6,977 research outputs found
Continuous-time mean-variance efficiency: the 80% rule
This paper studies a continuous-time market where an agent, having specified
an investment horizon and a targeted terminal mean return, seeks to minimize
the variance of the return. The optimal portfolio of such a problem is called
mean-variance efficient \`{a} la Markowitz. It is shown that, when the market
coefficients are deterministic functions of time, a mean-variance efficient
portfolio realizes the (discounted) targeted return on or before the terminal
date with a probability greater than 0.8072. This number is universal
irrespective of the market parameters, the targeted return and the length of
the investment horizon.Comment: Published at http://dx.doi.org/10.1214/105051606000000349 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
Policy Making, Industrial Structure and Economic Growth in a Dual Economy
This paper discusses a new growth mode, a country with a dual economic structure in which each economic sector will receive different government policies such as financial and fiscal policies. Firstly It obtains the economic growth rate and the growth rate of per capita output in the balanced growth path. Secondly it shows how different policy allocations and current industrial structure influence the economic growth. This model also reveals several other factors such as technology progress and population flow which have effect on economic growth. More importantly, two types of "traps" which are often neglected by policymaker are pointed out and given names. They are “Policy Trap” and “Labor-force Flow Trap” which deserve the attentions of policymaker.economic growth Policy Trap Labor-force Flow Trap industrial structure
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
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