 7599, F-75251 Paris 05, France Univ Paris 07 Paris France 05 s, CNRS UMR 7599, F-75251 Paris 05, France Univ Marne La Vallee, Equipe Anal & Math Appl, F-77454 Marne La Vallee, France Univ Marne La Vallee Marne La Vallee France F-77454 ne La Vallee, France
Titolo Testata:
ANNALS OF APPLIED PROBABILITY
fascicolo: 1, volume: 11, anno: 2001,
pagine: 210 - 238
SICI:
1050-5164(200102)11:1<210:OPIPOS>2.0.ZU;2-X
Fonte:
ISI
Lingua:
ENG
Soggetto:
INCOMPLETE INFORMATION; UTILITY MAXIMIZATION; CONSUMPTION; POLICIES; INVESTOR; ECONOMY; MARKETS;
Keywords:
stochastic volatility; filtering; utility maximization; dynamic programming; Bayesian control; Kalman-Bucy filter;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
27
Recensione:
Indirizzi per estratti:
Indirizzo: Pham, H Univ Paris 07, Lab Probabil & Modeles Aleatoires, CNRS UMR 7599, Case 7012,2 Pl Jussieu, F-75251 Paris 05, France Univ Paris 07 Case 7012,2 Pl Jussieu Paris France 05 s 05, France
Citazione:
H. Pham e M.C. Quenez, "Optimal portfolio in partially observed stochastic volatility models", ANN APPL PR, 11(1), 2001, pp. 210-238

Abstract

We address the maximization problem of expected utility from terminal wealth. The special feature of this paper is that we consider a financial market where price process of risky assets follows a stochastic volatility modeland we require that investors observe just the vector of stock prices. Using stochastic filtering techniques and adapting martingale duality methods in this partially observed incomplete model, we characterize the value function and the optimal portfolio policies. We study in detail the Bayesian case, when risk premia of the stochastic volatility model are unobservable random variables with known prior distribution. We also consider the case of unobservable risk premia modelled by linear Gaussian processes.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 06/04/20 alle ore 08:34:33