Multivariate multiple regression modelling for technology analysis

Technology analysis is important for technology management areas such as research and development strategy and new product development. So many studies on technology analysis have been used across a diverse array of fields. Most of these were based on patent analysis, which analyses patent documents using text mining and statistics. The studies on conventional patent analyses constructed models consisting of various independent variables (technologies) and one dependent variable. But in reality, we have to consider a model that includes several dependent variables at the same time, because most technologies influence each other. In this paper, we propose a methodology for patent analysis that reflects the various response technologies simultaneously. We perform multivariate multiple regression modelling in order to efficiently conduct our technology analysis. To show how our modelling can be applied to realistic context, we carry out a case study using the patent documents related to three-dimensional printing technology.     

Markov regime-switching Beta-t-EGARCH

We suggest a Markov regime-switching (MS) Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model for U.S. stock returns. We compare the in-sample statistical performance of the MS Beta-t-EGARCH model with that of the single-regime Beta-t-EGARCH model. For both models we consider leverage effects for conditional volatility. We use data from the Standard Poor’s 500 (S&P 500) index and also a random sample that includes 50 components of the S&P 500. We study the outlier-discounting property of the single-regime Beta-t-EGARCH and MS Beta-t-EGARCH models. For the S&P 500, we show that for the MS Beta-t-EGARCH model extreme observations are discounted more for the low-volatility regime than for the high-volatility regime. The conditions of consistency and asymptotic normality of the maximum likelihood estimator are satisfied for both the single-regime and MS Beta-t-EGARCH models. All likelihood-based in-sample statistical performance metrics suggest that the MS Beta-t-EGARCH model is superior to the single-regime Beta-t-EGARCH model. We present an application to the out-of-sample density forecast performance of both models. The results show that the density forecast performance of the MS Beta-t-EGARCH model is superior to that of the single-regime Beta-t-EGARCH model.     

基于交流外部性的行为主体城市聚集空间结构分析

因行为人寻求空间上交流的需求形成一种聚集的向心力,从而产生在一定区域内其分布状态表现出了单峰分布现状。但是在处于社会中的行为人是以城市为中心进行分布,使行为人聚集的空间结构产生了不同程度的变化。文章综述了行为主体（个人和企业）在交流情况下产生聚集的国内外研究现状,探讨了个人之间聚集社会外部性产生的前提条件,通过数学模型分析个人聚集得出了个人聚集分布的最优结论;通过对企业交流情况下聚集形成中央商务区的均衡分布状况,得出了企业在中央商务区为钟型分布,并且利用信息的传播界限可解释中央商务区的形成的结论,同时也说明了在信息传递条件下写字楼的高度随着距中央商务区距离远近而发生变化的结果。     

江西森林碳储量空间分布特征及其价值分析

利用立木蓄积量、森林面积及森林土壤碳储量等基本监测数据,对江西省森林生态系统的碳储量和碳汇及其经济价值进行了估算。结果表明:江西省森林生态系统碳储量为1 286.37×106t,年碳汇能力为53.92×106t,其经济价值分别为3 923.429亿元和164.47亿元,江西省森林生态系统平均碳密度为147.57 t/hm2。预计江西省年2010年到2020年间森林生态系统碳汇潜力278.09×106t碳或1 020.59×106t CO2,由此产生的经济效益平均每年可达53.01亿元。同时还对江西省11个市的森林碳储量、碳汇及其经济价值进行了估算,得出了全省各市森林生态系统碳储量和空间分布特征以及各地的森林碳汇林地价格。     

圆形阵列天线方向图的分布函数优化方法

以离散有限长复指数和的计算为基础,讨论了二维阵列的方向图 问题。通过引入分布函数的概念,可知离散有限长复指数和为其分布函数离散傅里叶变换在 1点的值。基于此结论,分析了二维阵天线阵元分布于天线方向图之间的关系：二维阵列任 意方向的方向图为其在该方向分布函数的离散傅里叶变换,该分布函数可通过统计其投影在 该方向的临近区域处阵元数目获得。然后,通过对混合圆形阵列天线方向图的分析,验证了 该关系的正确性,并说明了上述关系在分析阵列方向图的应用。在此基础上,进一步分析了 阵元权重系数对方向的影响,发现该方法与通过改变阵列天线阵元分布的方法等效。最后 ,提出了基于分布函数的圆形阵列方向图优化方法,并通过仿真实验验证了该方法的可 行性。该方法可通过改变圆形阵列阵元沿径向的权重系数使得其对应的任意方向的方向图与 期望的窗函数近似。     

中国城乡有效经济增长的差异性分析

本文提出了有效经济增长的概念,构建了有效经济增长动态模型,利用该模型建立了超额人均收入等相关变量的测定方法。在此基础上分别测算我国城镇部门与农村部门有效经济增长的相关指标,并进行比较分析。结果发现,改革开放以来我国城镇部门有效经济增长无论从绝对量上还是增长速度上都明显高于、快于农村部门;城镇部门有效经济增长的减损量大于农村部门;而农村部门对有效经济增长的减损强度却大于城镇部门。因此,推动城市化且合理控制城市化进程的速度,是降低我国有效经济增长的减损强度,实现有效经济增长可持续性的有力途径。     

Can channel pattern trading be profitably automated?

Financial markets, such as the global foreign exchange (FX) market, often exhibit trending behaviour. Within such trends, the market level oscillates with changes in market consensus. Continued oscillations of this type result in the formation of wave patterns within the underlying trend known as channels, which are used by technical analysts as trade entry signals. A sample space of such channels has been constructed from a set of US dollar/British pound Spot FX tick data from 1989–1997 using pattern recognition algorithms and the profitability of trading using such patterns has been estimated. A number of attributes of the resulting collection of channels has been subjected to statistical analysis with the aim of classifying patterns that can be traded profitably using a number of simple trading rules. Results of this analysis show that there exist statistically significant links between the channels' attributes and profitability.     

Lognormal Approximation of Complex Path-Dependent Pension Scheme Payoffs

Abstract

This paper analyzes an explicit return smoothing mechanism which has recently been introduced as part of a new type of pension savings contract that has been offered by Danish life insurers. We establish the payoff function implied by the return smoothing mechanism and show that its probabilistic properties are accurately approximated by a suitably adapted lognormal distribution. The quality of the lognormal approximation is explored via a range of simulation-based numerical experiments, and we point to several other potential practical applications of the paper's theoretical results.     

A multivariate Lévy process model with linear correlation

In this paper, we develop a multivariate risk-neutral Lévy process model and discuss its applicability in the context of the volatility smile of multiple assets. Our formulation is based upon a linear combination of independent univariate Lévy processes and can easily be calibrated to a set of one-dimensional marginal distributions and a given linear correlation matrix. We derive conditions for our formulation and the associated calibration procedure to be well-defined and provide some examples associated with particular Lévy processes permitting a closed-form characteristic function. Numerical results of the option premiums on three currencies are presented to illustrate the effectiveness of our formulation with different linear correlation structures.     

Evaluating portfolio Value-at-Risk using semi-parametric GARCH models

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for evaluating the Value-at-Risk (VaR) of a portfolio with arbitrary weights. We specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within-sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations.