215 research outputs found
Unified Framework of Mean-Field Formulations for Optimal Multi-period Mean-Variance Portfolio Selection
The classical dynamic programming-based optimal stochastic control methods
fail to cope with nonseparable dynamic optimization problems as the principle
of optimality no longer applies in such situations. Among these notorious
nonseparable problems, the dynamic mean-variance portfolio selection
formulation had posted a great challenge to our research community until
recently. A few solution methods, including the embedding scheme, have been
developed in the last decade to solve the dynamic mean-variance portfolio
selection formulation successfully. We propose in this paper a novel mean-field
framework that offers a more efficient modeling tool and a more accurate
solution scheme in tackling directly the issue of nonseparability and deriving
the optimal policies analytically for the multi-period mean-variance-type
portfolio selection problems
Mean-Variance Policy for Discrete-time Cone Constrained Markets: The Consistency in Efficiency and Minimum-Variance Signed Supermartingale Measure
The discrete-time mean-variance portfolio selection formulation, a
representative of general dynamic mean-risk portfolio selection problems, does
not satisfy time consistency in efficiency (TCIE) in general, i.e., a truncated
pre-committed efficient policy may become inefficient when considering the
corresponding truncated problem, thus stimulating investors' irrational
investment behavior. We investigate analytically effects of portfolio
constraints on time consistency of efficiency for convex cone constrained
markets. More specifically, we derive the semi-analytical expressions for the
pre-committed efficient mean-variance policy and the minimum-variance signed
supermartingale measure (VSSM) and reveal their close relationship. Our
analysis shows that the pre-committed discrete-time efficient mean-variance
policy satisfies TCIE if and only if the conditional expectation of VSSM's
density (with respect to the original probability measure) is nonnegative, or
once the conditional expectation becomes negative, it remains at the same
negative value until the terminal time. Our findings indicate that the property
of time consistency in efficiency only depends on the basic market setting,
including portfolio constraints, and this fact motivates us to establish a
general solution framework in constructing TCIE dynamic portfolio selection
problem formulations by introducing suitable portfolio constraints
OPTIMAL PORTFOLIO CONSTRUCTION BY MIXING HEDGE FUND
The returns of the hedge fund are declining in recent years, accompanying with the impact of the financial crisis in 2008. There will be a question that whether the hedge fund can still be used to blend in a conventional portfolio to improve the performance. Our paper focuses on the comparison analysis and does the basic asset allocation for the hedge fund and traditional portfolio. We analyze the risk-adjusted returns for conventional assets of US Equities, EAFE Equities, US Bonds and International Bonds as well as the hedge fund. Finally we find that, under current market condition, hedge fund is still an ideal alternative asset for the choice of the portfolio to increase the risk-adjusted return level
Decision Making under Cumulative Prospect Theory: An Alternating Direction Method of Multipliers
This paper proposes a novel numerical method for solving the problem of
decision making under cumulative prospect theory (CPT), where the goal is to
maximize utility subject to practical constraints, assuming only finite
realizations of the associated distribution are available. Existing methods for
CPT optimization rely on particular assumptions that may not hold in practice.
To overcome this limitation, we present the first numerical method with a
theoretical guarantee for solving CPT optimization using an alternating
direction method of multipliers (ADMM). One of its subproblems involves
optimization with the CPT utility subject to a chain constraint, which presents
a significant challenge. To address this, we develop two methods for solving
this subproblem. The first method uses dynamic programming, while the second
method is a modified version of the pooling-adjacent-violators algorithm that
incorporates the CPT utility function. Moreover, we prove the theoretical
convergence of our proposed ADMM method and the two subproblem-solving methods.
Finally, we conduct numerical experiments to validate our proposed approach and
demonstrate how CPT's parameters influence investor behavior using real-world
data.Comment: 30 page
Soft Actor-Critic Learning-Based Joint Computing, Pushing, and Caching Framework in MEC Networks
To support future 6G mobile applications, the mobile edge computing (MEC)
network needs to be jointly optimized for computing, pushing, and caching to
reduce transmission load and computation cost. To achieve this, we propose a
framework based on deep reinforcement learning that enables the dynamic
orchestration of these three activities for the MEC network. The framework can
implicitly predict user future requests using deep networks and push or cache
the appropriate content to enhance performance. To address the curse of
dimensionality resulting from considering three activities collectively, we
adopt the soft actor-critic reinforcement learning in continuous space and
design the action quantization and correction specifically to fit the discrete
optimization problem. We conduct simulations in a single-user single-server MEC
network setting and demonstrate that the proposed framework effectively
decreases both transmission load and computing cost under various
configurations of cache size and tolerable service delay
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