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Fridli, S. and Schipp, F. , Strong approximation via Sidon type inequalities (English summary), J. Approx. Theory , 94 (1998), no. 2, 263–284. MR 1637418 ( 99e :41011) Schipp F

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We study the large-time behavior of the charged-polymer Hamiltonian H n of Kantor and Kardar [Bernoulli case] and Derrida, Griffiths, and Higgs [Gaussian case], using strong approximations to Brownian motion. Our results imply, among other things, that in one dimension the process {H [nt]}0≤t≤1 behaves like a Brownian motion, time-changed by the intersection local-time process of an independent Brownian motion. Chung-type LILs are also discussed.

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We prove that the centered three-dimensional Wiener sausage can be strongly approximated by a one-dimensional Brownian motion running at a suitable time clock. The strong approximation gives all possible laws of iterated logarithm as well as the convergence in law in terms of process for the normalized Wiener sausage. The proof relies on Le Gall [10]șs fine L 2-norm estimates between the Wiener sausage and the Brownian intersection local times.

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Leindler, L. , Strong Approximation by Fourier Series , Akadémiai Kiadó (Budapest, 1985). MR 87g :42006 Leindler L

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introduction to the Wiener process and stochastic integrals , Studia Sci. Math. Hung. 31 ( 1996 ), 249 – 297 . MR 96k:60212 [12] S zabados , Т. , Strong

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LetS n be the partial sums of ?-mixing stationary random variables and letf(x) be a real function. In this note we give sufficient conditions under which the logarithmic average off(S n/sn) converges almost surely to ?-8 8 f(x)dF(x). We also obtain strong approximation forH(n)=?k=1 n k -1 f(S k/sk)=logn ?-8 8 f(x)dF(x) which will imply the asymptotic normality ofH(n)/log1/2 n. But for partial sums of i.i.d. random variables our results will be proved under weaker moment condition than assumed for ?-mixing random variables.

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By applying the Skorohod martingale embedding method, a strong approximation theorem for partial sums of asymptotically negatively dependent (AND) Gaussian sequences, under polynomial decay rates, is established. As applications, the law of the iterated logarithm, the Chung-type law of the iterated logarithm and the almost sure central limit theorem for AND Gaussian sequences are derived.

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We present an estimation of the

\documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{upgreek} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage{bbm} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} $$H_{k_0 ,k_r }^q f$$ \end{document}
and H u λφ f means as approximation versions of the Totik type generalization (see [6, 7]) of the result of G. H. Hardy, J. E. Littlewood, considered by N. L. Pachulia in [5]. Some results on the norm approximation will also be given.

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Summary In this paper, embedding results are considered which arise in the strong approximation by Fourier series. We prove several theorems on the interrelation between the classes  W r H β ω  and H (λ,p,r,ω), the latter being defined by L. Leindler. Previous related results in Leindler’s book [2] and paper [5] are particular cases of our results.

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A basic model in financial mathematics was introduced by Black, Scholes and Merton in 1973. A classical discrete approximation in distribution is the binomial model given by Cox, Ross and Rubinstein in 1979. In this work we give a strong (almost sure, pathwise) discrete approximation of the BSM model using a suitable nested sequence of simple, symmetric random walks. The approximation extends to the stock price process, the value process, the replicating portfolio, and the greeks. An important tool in the approximation is a discrete version of the Feynman-Kac formula as well.

Our aim is to show that from an elementary discrete approach, by taking simple limits, one may get the continuous versions. We think that such an approach can be advantageous for both research and applications. Moreover, it is hoped that this approach has pedagogical merits as well: gives insight and seems suitable for teaching students whose mathematical background may not contain e.g. measure theory or stochastic analysis.

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