95 research outputs found
Almost Sure Convergence of Solutions to Non-Homogeneous Stochastic Difference Equation
We consider a non-homogeneous nonlinear stochastic difference equation
X_{n+1} = X_n (1 + f(X_n)\xi_{n+1}) + S_n, and its important special case
X_{n+1} = X_n (1 + \xi_{n+1}) + S_n, both with initial value X_0, non-random
decaying free coefficient S_n and independent random variables \xi_n. We
establish results on \as convergence of solutions X_n to zero. The necessary
conditions we find tie together certain moments of the noise \xi_n and the rate
of decay of S_n. To ascertain sharpness of our conditions we discuss some
situations when X_n diverges. We also establish a result concerning the rate of
decay of X_n to zero.Comment: 22 pages; corrected more typos, fixed LaTeX macro
Precautionary Measures for Credit Risk Management in Jump Models
Sustaining efficiency and stability by properly controlling the equity to
asset ratio is one of the most important and difficult challenges in bank
management. Due to unexpected and abrupt decline of asset values, a bank must
closely monitor its net worth as well as market conditions, and one of its
important concerns is when to raise more capital so as not to violate capital
adequacy requirements. In this paper, we model the tradeoff between avoiding
costs of delay and premature capital raising, and solve the corresponding
optimal stopping problem. In order to model defaults in a bank's loan/credit
business portfolios, we represent its net worth by Levy processes, and solve
explicitly for the double exponential jump diffusion process and for a general
spectrally negative Levy process.Comment: 31 pages, 4 figure
On Convergence Properties of Shannon Entropy
Convergence properties of Shannon Entropy are studied. In the differential
setting, it is shown that weak convergence of probability measures, or
convergence in distribution, is not enough for convergence of the associated
differential entropies. A general result for the desired differential entropy
convergence is provided, taking into account both compactly and uncompactly
supported densities. Convergence of differential entropy is also characterized
in terms of the Kullback-Liebler discriminant for densities with fairly general
supports, and it is shown that convergence in variation of probability measures
guarantees such convergence under an appropriate boundedness condition on the
densities involved. Results for the discrete setting are also provided,
allowing for infinitely supported probability measures, by taking advantage of
the equivalence between weak convergence and convergence in variation in this
setting.Comment: Submitted to IEEE Transactions on Information Theor
On finite-difference approximations for normalized Bellman equations
A class of stochastic optimal control problems involving optimal stopping is
considered. Methods of Krylov are adapted to investigate the numerical
solutions of the corresponding normalized Bellman equations and to estimate the
rate of convergence of finite difference approximations for the optimal reward
functions.Comment: 36 pages, ArXiv version updated to the version accepted in Appl.
Math. Opti
Spherical Model in a Random Field
We investigate the properties of the Gibbs states and thermodynamic
observables of the spherical model in a random field. We show that on the
low-temperature critical line the magnetization of the model is not a
self-averaging observable, but it self-averages conditionally. We also show
that an arbitrarily weak homogeneous boundary field dominates over fluctuations
of the random field once the model transits into a ferromagnetic phase. As a
result, a homogeneous boundary field restores the conventional self-averaging
of thermodynamic observables, like the magnetization and the susceptibility. We
also investigate the effective field created at the sites of the lattice by the
random field, and show that at the critical temperature of the spherical model
the effective field undergoes a transition into a phase with long-range
correlations .Comment: 29 page
Soft and hard wall in a stochastic reaction diffusion equation
We consider a stochastically perturbed reaction diffusion equation in a
bounded interval, with boundary conditions imposing the two stable phases at
the endpoints. We investigate the asymptotic behavior of the front separating
the two stable phases, as the intensity of the noise vanishes and the size of
the interval diverges. In particular, we prove that, in a suitable scaling
limit, the front evolves according to a one-dimensional diffusion process with
a non-linear drift accounting for a "soft" repulsion from the boundary. We
finally show how a "hard" repulsion can be obtained by an extra diffusive
scaling.Comment: 33 page
Probabilistic analysis of the upwind scheme for transport
We provide a probabilistic analysis of the upwind scheme for
multi-dimensional transport equations. We associate a Markov chain with the
numerical scheme and then obtain a backward representation formula of
Kolmogorov type for the numerical solution. We then understand that the error
induced by the scheme is governed by the fluctuations of the Markov chain
around the characteristics of the flow. We show, in various situations, that
the fluctuations are of diffusive type. As a by-product, we prove that the
scheme is of order 1/2 for an initial datum in BV and of order 1/2-a, for all
a>0, for a Lipschitz continuous initial datum. Our analysis provides a new
interpretation of the numerical diffusion phenomenon
A simple mean field model for social interactions: dynamics, fluctuations, criticality
We study the dynamics of a spin-flip model with a mean field interaction. The
system is non reversible, spacially inhomogeneous, and it is designed to model
social interactions. We obtain the limiting behavior of the empirical averages
in the limit of infinitely many interacting individuals, and show that phase
transition occurs. Then, after having obtained the dynamics of normal
fluctuations around this limit, we analize long time fluctuations for critical
values of the parameters. We show that random inhomogeneities produce critical
fluctuations at a shorter time scale compared to the homogeneous system.Comment: 37 pages, 2 figure
Almost sure exponential stability of numerical solutions for stochastic delay differential equations
Using techniques based on the continuous and discrete semimartingale convergence theorems, this paper investigates if numerical methods may reproduce the almost sure exponential stability of the exact solutions to stochastic delay differential equations (SDDEs). The important feature of this technique is that it enables us to study the almost sure exponential stability of numerical solutions of SDDEs directly. This is significantly different from most traditional methods by which the almost sure exponential stability is derived from the moment stability by the Chebyshev inequality and the Borel–Cantelli lemma
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