7,243 research outputs found
Labour Migrant Adjustments in the Aftermath of the Financial Crisis
Based on individual longitudinal data, we examine the evolution of employment and earnings of post-EU accession Eastern European labour immigrants to Norway for a period of up to eight years after entry. We find that the migrants were particularly vulnerable to the negative labour demand shock generated by the financial crisis. During the winter months of 2008/09, the fraction of immigrant men claiming unemployment insurance benefits rose from below 2 to 14 per cent. Some of this increase turned out to be persistent, and unemployment remained considerably higher among immigrants than natives even three years after the crisis. Although we find that negative labour demand shocks raise the probability of return migration, the majority of the labour migrants directly affected by the downturn stayed in Norway and claimed unemployment insurance benefits
Random many-particle systems: applications from biology, and propagation of chaos in abstract models
The paper discusses a family of Markov processes that represent many particle
systems, and their limiting behaviour when the number of particles go to
infinity. The first part concerns model of biological systems: a model for
sympatric speciation, i.e. the process in which a genetically homogeneous
population is split in two or more different species sharing the same habitat,
and models for swarming animals. The second part of the paper deals with
abstract many particle systems, and methods for rigorously deriving mean field
models.Comment: These are notes from a series of lectures given at the 5
Summer School on Methods and Models of Kinetic Theory, Porto Ercole, 2010.
They are submitted for publication in "Rivista di Matematica della
Universit\`a di Parma
Infinite horizon optimal control of forward-backward stochastic differential equations with delay
We consider a problem of optimal control of an infinite horizon system
governed by forward-backward stochastic differential equations with delay.
Sufficient and necessary maximum principles for optimal control under partial
information in infinite horizon are derived. We illustrate our results by an
application to a problem of optimal consumption with respect to recursive
utility from a cash flow with delay
A Donsker delta functional approach to optimal insider control and applications to finance
We study \emph{optimal insider control problems}, i.e. optimal control
problems of stochastic systems where the controller at any time in addition
to knowledge about the history of the system up to this time, also has
additional information related to a \emph{future} value of the system. Since
this puts the associated controlled systems outside the context of
semimartingales, we apply anticipative white noise analysis, including forward
integration and Hida-Malliavin calculus to study the problem. Combining this
with Donsker delta functionals we transform the insider control problem into a
classical (but parametrised) adapted control system, albeit with a
non-classical performance functional. We establish a sufficient and a necessary
maximum principle for such systems. Then we apply the results to obtain
explicit solutions for some optimal insider portfolio problems in financial
markets described by It\^ o-L\' evy processes. Finally, in the Appendix we give
a brief survey of the concepts and results we need from the theory of white
noise, forward integrals and Hida-Malliavin calculus
Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval
We propose a novel algorithm for the task of supervised discriminative
distance learning by nonlinearly embedding vectors into a low dimensional
Euclidean space. We work in the challenging setting where supervision is with
constraints on similar and dissimilar pairs while training. The proposed method
is derived by an approximate kernelization of a linear Mahalanobis-like
distance metric learning algorithm and can also be seen as a kernel neural
network. The number of model parameters and test time evaluation complexity of
the proposed method are O(dD) where D is the dimensionality of the input
features and d is the dimension of the projection space - this is in contrast
to the usual kernelization methods as, unlike them, the complexity does not
scale linearly with the number of training examples. We propose a stochastic
gradient based learning algorithm which makes the method scalable (w.r.t. the
number of training examples), while being nonlinear. We train the method with
up to half a million training pairs of 4096 dimensional CNN features. We give
empirical comparisons with relevant baselines on seven challenging datasets for
the task of low dimensional semantic category based image retrieval.Comment: ICCV 2015 preprin
Optimal insider control of stochastic partial differential equations
We study the problem of optimal inside control of an SPDE (a stochastic
evolution equation) driven by a Brownian motion and a Poisson random measure.
Our optimal control problem is new in two ways: (i) The controller has access
to inside information, i.e. access to information about a future state of the
system, (ii) The integro-differential operator of the SPDE might depend on the
control.
In the first part of the paper, we formulate a sufficient and a necessary
maximum principle for this type of control problem, in two cases: (1) When the
control is allowed to depend both on time t and on the space variable x. (2)
When the control is not allowed to depend on x.
In the second part of the paper, we apply the results above to the problem of
optimal control of an SDE system when the inside controller has only noisy
observations of the state of the system. Using results from nonlinear
filtering, we transform this noisy observation SDE inside control problem into
a full observation SPDE insider control problem.
The results are illustrated by explicit examples
Optimal insider control and semimartingale decompositions under enlargement of filtration
We combine stochastic control methods, white noise analysis and
Hida-Malliavin calculus applied to the Donsker delta functional to obtain new
representations of semimartingale decompositions under enlargement of
filtrations. The results are illustrated by explicit examples
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
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