20 research outputs found
Corrigendum to "Weak Approximations for Wiener Functionals" [Ann. Appl. Probab. (2013), 23, 4, 1660-1691
The proofs of Theorem 3.1 and Corollary 4.1 in Le\~ao and Ohashi (2013) are
incomplete. The reason is a wrong statement in Remark 2.2. The hypotheses and
statements of Theorem 3.1 and Corollary 4.1 in Le\~ao and Ohashi (2013) remain
unchanged but the proofs have to be modified. In this short note, we provide
the details.Comment: Errata of the paper Weak approximations for Wiener functional
Discrete-type approximations for non-Markovian optimal stopping problems: Part I
In this paper, we present a discrete-type approximation scheme to solve
continuous-time optimal stopping problems based on fully non-Markovian
continuous processes adapted to the Brownian motion filtration. The
approximations satisfy suitable variational inequalities which allow us to
construct -optimal stopping times and optimal values in full
generality. Explicit rates of convergence are presented for optimal values
based on reward functionals of path-dependent SDEs driven by fractional
Brownian motion. In particular, the methodology allows us to design concrete
Monte-Carlo schemes for non-Markovian optimal stopping time problems as
demonstrated in the companion paper by Bezerra, Ohashi and Russo.Comment: Final version to appear in Journal of Applied Probabilit
Stochastic Near-Optimal Controls for Path-Dependent Systems
In this article, we present a general methodology for control problems driven
by the Brownian motion filtration including non-Markovian and
non-semimartingale state processes controlled by mutually singular measures.
The main result of this paper is the development of a concrete pathwise method
for characterizing and computing near-optimal controls for abstract controlled
Wiener functionals. The theory does not require ad hoc functional
differentiability assumptions on the value process and elipticity conditions on
the diffusion components. The analysis is pathwise over suitable finite
dimensional spaces and it is based on the weak differential structure
introduced by Le\~ao, Ohashi and Simas jointly with measurable selection
arguments. The theory is applied to stochastic control problems based on
path-dependent SDEs where both drift and possibly degenerated diffusion
components are controlled. Optimal control of drifts for path-dependent SDEs
driven by fractional Brownian motion is also discussed. We finally provide an
application in the context of financial mathematics. Namely, we construct
near-optimal controls in a non-Markovian portfolio optimization problem.Comment: We shorten some of the proofs, the Introduction was updated and a
concrete example to Mathematical Finance is presente
A general Multidimensional Monte Carlo Approach for Dynamic Hedging under stochastic volatility
In this work, we introduce a Monte Carlo method for the dynamic hedging of
general European-type contingent claims in a multidimensional Brownian
arbitrage-free market. Based on bounded variation martingale approximations for
Galtchouk-Kunita-Watanabe decompositions, we propose a feasible and
constructive methodology which allows us to compute pure hedging strategies
w.r.t arbitrary square-integrable claims in incomplete markets. In particular,
the methodology can be applied to quadratic hedging-type strategies for fully
path-dependent options with stochastic volatility and discontinuous payoffs. We
illustrate the method with numerical examples based on generalized
Follmer-Schweizer decompositions, locally-risk minimizing and mean-variance
hedging strategies for vanilla and path-dependent options written on local
volatility and stochastic volatility models.Comment: Some typos are corrected in Section
A weak version of path-dependent functional It\^o calculus
We introduce a variational theory for processes adapted to the
multi-dimensional Brownian motion filtration that provides a differential
structure allowing to describe infinitesimal evolution of Wiener functionals at
very small scales. The main novel idea is to compute the "sensitivities" of
processes, namely derivatives of martingale components and a weak notion of
infinitesimal generators, via a finite-dimensional approximation procedure
based on controlled inter-arrival times and approximating martingales. The
theory comes with convergence results that allow to interpret a large class of
Wiener functionals beyond semimartingales as limiting objects of differential
forms which can be computed path wisely over finite-dimensional spaces. The
theory reveals that solutions of BSDEs are minimizers of energy functionals
w.r.t Brownian motion driving noise.Comment: Version to appear in Annals of Probabilit
Weak differentiability of Wiener functionals and occupation times
In this paper, we establish a universal variational characterization of the
non-martingale components associated with weakly differentiable Wiener
functionals in the sense of Le\~ao, Ohashi and Simas. It is shown that any
Dirichlet process (in particular semimartingales) is a differential form w.r.t
Brownian motion driving noise. The drift components are characterized in terms
of limits of integral functionals of horizontal-type perturbations and
first-order variation driven by a two-parameter occupation time process.
Applications to a class of path-dependent rough transformations of Brownian
paths under finite -variation () regularity is also discussed. Under
stronger regularity conditions in the sense of finite -variation, the
connection between weak differentiability and two-parameter local time
integrals in the sense of Young is established.Comment: Revised version. To appear in Bulletin des Sciences Math\'ematiques.
arXiv admin note: text overlap with arXiv:1707.04972, arXiv:1408.142
Weak Functional It\^o Calculus and Applications
We introduce a variational theory for processes adapted to the
multi-dimensional Brownian motion filtration. The theory provides a
differential structure which describes the infinitesimal evolution of Wiener
functionals at very small scales. The Markov property is replaced by a
finite-dimensional approximation procedure based on controlled inter-arrival
times and jumps of approximating martingales. The theory reveals that a large
class of adapted processes follow a differential rule which is similar in
nature to a fundamental theorem of calculus in the context of Wiener
functionals. Null stochastic derivative term turns out to be a non-Markovian
version of the classical second order parabolic operator and connections with
theory of local-times and -variation regularity are established. The
framework extends the pathwise functional calculus and it opens the way to
obtain variational principles for processes. Applications to semi-linear
variational equations and stochastic variational inequalities are presented. In
particular, we provide a feasible dynamic programming principle for fully
non-Markovian/non-semimartingale optimal stopping problems.Comment: The results of the article were reformulated and split into two new
paper
On the Discrete Cram\'er-von Mises Statistics under Random Censorship
In this work, nonparametric log-rank-type statistical tests are introduced in
order to verify homogeneity of purely discrete variables subject to arbitrary
right-censoring for infinitely many categories. In particular, the Cram\'er-von
Mises test statistics for discrete models under censoring is established. In
order to introduce the test, we develop the weighted log-rank statistics in a
general multivariate discrete setup which complements previous fundamental
results of Gill (1980) and Andersen et al. (1982). Due to the presence of
persistent jumps over the unbounded set of categories, the asymptotic
distribution of the test is not distribution-free. The statistical test for a
large class of weighted processes is described as a weighted series of
independent chi-squared variables whose weights can be consistently estimated
and the associated limiting covariance operator can be infinite-dimensional.
The test is consistent to any alternative hypothesis and, in particular, it
allows us to deal with crossing hazard functions. We also provide a simulation
study in order to illustrate the theoretical results.Comment: 50 page
Estimation of Distribution Algorithm for Protein Structure Prediction
Proteins are essential for maintaining life. For example, knowing the
structure of a protein, cell regulatory mechanisms of organisms can be modeled,
supporting the development of disease treatments or the understanding of
relationships between protein structures and food attributes. However,
discovering the structure of a protein can be a difficult and expensive task,
since it is hard to explore the large search to predict even a small protein.
Template-based methods (coarse-grained, homology, threading etc) depend on
Prior Knowledge (PK) of proteins determined using other methods as X-Ray
Crystallography or Nuclear Magnetic Resonance. On the other hand, template-free
methods (full-atom and ab initio) rely on atoms physical-chemical properties to
predict protein structures. In comparison with other approaches, the Estimation
of Distribution Algorithms (EDAs) can require significant less PK, suggesting
that it could be adequate for proteins of low-level of PK. Finding an EDA able
to handle both prediction quality and computational time is a difficult task,
since they are strong inversely correlated. We developed an EDA specific for
the ab initio Protein Structure Prediction (PSP) problem using full-atom
representation. We developed one univariate and two bivariate probabilistic
models in order to design a proper EDA for PSP. The bivariate models make
relationships between dihedral angles and within an amino acid.
Furthermore, we compared the proposed EDA with other approaches from the
literature. We noticed that even a relatively simple algorithm such as Random
Walk can find the correct solution, but it would require a large amount of
prior knowledge (biased prediction). On the other hand, our EDA was able to
correctly predict with no prior knowledge at all, characterizing such a
prediction as pure ab initio.Comment: 45 pages, 13 figure
Data Analysis for Proficiency Testing
Proficiency Testing (PT) determines the performance of individual
laboratories for specific tests or measurements and it is used to monitor the
reliability of laboratories measurements. PT plays a highly valuable role as it
provides an objective evidence of the competence of the participant
laboratories. In this paper, we propose a multivariate model to assess
equivalence among laboratories measurements in proficiency testing. Our method
allow to include type B source of variation and to deal with multivariate data,
where the item under test is measured at different levels. Although intuitive,
the proposed model is nonergodic, which means that the asymptotic Fisher
information matrix is random. As a consequence, a detailed asymptotic analysis
was carried out to establish the strategy for comparing the results of the
participating laboratories. To illustrate, we apply our method to analyze the
data from the Brazilian Engine test group, PT program, where the power of an
engine was measured by 8 laboratories at several levels of rotation