同时有短期和长期债券的公司债券定价

主要考虑公司同时发行短期和长期零息票债券的定价问题.利用随机分析方法,在短期债券到期日,由于偿付短期债务会引起公司资产的跳跃,所以,长期债券的定价需要分两个时间段进行.在结构化模型下给出了公司在短期债券存续期间没有违约的概率,以及在此条件下公司资产的条件概率分布,得到短期债券和长期债券的定价公式.通过数值计算,分析债券价格随各个参数变化的金融意义.     

Unobserved components in ARCH models: An application to seasonal adjustment

The paper deals with unobserved components in ARIMA models with GARCH errors, in the context of an actual application, namely seasonal adjustment of the monthly Spanish money supply series. The series shows clear evidence of (moderate) non-linearity, which does not disappear with simple outlier correction. The GARCH structure explains reasonably well the non-linearity, and this explanation is robust with respect to the GARCH specification. We look at the time variation of the standard error of the adjusted series estimator and show how it can be measured. Next, we look at the implications this variation has on short-term monetary control. The non-linearity seems to have a small effect in practice. It is further seen that the conditional variance of the GARCH process may, in turn, be decomposed into components. In fact, the conditional variance of the money supply series is the sum of a weak linear trend, a strong non-linear seasonal component, and a moderate non-linear irregular component. This information has policy implications: for example, there are periods in the year when policy can be more assertive because information is more precise. Finally, looking at the non-linear components of the money supply it is seen how linear combinations of non-linear series can produce series that behave linearly.     

一种新颖的有制冷直调激光器偏置电路设计

提出了单电源供电的有制冷直调激光器设计方案,解决了APC反馈控制的重复设计问题;在简化电路,降低成本的同时,保证了激光器温度和输出功率的稳定性.     

分支结构电路的可测性设计优化

在硬件设计的初期可以对硬件测试中条件分支结构引起的测试向量冗余问题加以解决.以ALU为例,提出了两种分支结构电路的可测性优化设计,通过调整分支电路的选择条件来控制测试向量的施加,在保证错误覆盖率的同时可以明显减少不必要的测试向量.     

A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility

In a conditional predictive ability test framework, we investigate whether market factors influence the relative conditional predictive ability of realized measures (RMs) and implied volatility (IV), which is able to examine the asynchronism in their forecasting accuracy, and further analyze their unconditional forecasting performance for volatility forecast. Our results show that the asynchronism can be detected significantly and is strongly related to certain market factors, and the comparison between RMs and IV on average forecast performance is more efficient than previous studies. Finally, we use the factors to extend the empirical similarity (ES) approach for combination of forecasts derived from RMs and IV.     

条件独立及其性质

独立是概率论中特有的概念,有着十分重要的意义.本文应用条件概率给出了条件独立的概念,得到了条件独立的相关性质.     

一种基于动作条件互斥的处理动作条件效果的算法

在给出动作无条件互斥、动作半条件互斥和动作全条件互斥等概念的基础上,提出一种新的算法,在规划图的扩张阶段建立各种互斥关系,在规划解的搜索过程中利用这些互斥关系,判断是否需要进行条件冲突检测,从而加快了有效规划的搜索,提高了系统的效率.     

Optimal sampling frequency for volatility forecast models for the Indian stock markets

This paper evaluates the performance of conditional variance models using high‐frequency data of the National Stock Index (S&P CNX NIFTY) and attempts to determine the optimal sampling frequency for the best daily volatility forecast. A linear combination of the realized volatilities calculated at two different frequencies is used as benchmark to evaluate the volatility forecasting ability of the conditional variance models (GARCH (1, 1)) at different sampling frequencies. From the analysis, it is found that sampling at 30 minutes gives the best forecast for daily volatility. The forecasting ability of these models is deteriorated, however, by the non‐normal property of mean adjusted returns, which is an assumption in conditional variance models. Nevertheless, the optimum frequency remained the same even in the case of different models (EGARCH and PARCH) and different error distribution (generalized error distribution, GED) where the error is reduced to a certain extent by incorporating the asymmetric effect on volatility. Our analysis also suggests that GARCH models with GED innovations or EGRACH and PARCH models would give better estimates of volatility with lower forecast error estimates. Copyright © 2008 John Wiley & Sons, Ltd.     

基于条件蒙特卡罗方法的信用违约互换合约定价

多标的资产违约相关性结构的度量及其联合违约时间的模拟是信用违约互换合约定价的关键.Copula函数和蒙特卡罗模拟是解决此关键问题的有力工具,被广泛应用于信用衍生品定价.本文基于因子t-copula模型,结合条件蒙特卡罗模拟,构建了计算第7n次信用违约互换合约的条件蒙特卡罗算法.该算法能够捕捉多标的资产违约的尾部相关性,更准确地度量标的资产组合的违约风险及提高违约事件的模拟效率.数值结果表明,在考虑尾部相关性的情形下,采用重要抽样技术的JK算法和改进的JK算法是不稳定的,不能达到减方差的目的;而本文新构建的定价算法更稳定,在高斯copula和t-copula模型下,都能够有效减小估计量的方差,提高信用违约互换合约的定价精度和可靠性.     

两两NQD列的一个弱大数定律

研究一类广泛的随机变量序列NQD列的收敛性质,获得与独立情形一样的弱大数定律,而且得到了同分布NQD列的相应结果.