二叉树期权定价模型在人力资源价值计量中的应用

通过研究人力资源所展现的期权特征,引入二叉树期权定价模型对人力资源的价值加以计量,并对其公式进行了简单的推导,之后举例说明推迟期权、增长期权和放弃期权在人力资源各阶段的应用,以期从期权的角度对人力资源进行定量计量.     

校园网IP地址分配方式的使用分析

IP地址分配方式是校园网建设的一个重要组成部分，良好的IP地址分配方式能够有效地控制网络地址冲突，减少日常管理和维护工作负担，提高工作效率。文章提出了在校园网传统IP地址分配方式的基础上，结合目前较先进的技术进行实际应用方面的设计。     

Dynamic density forecasts for multivariate asset returns

We propose a simple and flexible framework for forecasting the joint density of asset returns. The multinormal distribution is augmented with a polynomial in (time‐varying) non‐central co‐moments of assets. We estimate the coefficients of the polynomial via the method of moments for a carefully selected set of co‐moments. In an extensive empirical study, we compare the proposed model with a range of other models widely used in the literature. Employing a recently proposed as well as standard techniques to evaluate multivariate forecasts, we conclude that the augmented joint density provides highly accurate forecasts of the ‘negative tail’ of the joint distribution. Copyright © 2010 John Wiley & Sons, Ltd.     

有限时期障碍期权的三项式期权定价模型

本文研究了有限时期障碍期权的三项式定价模型,对具有三种变化的股票价格模型进行了讨论,推广了CRR二项式定价模型,推出了某一假定证券市场中有限时期障碍期权的三项式期权定价公式。     

股票交易量对证券期权价格的影响

用复合Poisson过程描述股票的交易量,据此构造股票价格的随机过程,进而推导出在风险中立条件下,欧式买入期权的价格公式,并对风险中立条件下及风险回避市场中,期权价格的边界问题进行讨论.     

不确定条件下不连续创新项目期权价值优化模型

为探讨不确定性对不连续创新项目期权价值及投资策略的影响,通过引入泊松跳跃过程模拟项目价值的不连续变化建立了不连续创新项目的期权价值优化模型,将技术、市场、组织与资源不确定性分别表示为项目产业化成功率、项目价值和投资成本的不确定性,然后具体解析了不连续变化的跳跃强度、跳跃幅度、跳跃幅度波动率等不确定性对不连续创新项目最优投资策略的影响以及不确定性与期权价值的关系,最后通过蒙特卡罗模拟检验了模型的有效性.模型将现有研究扩展到投资成本亦为不确定的情形,可为不连续创新项目投资和项目价值与风险评估提供决策参考.     

The utilization of two‐metre Countryside Stewardship Scheme grass margins by the gatekeeper Pyronia tithonus (L)

The utilization of 2‐m Countryside Stewardship Scheme (CSS) grass margins by Pyronia tithonus was investigated during the period 1997–2000 at three farms in Essex, UK. The aims of the research were to investigate whether 2‐m grass margins established using the CSS would provide suitable habitat for P. tithonus. Overall, there was no significant difference between P. tithonus abundance on the 2‐m grass margins and the control sections without margins, but significantly more P. tithonus were observed on the 2‐m grass margins in 2000 than in 1997. There was a strong relationship between P. tithonus abundance and the presence of hedgerows, and significantly more P. tithonus were recorded on 2‐m grass margins next to hedgerows than on 2‐m grass margins established in areas without adjacent hedgerows. It was suggested that P. tithonus would benefit most from habitats with 2‐m grass margins sown with a seed mixture containing a range of fine‐leaved grasses and wildflowers, next to a hedgerow, and managed in accordance with current practices.     

Prediction from the One‐Way Error Components Model with AR(1) Disturbances

In this paper we extend the works of Baillie and Baltagi (1999, in Analysis of Panels and Limited Dependent Variables Models, Hsiao C et al. (eds). Cambridge University Press: Cambridge, UK; 255–267) and generalize certain results from the Baltagi and Li (1992, Journal of Forecasting 11 : 561–567) paper accounting for AR(1) errors in the disturbance term. In particular, we derive six predictors for the one‐way error components model, as well as their associated asymptotic mean squared error of multi‐step prediction in the presence of AR(1) errors in the disturbance term. In addition, we also provide both theoretical and simulation evidence as to the relative efficiency of our alternative predictors. The adequacy of the prediction AMSE formula is also investigated by the use of Monte Carlo methods and indicates that the ordinary optimal predictor performs well for various accuracy criteria. Copyright © 2011 John Wiley & Sons, Ltd.     

Henderson‐Trending of Macroeconomic Variables and Forecasting Accuracy

Using a structural time‐series model, the forecasting accuracy of a wide range of macroeconomic variables is investigated. Specifically of importance is whether the Henderson moving‐average procedure distorts the underlying time‐series properties of the data for forecasting purposes. Given the weight of attention in the literature to the seasonal adjustment process used by various statistical agencies, this study hopes to address the dearth of literature on ‘trending’ procedures. Forecasts using both the trended and untrended series are generated. The forecasts are then made comparable by ‘detrending’ the trended forecasts, and comparing both series to the realised values. Forecasting accuracy is measured by a suite of common methods, and a test of significance of difference is applied to the respective root mean square errors. It is found that the Henderson procedure does not lead to deterioration in forecasting accuracy in Australian macroeconomic variables on most occasions, though the conclusions are very different between the one‐step‐ahead and multi‐step‐ahead forecasts. Copyright © 2011 John Wiley & Sons, Ltd.     

Discrete Euler processes and their applications

This paper introduces discrete Euler processes and shows their application in detecting and forecasting cycles in non‐stationary data where periodic behavior changes approximately linearly in time. A discrete Euler process becomes a classical stationary process if ‘time’ is transformed properly. By moving from one time domain to another, one may deform certain time‐varying data to non‐time‐varying data. With these non‐time‐varying data on the deformed timescale, one may use traditional tools to do parameter estimation and forecasts. The obtained results then can be transformed back to the original timescale. For datasets with an underlying discrete Euler process, the sample M‐spectrum and the spectra estimator of a Euler model (i.e., EAR spectral) are used to detect cycles of a Euler process. Beam response and whale data are used to demonstrate the usefulness of a Euler model. Copyright © 2008 John Wiley & Sons, Ltd.