Risk Analysis of Shanghai Inter-Bank Offered Rate - A GARCH-VaR Approach

The inter-bank offered rate widely used by Chinese commercial banks is Shanghai Inter-Bank Offered Rate (Shibor). Shibor has experienced significant development since it was created. It offers different products by duration. Despite its importance in China’s financial market, Shibor’s risk has largely remained unexplored. Making contribution to existing literature on risk management of Shibor, this paper investigates risk of Shanghai Inter- Bank Offered Rate (Shibor) utilizing GARCH-VaR method. The VaR of each product is calculated and compared while GARCH model is designed for a simpler calculation. In order to have a clearer view of Chinese commercial banks, the data selected is Shibor data sample from 2006 to 2016, which is measured by GARCH-VaR model and verified effectiveness by chi-square test. Empirical results show strong evidence for the need of Chinese commercial banks to change the status quo so that the great fluctuation and abnormal situation can be avoided. Policy implication, involving the interest rate management and internal problem in commercial banks, is proposed for financial regulators

Evaluation of Germany’s Vulnerability Based on Economic Principles and Data Science

Vulnerability refers to the ability of a country or a region to resist internal and external natural factors such as ecological environment, economy, and society during its development. Germany is a country with low overall vulnerability and distinct regional differences, so research on its regional vulnerability can be a representative case for developing countries, as it provides a comprehensive assessment of regional vulnerability via scientific methodology and at the same time proposes rational solutions. The research collects quarterly data of 16 states of Germany from 2000 to 2015. This study describes a series of features of the data and establishes a comprehensive assessment of regional vulnerability including 33 indicators. Combined with the multi-criteria decision analysis method (MCDM), the analytic hierarchy process (AHP) method and the entropy method are applied to calculate the weights. A linear weighted sum method is applied to obtain the regional vulnerability index of Germany. Afterwards, by performing regression tests, this study empirically assess the influencing factors of the regional vulnerability of Germany. Moreover, this study adopts the neural network training model and forecasts the regional vulnerability of Germany of 2016 to 2020. This study identifies the main factors that influence the regional vulnerability of Germany, and proposes policy implications on the overall regulation to reduce the vulnerability of different regions in Germany accordingly

Multi-Objective Self-Paced Learning

Current self-paced learning (SPL) regimes adopt the greedy strategy to obtain the solution with a gradually increasing pace parameter while where to optimally terminate this increasing process is difficult to determine.Besides, most SPL implementations are very sensitive to initialization and short of a theoretical result to clarify where SPL converges to with pace parameter increasing.In this paper, we propose a novel multi-objective self-paced learning (MOSPL) method to address these issues.Specifically, we decompose the objective functions as two terms, including the loss and the self-paced regularizer, respectively, and treat the problem as the compromise between these two objectives.This naturally reformulates the SPL problem as a standard multi-objective issue.A multi-objective evolutionary algorithm is used to optimize the two objectives simultaneously to facilitate the rational selection of a proper pace parameter.The proposed technique is capable of ameliorating a set of solutions with respect to a range of pace parameters through finely compromising these solutions inbetween, and making them perform robustly even under bad initialization.A good solution can then be naturally achieved from these solutions by making use of some off-the-shelf tools in multi-objective optimization.Experimental results on matrix factorization and action recognition demonstrate the superiority of the proposed method against the existing issues in current SPL research