几种非线性滤波算法的性能分析

非线性随机动态系统的滤波问题是一类经常遇到的实际应用问题,本文分析了扩展卡尔曼(EKF)、无迹卡尔曼滤波(UKF)和粒子滤波(PF)这三种非线性滤波算法的基本原理和特点以及适应的条件。并通过一个强非线性系统的实验仿真,验证了各自算法的性能。     

Distance Association Models for the Analysis of Repeated Transition Frequency Tables

The present paper is concerned with the analysis of repeated transition frequency tables, for example, occupational mobility data measured in different cohorts. The association present in such a table will be modeled by a distance in Euclidean space. A large distance corresponds to a small association; a small distance corresponds to a large association. A more direct interpretation is that more transitions occur between categories that are close together in a social space. It is assumed that the same social structure (space) exists for the different slices (cohorts/time points) of a table, but that the dimensions of this space are weighted for the different slices, i.e., for each slice the dimensions are stretched or squeezed. We will propose a model, discuss an algorithm to obtain maximum likelihood estimates and apply the model to an empirical data set.     

零膨胀损失次数回归模型及其应用

在许多情况下,损失次数数据一方面具有零膨胀特点,另一方面尾部较长,存在过离散特征。此时,通常的损失次数模型（如泊松回归）将不能很好地拟合实际损失数据。本文对零膨胀泊松回归、零膨胀负二项回归、零膨胀广义泊松回归和零膨胀泊松—逆高斯回归的模型构造和参数估计进行讨论,并用这些模型对一组实际损失数据进行了拟合。结果表明,当实际数据存在零膨胀特点时,零膨胀回归模型可以显著改善对实际损失数据的拟合效果。     

RST–GCBR‐CLUSTERING‐BASED RGA–SVM MODEL FOR CORPORATE FAILURE PREDICTION

Corporate bankruptcy is perceived as a shocking event. Several researchers focused on the prediction of these phenomena using various methods aiming to avoid high generated costs. In this paper, a new hybrid approach is proposed to deal with corporate failure prediction. Based on financial ratios as input data and in order to predict if the business unit will fail or not, our approach integrates rough set theory, Gaussian case‐based reasoning‐clustering, real‐valued genetic algorithm with support vector machines. This combination is justified by a high accuracy rate, reaching 100% at 1 year before failure and 94.0925% at 3 years before failure. Copyright © 2011 John Wiley & Sons, Ltd.     

A heuristic method for parameter selection in LS-SVM: Application to time series prediction

Least Squares Support Vector Machines (LS-SVM) are the state of the art in kernel methods for regression. These models have been successfully applied for time series modelling and prediction. A critical issue for the performance of these models is the choice of the kernel parameters and the hyperparameters which define the function to be minimized. In this paper a heuristic method for setting both the σ parameter of the Gaussian kernel and the regularization hyperparameter based on information extracted from the time series to be modelled is presented and evaluated.     

MultiFactor Valuation of Floating Range Notes

Under a one-factor Gaussian Heath-Jarrow-Morton model, Turnbull (1995) as well as Navatte and Quittard-Pinon (1999) have provided explicit pricing solutions for range notes contracts. The present paper generalizes such closed-form solutions for the context of a multifactor Gaussian HJM framework.     

The smoothing dichotomy in nonparametric regression under long-memory errors

The phenomenon of smoothing dichotomy in random-design nonparametric regression is exposed in nontechnical terms from two recent papers published jointly with Jan Mielniczuk. This concerns the asymptotic distribution of kernel estimators when the errors exhibit long-range dependence, being instantaneous functions either of Gaussian sequences or of infinite-order moving averages, depending on the amount of smoothing.     

VALUATION OF CONTINUOUSLY MONITORED DOUBLE BARRIER OPTIONS AND RELATED SECURITIES

In this paper, we apply Carr's randomization approximation and the operator form of the Wiener‐Hopf method to double barrier options in continuous time. Each step in the resulting backward induction algorithm is solved using a simple iterative procedure that reduces the problem of pricing options with two barriers to pricing a sequence of certain perpetual contingent claims with first‐touch single barrier features. This procedure admits a clear financial interpretation that can be formulated in the language of embedded options. Our approach results in a fast and accurate pricing method that can be used in a rather wide class of Lévy‐driven models including Variance Gamma processes, Normal Inverse Gaussian processes, KoBoL processes, CGMY model, and Kuznetsov's β ‐class. Our method can be applied to double barrier options with arbitrary bounded terminal payoff functions, which, in particular, allows us to price knock‐out double barrier put/call options as well as double‐no‐touch options.     

股票收益率非正态性的蒙特卡罗模拟检验

现实金融数据的分布通常表现为厚尾性和不对称性,因此用正态分布拟合实际金融数据的分布有很大的局限性.文章利用广义双曲线分布的厚尾性和不对称性对1997年1月2日～2003年9月19日的上证综指日收益率分布分别做了正态分布、广义双曲线分布、正态逆高斯分布和双曲线分布的拟合及蒙特卡罗模拟检验,结果表明广义双曲线分布和正态逆高斯分布可以较好地拟合上证综指日收益率分布.另外,文章还建立了一个带噪声干扰的线性系统,对实际的股票收益率并不服从正态分布,而表现出尖峰厚尾的特征做出了一种可能的解释.