On the Quasi Gaussian Interest Rate Models

In this paper some remarks on the interest rate model proposed by Jamishidian (1991) and Ritchken and Sankarasubramanian (1995b) are presented. This revised version was published online in August 2006 with corrections to the Cover Date.     

强非局域非线性介质中高斯光束的传输特性

从Helmholtz方程出发，讨论了高斯光束在1＋1维强非局域非线性介质中的传输特性及其光束柬宽随位置的演化，即当介质的响应函数为高斯型时，用理论分析和数值模拟的方法，讨论了响应函数的特征宽度wm对光束传输的影响．     

FORECASTING VOLATILITY WITH THE MULTIFRACTAL RANDOM WALK MODEL

We study the problem of forecasting volatility for the multifractal random walk model. In order to avoid the ill‐posed problem of estimating the correlation length T of the model, we introduce a limiting object defined in a quotient space; formally, this object is an infinite range log volatility. For this object and the nonlimiting object, we obtain precise prediction formulas and we apply them to the problem of forecasting volatility and pricing options with the MRW model in the absence of a reliable estimate of σ and T.     

基于单因子利率期限结构模型的中国银行间短期利率行为的实证研究

文章采用高斯估计方法,使用中国银行间债券市场国债短期利率数据,对单因子连续时间利率期限结构模型进行了参数估计,实证结果显示我国银行间国债市场的短期利率具有均值恢复特性。和其它模型相比,BS模型在数据拟合方面表现较好。     

不同调制方式下高斯信道容量的计算

计算不同调制方式下的信道容量对实际通信系统的信道编译码设计具有重要的理论意义。首先从Shannon信息论出发,介绍了平稳无记忆连续信道与加性白高斯噪声信道容量的定义。接着利用互信息与微分熵的相互关系,推导了几种常用的不同调制方式下高斯信道的容量,并给出了相应的信道容量闭合计算表达式。通过将电力卫星通信信道模拟为高斯信道,对采用无速率信道编码联合不同调制方式的卫星通信进行了数值仿真。仿真结果显示,通过推导得到的闭合表达式计算信道容量,能对实际卫星通信的编码速率、译码启动条件的确定起到很好的理论指导作用。     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.     

THE TERM STRUCTURE OF INTEREST RATES AS A GAUSSIAN RANDOM FIELD

A simple model of the term structure of interest rates is introduced in which the family of instantaneous forward rates evolves as a continuous Gaussian random field. A necessary and sufficient condition for the associated family of discounted zero-coupon bond prices to be martingales is given, permitting the consistent pricing of interest rate contingent claims. Examples of the pricing of interest-rate caps and the situation when the Gaussian random field may be viewed as a deterministic time change of the standard Brownian sheet are discussed.     

Semiparametric time series models driven by latent factor

We introduce a class of semiparametric time series models (SemiParTS) driven by a latent factor process. The proposed SemiParTS class is flexible because, given the latent process, only the conditional mean and variance of the time series are specified. These are the primary features of SemiParTS: (i) no parametric form is assumed for the conditional distribution of the time series given the latent process; (ii) it is suitable for a wide range of data: non-negative, count, bounded, binary, and real-valued time series; (iii) it does not constrain the dispersion parameter to be known. The quasi-likelihood inference is employed in order to estimate the parameters in the mean function. Here, we derive explicit expressions for the marginal moments and for the autocorrelation function of the time series process so that a method of moments can be employed to estimate the dispersion parameter and also the parameters related to the latent process. Simulated results that aim to check the proposed estimation procedure are presented. Forecasting procedures are proposed and evaluated in simulated and real data. Analyses of the number of admissions in a hospital due to asthma and a total insolation time series illustrate the potential for practical situations that involve the proposed models.     

Influence analysis for Poisson inverse Gaussian regression models based on the EM algorithm

For Poisson inverse Gaussian regression models, it is very complicated to obtain the influence measures based on the traditional method, because the associated likelihood function involves intractable expressions, such as the modified Bessel function. In this paper, the EM algorithm is employed as a basis to derive diagnostic measures for the models by treating them as a mixed Poisson regression with the weights from the inverse Gaussian distributions. Several diagnostic measures are obtained in both case-deletion model and local influence analysis, based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm. Two numerical examples are given to illustrate the results.