2001
DOI: 10.1007/s007800000032
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Optimal portfolio management rules in a non-Gaussian market with durability and intertemporal substitution

Abstract: We determine the optimal portfolio management rules for a portfolio selection problem with consumption which incorporates the notions of durability and intertemporal substitution. The logreturns of the uncertain assets are not necessarily normally distributed. The natural models then involve Lévy processes as the driving noise instead of the more frequently used Brownian motion. The optimization problem is a state constrained singular stochastic control problem and the associated Hamilton-Jacobi-Bellman equati… Show more

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Cited by 46 publications
(69 citation statements)
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“…Lemma 6.1 (Sign of RHS of Equation (6.8)) If A n+1 (Q k )V k is given by equation (4.8), with the control parameter determined by 12) then every element of the right hand side of equation (6.8) is nonnegative, that is,…”
Section: Policy Iterationmentioning
confidence: 99%
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“…Lemma 6.1 (Sign of RHS of Equation (6.8)) If A n+1 (Q k )V k is given by equation (4.8), with the control parameter determined by 12) then every element of the right hand side of equation (6.8) is nonnegative, that is,…”
Section: Policy Iterationmentioning
confidence: 99%
“…Examples where such nonlinear PDEs arise include transaction cost/uncertain volatility models [28,4,38], passport options [3,43], unequal borrowing/lending costs [13], large investor effects [2], risk control in reinsurance [32], pricing options and insurance in incomplete markets using an instantaneous Sharpe ratio [51,31,11], and optimal consumption [12,15]. A recent survey article on the theoretical aspects of this topic is given in [35].…”
Section: Introductionmentioning
confidence: 99%
“…The difficulty of dealing with a boundary where no data are assigned has been overcome in [6] by investigating constrained solutions. With respect to equations of the type (1.1) the use of constrained solutions is not necessary.…”
Section: 1mentioning
confidence: 99%
“…Empirical work shows that the normal distribution poorly fits the logreturn data for, e.g., stock prices. Among other things the data show heavier tails than predicted by the normal distribution, and it has in recent years been suggested to model logreturns by generalized hyperbolic distributions (see the references in [6,7,8,10,26,9] for relevant works).…”
Section: Introductionmentioning
confidence: 99%
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