77,086 research outputs found
Dynamic robust duality in utility maximization
A celebrated financial application of convex duality theory gives an explicit
relation between the following two quantities:
(i) The optimal terminal wealth of the problem
to maximize the expected -utility of the terminal wealth
generated by admissible portfolios in a market
with the risky asset price process modeled as a semimartingale;
(ii) The optimal scenario of the dual problem to minimize
the expected -value of over a family of equivalent local
martingale measures , where is the convex conjugate function of the
concave function .
In this paper we consider markets modeled by It\^o-L\'evy processes. In the
first part we use the maximum principle in stochastic control theory to extend
the above relation to a \emph{dynamic} relation, valid for all .
We prove in particular that the optimal adjoint process for the primal problem
coincides with the optimal density process, and that the optimal adjoint
process for the dual problem coincides with the optimal wealth process, . In the terminal time case we recover the classical duality
connection above. We get moreover an explicit relation between the optimal
portfolio and the optimal measure . We also obtain that the
existence of an optimal scenario is equivalent to the replicability of a
related -claim.
In the second part we present robust (model uncertainty) versions of the
optimization problems in (i) and (ii), and we prove a similar dynamic relation
between them. In particular, we show how to get from the solution of one of the
problems to the other. We illustrate the results with explicit examples
Performance analysis and optimal selection of large mean-variance portfolios under estimation risk
We study the consistency of sample mean-variance portfolios of arbitrarily
high dimension that are based on Bayesian or shrinkage estimation of the input
parameters as well as weighted sampling. In an asymptotic setting where the
number of assets remains comparable in magnitude to the sample size, we provide
a characterization of the estimation risk by providing deterministic
equivalents of the portfolio out-of-sample performance in terms of the
underlying investment scenario. The previous estimates represent a means of
quantifying the amount of risk underestimation and return overestimation of
improved portfolio constructions beyond standard ones. Well-known for the
latter, if not corrected, these deviations lead to inaccurate and overly
optimistic Sharpe-based investment decisions. Our results are based on recent
contributions in the field of random matrix theory. Along with the asymptotic
analysis, the analytical framework allows us to find bias corrections improving
on the achieved out-of-sample performance of typical portfolio constructions.
Some numerical simulations validate our theoretical findings
Horizon-unbiased Investment with Ambiguity
In the presence of ambiguity on the driving force of market randomness, we
consider the dynamic portfolio choice without any predetermined investment
horizon. The investment criteria is formulated as a robust forward performance
process, reflecting an investor's dynamic preference. We show that the market
risk premium and the utility risk premium jointly determine the investors'
trading direction and the worst-case scenarios of the risky asset's mean return
and volatility. The closed-form formulas for the optimal investment strategies
are given in the special settings of the CRRA preference
- …