Randomized unit root processes for modelling and forecasting financial time series: Theory and applications

This paper considers the problems of statistically analysing the levels of financial time series rather than their differences, which are often equivalent to returns and which are traditionally analysed in econometric modelling. This focus on differences is a consequence of the inherent nonstationarity of the levels, and hence analysing the latter requires introducing an alternative framework for modelling nonstationary behaviour. We do this by considering randomized unit root processes, arguing that these can have a natural interpretation in the financial context. The paper thus develops methods for testing for randomized unit roots and for modelling such processes. It then applies these techniques to various financial time series, so as to ascertain their potential usefulness, particularly for forecasting.     

Forecasting and analyzing economic activity with coincident and leading indexes: The case of Connecticut

We develop coincident and leading employment indexes for the Connecticut economy. Four employment-related variables enter the coincident index while five employment-related variables enter the leading index. The peaks and troughs in the leading index lead the peaks and troughs in the coincident index by an average of 3 and 9 months. Finally, we use the leading index in vector-autoregressive (VAR) and Bayesian vector-autoregressive (BVAR) models to forecast the coincident index, non-farm employment, and the unemployment rate.     

利用能量原理研究单壁碳纳米管的剪切模量

利用能量方法研究了单壁碳纳米管（SWCNT）的剪切模量．采用分子力学理论得出了受扭矩作用下单壁碳纳米管的总势能;通过总势能与相应的薄壁圆筒的扭转变形能比较,推导出了单壁碳纳米管剪切模量的计算公式;碳纳米管剪切模量的计算结果与现有的研究结果相符,从而证实了本文计算公式正确有效．     

量子全加法器的修改和算法研究

通过与经典全加器的基本模型进行比较后,讨论了一个改进后的量子平面加法器的基本构型.对其原理、组件和算法进行了研究,比较了本加法器两个主要组件与一般量子加法器的不同.作为应用的例,设计了一个n比特量子全加法器的模型,对其具体运算过程和基本功能进行了说明.     

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.     

The x-phi(les): unusual insights into the nature of inquiry

Experimental philosophy is often regarded as a category mistake. Even those who reject that view typically see it as irrelevant to standard philosophical projects. We argue that neither of these claims can be sustained and illustrate our view with a sketch of the rich interconnections with philosophy of science.