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Few would deny the contribution of vocational education on economic growth and social development, but the spatial dynamics behind the economic role of vocational education in transition economies has not been examined by the literature on economics of education. Specifically, two hypotheses are tested. First, the economic growth and vocational education development have significant global spatial autocorrelation, which means the development of economy and vocational education of one province depends on the economic or education level of neighboring provinces. Second, the economic growth and vocational education development have significant local spatial autocorrelation. With per capital GDP and vocational education scale data of 31 provinces in China from 1995 to 2008, both hypotheses are supported. Finally, the results show that the elasticity with spatial metrics is 1.522, which means the stronger economic role of vocational education because the elasticity is larger than 1, while the elasticity without spatial dynamics is only 0.926 which implies the weak economic role of vocational education. It also shows that the OLS model is confronted with the risk of spurious regression without considering spatial dynamics and the spatial error model is preferred because it’s robust.

This paper mainly studies the American put option pricing with transaction costs in the CEV process. The specific Crank-Nicolson form of numerical solution is obtained by the finite difference method. On this basis, Hong Kong stock CKH option is selected as the object to estimate option price. Finally, by comparing with the actual price, the American put option pricing model is verified as reasonable. This paper is significant to the rational pricing and the institutional construction of the upcoming stock options in mainland China.

Taking the special nonlinear characteristics of the domestic and international gold price into account, this paper systematically analyzed its nonlinearity by the methods of BDS test, R/S analysis and improved largest Lyapunov exponent. We find three main results: (1) ARMA-GARCH model could adequately explain the linear and nonlinear dependence of gold price series; (2) long-memory does not exist anymore in price series explained by ARMA-GARCH model; (3) chaos phenomenon which is sensitive to the initial value does not exist either in the residuals of regression model. Therefore, we believe that the nonlinearity of gold price is mainly characterized in conditional heteroscedasticity rather than chaos.

The financial crisis since 2007 has highlighted the fragility of the banking system. To address this deficiency, the Basel committee has agreed upon Basel III which consists of reinforcing banks’ capital through new regulatory requirements. Raising additional funds by issuing common equity would lead to a significant cost for banks. Facing the problem, regulators came up with the concept of contingent capital. Contingent convertible (Coco) bonds have been the topic as both a solution to the “too big to fail” problem and a measure by which financial institutions can save themselves. In this paper we first give the introduction of Coco bonds, then present the design of Chinese Coco bonds and pricing Coco bonds through an equity derivative approach as well as sensitivity analysis based on B-S-M hypothesis. Considering that the stock return follows fat-tail distribution, this paper uses Heston stochastic volatility model to price Coco bonds. Finally we give some proposals for developing Coco bonds market in China.

Factor analysis is widely used in psychology, sociology and economics, as an analytically tractable method of reducing the dimensionality of the data in multivariate statistical analysis. The classical factor analysis model in which the unobserved factor scores and errors are assumed to follow the normal distributions is often criticized because of its lack of robustness. This paper introduces a new robust factor analysis model for dichotomous data by using robust distributions such as multivariate t -distribution. After comparing the fitting results of the normal factor analysis model and the robust factor analysis model for dichotomous data, it can been seen that the robust factor analysis model can get more accurate analysis results in some cases, which indicates this model expands the application range and practical value of the factor analysis model.

In this paper, a new location analysis method is presented. Given a connected graph G = ( V, E )with nonnegative edge cost c e for each edge e ∊ E , d ij is the cost of the shortest path between vertices i and j in the graph. The Connected p-facility Location Problem (CpLP) is to choose p vertices from V so as to minimize the total cost of shortest path of pair-wise of these p vertices. This problem is proved to be NP-hard and non-linear integer programming is formulated. Then, two algorithms are designed for solving the CpLP. One is a heuristic algorithm based on classical maximum clique approach, while the second one is genetic algorithm. Finally, computational results show the comparison between these two algorithms.

The author presents a new comprehensive evaluation method based on uncertainty theory in this paper. According to this kind of method, the evaluation quality of each evaluated index given by every expert is regarded as an uncertain variable. Then a comparison rule of uncertain comprehensive evaluation is proposed by means of expected value of uncertain variable. Some properties about the comparison rule are discussed. At last, an example about the comparison of urban environment quality is given to illustrate the uncertain comprehensive evaluation method.

Recognition of quality abnormal patterns for a dynamic process has seen increasing demands nowadays in the real-time process fault detection and diagnosis. As the dynamic data from a quality abnormal process is linearly inseparable, the recognition efficiency of a support vector machine (SVM) model mainly depends on the selection of the kernel functions and the optimizing of their parameters. Based on the analysis of the quality abnormal patterns in a dynamic process, this paper presents a recognition framework of quality abnormal patterns by using a multi-SVM (MSVM). For the different quality abnormal patterns, the simulation results indicate that the recognition accuracies of the MSVM classifiers with the selected kernel functions are quite different. A MSVM recognition model for quality abnormal patterns in a dynamic process is proposed by the kernel functions being of high accuracies. It is shown that this MSVM model with suitable kernel functions can increase the recognition accuracy.