应用Hermit插值方法修正Dome-A自动气象站记录的雪层温度

随着冰盖表面雪的积累或消融,自动气象站(AWS)上所携带的传感器相对于地表的高度/深度不断变化,给观测数据带来误差.本文利用分段三次Hermit插值的方法对Dome A地区的AWS历史雪层温度数据进行修正.结果表明,雪层的修正量随着雪的积累率的增加而增大,在夏半年为正,在冬半年为负.该方法对于浅层雪温的修正有较好的效果.     

具有分段线性水平截线的双参数Copula函数簇

构造了一类具有分段线性水平截线的双参数Copula函数簇,并讨论了这类Copula函数簇的绝对连续性和极限性质.     

分片泛函多延迟微分方程Runge-Kutta方法的稳定性分析

将Runge-Kutta方法用于求解一类分片泛函多延迟微分方程,研究其数值解的稳定性.给出了其解析解的渐近稳定区域包含在其数值解的渐近稳定区域的充分必要条件.最后,用一些数值算例验证了理论结果.     

一类二阶中立型微分方程的概周期解的新证法

给出了一类有逐段常变量的二阶中立型时滞微分方程的概周期解的存在性和唯一性的一种新证法。     

两类确为初等函数的分段函数

先证明两类分段函数是初等函数，然后举例说明将这两类分段函数转化为用一个解析式表示的方法。     

指数自回归模型的辨识的研究

本文讨论了指数自回归模型的辨识问题，证明了该模型的最小二乘问题的非凸性，并给出其保证凸性的条件，最后运用混合算法，辨识了该模型，并用数值算例加以说明。     

分片代数曲面的绘制

提出了一种基于多边形化的分片代数曲面的绘制方法．首先，计算每一个单元所处的片的序号，接着计算它们和曲面的所有的交点形成一个多边形，然后将这个多边形分解成若干个三角形使得每一个三角形都落在同一片曲面上．在该方法中，每一片被染上不同的颜色以便对它们进行局部的控制．最后给出了一些例子以说明这个方法的有效．     

Autoregressive gamma processes

We introduce a class of autoregressive gamma processes with conditional distributions from the family of noncentred gamma (up to a scale factor). The paper provides the stationarity and ergodicity conditions for ARG processes of any autoregressive order p, including long memory, and closed‐form expressions of conditional moments. The nonlinear state space representation of an ARG process is used to derive the filtering, smoothing and forecasting algorithms. The paper also presents estimation and inference methods, illustrated by an application to interquote durations data on an infrequently traded stock listed on the Toronto Stock Exchange (TSX). Copyright © 2006 John Wiley & Sons, Ltd.     

Bias in the estimation of non‐linear transformations of the integrated variance of returns

Volatility models such as GARCH, although misspecified with respect to the data‐generating process, may well generate volatility forecasts that are unconditionally unbiased. In other words, they generate variance forecasts that, on average, are equal to the integrated variance. However, many applications in finance require a measure of return volatility that is a non‐linear function of the variance of returns, rather than of the variance itself. Even if a volatility model generates forecasts of the integrated variance that are unbiased, non‐linear transformations of these forecasts will be biased estimators of the same non‐linear transformations of the integrated variance because of Jensen's inequality. In this paper, we derive an analytical approximation for the unconditional bias of estimators of non‐linear transformations of the integrated variance. This bias is a function of the volatility of the forecast variance and the volatility of the integrated variance, and depends on the concavity of the non‐linear transformation. In order to estimate the volatility of the unobserved integrated variance, we employ recent results from the realized volatility literature. As an illustration, we estimate the unconditional bias for both in‐sample and out‐of‐sample forecasts of three non‐linear transformations of the integrated standard deviation of returns for three exchange rate return series, where a GARCH(1, 1) model is used to forecast the integrated variance. Our estimation results suggest that, in practice, the bias can be substantial. Copyright © 2006 John Wiley & Sons, Ltd.