Goodness of fit assessment for a fractal model of stock markets

An assessment of the goodness of fit of a new stochastic model of stock dynamics is investigated. The model is the multifractional Brownian motion (mBm), introduced independently by Péltier and Lévy Véhel (1995) [2] and Benassi (1997) [3]. The analysis concerns the (un)conditional distributions of log-variations of the Dow Jones Industrial Average (DJIA). By comparing the performance of mBm with respect to a Garch (1,1), we argue that the former captures the distributional features as well as the pathwise empirical ones displayed by the U.S. Dow Jones index, while the Garch (1,1) works better in global terms.     

Modelling NASDAQ Series by Sparse Multifractional Brownian Motion

The objective of this paper is to compare the performance of different estimators of Hurst index for multifractional Brownian motion (mBm), namely, Generalized Quadratic Variation (GQV) Estimator, Wavelet Estimator and Linear Regression GQV Estimator. Both estimators are used in the real financial dataset Nasdaq time series from 1971 to the 3rd quarter of 2009. Firstly, we review definitions, properties and statistical studies of fractional Brownian motion (fBm) and mBm. Secondly, a numerical artifact is observed: when we estimate the time varying Hurst index H(t) for an mBm, sampling fluctuation gives the impression that H(t) is itself a stochastic process, even when H(t) is constant. To avoid this artifact, we introduce sparse modelling for mBm and apply it to Nasdaq time series.     

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This paper considers the asymptotics of randomly weighted sums and their maxima, where the increments {X_i,i\geq1\} is a sequence of independent, identically distributed and real-valued random variables and the weights {\theta_i,i\geq1\} form another sequence of non-negative and independent random variables, and the two sequences of random variables follow some dependence structures. When the common distribution F of the increments belongs to dominant variation class, we obtain some weakly asymptotic estimations for the tail probability of randomly weighted sums and their maxima. In particular, when the F belongs to consistent variation class, some asymptotic formulas is presented. Finally, these results are applied to the asymptotic estimation for the ruin probability.     

Convex concentration for some additive functionals of jump stochastic differential equations

Using forward-backward stochastic calculus, we prove convex concentration inequalities for some additive functionals of the solution of stochastic differential equations with jumps admitting an invariant probability measure. As a consequence, transportation-information inequalities are obtained and bounds on option prices for interest rate derivatives are given as an application.     

Infinite-Dimensional Quadrature and Approximation of Distributions

We study numerical integration of Lipschitz functionals on a Banach space by means of deterministic and randomized (Monte Carlo) algorithms. This quadrature problem is shown to be closely related to the problem of quantization and to the average Kolmogorov widths of the underlying probability measure. In addition to the general setting, we analyze, in particular, integration with respect to Gaussian measures and distributions of diffusion processes. We derive lower bounds for the worst case error of every algorithm in terms of its cost, and we present matching upper bounds, up to logarithms, and corresponding almost optimal algorithms. As auxiliary results, we determine the asymptotic behavior of quantization numbers and Kolmogorov widths for diffusion processes.      

White noise-based stochastic calculus with respect to multifractional Brownian motion

Stochastic calculus with respect to fractional Brownian motion (fBm) has attracted a lot of interest in recent years, motivated in particular by applications in finance and Internet traffic modeling. Multifractional Brownian motion (mBm) is an extension of fBm enabling to control the local regularity of the process. It is obtained by replacing the constant Hurst parameter H of fBm by a function h(t), thus allowing for a finer modelling of various phenomena.

In this work we extend to mBm the construction of the Wick–Itô stochastic integral with respect to fBm, as originally proposed in Bender (Stoch. Process. Appl. 104 (2003), pp. 81–106), Bender (Bernouilli 9(6) (2003), pp. 955–983), Biagini et al. (Proceedings of Royal Society, special issue on stochastic analysis and applications, 2004, pp. 347–372) and Elliott and Van der Hoek (Math. Finance 13(2) (2003), pp. 301–330). In that view, a multifractional white noise is defined and used to integrate with respect to mBm a large class of stochastic processes using Wick products. Itô formulas (both for tempered distributions and for functions with sub-exponential growth) are obtained, as well as a Tanaka Formula.     

Asymptotic properties of LSE of regression coefficients on singular random fields observed on a sphere

We present some upper bounds on the rate of convergence in the central limit theorem for normalized least square estimates (LSE) in a spherical regression model with long range dependence (LRD) stationary errors. The used method is based on the asymptotic analysis of orthogonal expansion of non-linear functionals of homogeneous isotropic Gaussian random fields and on the Kolmogorov distance. The theory have many applications in science for instance in evaluating the COBE data.     

Small Deviations for Some Multi-Parameter Gaussian Processes

We prove some general lower bounds for the probability that a multi-parameter Gaussian process has very small values. These results, when applied to a certain class of fractional Brownian sheets, yield the exact rate for their so-called small ball probability. We show by example how to use such results to compute the Hausdorff dimension of some exceptional sets determined by maximal increments.     

On the inefficiency of state-independent importance sampling in the presence of heavy tails

This paper proves that there does not exist an asymptotically optimal state-independent change-of-measure for estimating the probability that a random walk with heavy-tailed increments exceeds a “high” threshold before going below zero. Explicit bounds are given on the best asymptotic variance reduction that can be achieved by state-independent schemes.     

Statistical tests of heterogeneity for anisotropic multifractional Brownian fields

In this paper, we deal with some anisotropic extensions of the multifractional Brownian fields that account for spatial phenomena whose properties of regularity and directionality may both vary in space. Our aim is to set statistical tests to decide whether an observed field of this kind is heterogeneous or not. The statistical methodology relies upon a field analysis by quadratic variations, which are averages of square field increments. Specific to our approach, these variations are computed locally in several directions. We establish an asymptotic result showing a linear Gaussian relationship between these variations and parameters related to regularity and directional properties of the model. Using this result, we then design a test procedure based on Fisher statistics of linear Gaussian models. Eventually we evaluate this procedure on simulated data.     

Asymptotic results for exponential functionals of Lévy processes

The asymptotic behavior of expectations of some exponential functionals of a Lévy process is studied. The key point is the observation that the asymptotics only depend on the sample paths with slowly decreasing local infimum. We give not only the convergence rate but also the expression of the limiting coefficient. The latter is given in terms of some transformations of the Lévy process based on its renewal function. As an application, we give an exact evaluation of the decay rate of the survival probability of a continuous-state branching process in random environment with stable branching mechanism.     

Growth of power-free languages: Numerical and asymptotic bounds

We consider power-free languages over finite alphabets. That is, the words which are powers with the exponent greater than a given constant cannot be factors of words in these languages. We give numerical bounds of the exponential growth rate for a wide range of such languages. These bounds are obtained using the algorithms proposed by the author. On the basis of these bounds we suggest the asymptotic behaviour of the growth rate as a function of the exponent and the size of the alphabet, and give some theorems about this behaviour.     

Error bounds for asymptotic approximations of the partition function

We consider Markovian queueing models with a finite number of states and a product form solution for its steady state probability distribution. Starting from the integral representation for the partition function in complex space we construct error bounds for its asymptotic expansion obtained by the saddle point method. The derivation of error bounds is based on an idea by Olver applicable to integral transforms with an exponentially decaying kernel. The bounds are expressed in terms of the supremum of a certain function and are asymptotic to the absolute value of the first neglected term in the expansion as the large parameter approaches infinity. The application of these error bounds is illustrated for two classes of queueing models: loss systems and single chain closed queueing networks.     

Tail asymptotic for discounted aggregate claims with one-sided linear dependence and general investment return

In this study, we investigate the tail probability of the discounted aggregate claim sizes in a dependent risk model. In this model, the claim sizes are observed to follow a one-sided linear process with independent and identically distributed innovations. Investment return is described as a general stochastic process with c`adl`ag paths. In the case of heavy-tailed innovation distributions, we are able to derive some asymptotic estimates for tail probability and to provide some asymptotic upper bounds to improve the applicability of our study.     

Functionals with sign-definite derivative for the stabilization of delay systems

We present new conditions for the uniform asymptotic stability of equilibria in delay systems. These conditions are based on Lyapunov functionals that have negative definite derivatives along the trajectories of the system only on some part of the phase space.By using these conditions, we establish new optimal stabilization tests, which admit the use of a performance functional whose weight functional is not negative definite on the entire phase space.We introduce a new notion of smallness for perturbations in delay systems and present stabilization tests by the first approximation.     

The vibrating string forced by white noise

Summary The equation of the vibrating string forced by white noise is formally solved, using stochastic integrals with respect to a plane Brownian motion, and it is proved that a certain process associated to the energy is a martingale. Then Doob's martingale inequality is used to furnish some probability bounds for the energy.Such bounds provide a solution for the double barrier problem for the class of Gaussian stationary processes which can be represented as linear functionals of the positions and the velocities of the string.     

On Upper Bounds for Minimum Distance and Covering Radius of Non-binary Codes

We consider upper bounds on two fundamental parameters of a code; minimum distance and covering radius. New upper bounds on the covering radius of non-binary linear codes are derived by generalizing a method due to S. Litsyn and A. Tietäväinen lt:newu and combining it with a new upper bound on the asymptotic information rate of non-binary codes. The upper bound on the information rate is an application of a shortening method of a code and is an analogue of the Shannon-Gallager-Berlekamp straight line bound on error probability. These results improve on the best presently known asymptotic upper bounds on minimum distance and covering radius of non-binary codes in certain intervals.     

Limit behavior of two-time-scale diffusions revisited

This work is concerned with diffusions with two-time scales or singularly perturbed diffusions. Asymptotic expansions of the solution of the associated Cauchy problem for parabolic partial differential equation are obtained and the desired error bounds are derived. These asymptotic expansions are then used to analyze related limit distributions of normalized integral functionals.     

A note on approximations of traces of products of truncated Toeplitz matrices

The paper establishes error orders for integral limit approximations to the traces of products of truncated Toeplitz matrices generated by integrable real symmetric functions defined on the unit circle. These approximations and the corresponding error bounds are of importance in the statistical analysis of discrete-time stationary processes (asymptotic distributions and large deviations of Toeplitz type quadratic forms, estimation of the spectral parameters and functionals, asymptotic expansions of the estimators, etc.). The results improve the rates obtained by the authors in an earlier paper.     

Complete asymptotic expansions for lower (upper) continuous random walks with two boundaries. I

The work is devoted to the investigation of some asymptotic properties of probability distributions generated by semicontinuous random walks. Complete asymptotic expansions of the distributions of the boundary functionals connected with two boundaries (a walk in a strip) are obtained.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova Akad. Nauk SSSR, Vol. 55, pp. 64–101, 1976.