A neural network enhanced volatility component model

Volatility prediction, a central issue in financial econometrics, attracts increasing attention in the data science literature as advances in computational methods enable us to develop models with great forecasting precision. In this paper, we draw upon both strands of the literature and develop a novel two-component volatility model. The realized volatility is decomposed by a nonparametric filter into long- and short-run components, which are modeled by an artificial neural network and an ARMA process, respectively. We use intraday data on four major exchange rates and a Chinese stock index to construct daily realized volatility and perform out-of-sample evaluation of volatility forecasts generated by our model and well-established alternatives. Empirical results show that our model outperforms alternative models across all statistical metrics and over different forecasting horizons. Furthermore, volatility forecasts from our model offer economic gain to a mean-variance utility investor with higher portfolio returns and Sharpe ratio

Semi-Empirical Method for Estimating Stiffness and Deformation of Cylindrical Retaining Diaphragm Wall

This study presents a semi-empirical method to estimate stiffness and deformation of cylindrical retaining diaphragm wall. Based on the concept of "arch-beam" method, the retaining structure is separated into two structural components: arch unit and supported beam unit. The stiffness of both units is computed by parameter analytical method and then combined to obtain the total retaining stiffness of cylindrical diaphragm wall. The proposed model incorporates major factors considered in design of cylindrical retaining structure such as soil condition, geometry of excavation, geometries and materials of diaphragm wall, spacing and stiffness of ring beam, joints in diaphragm wall. A statistical equation is developed to relate the stiffness and lateral wall deformation. The proposed stiffness and deformation model is validated by 24 cylindrical excavation cases in literature

A Complex Event Processing-Based Online Shopping User Risk Identification System

Online shopping is an important part of the development of the Internet and plays a critical role in the current and future economy. However, there are many risks in the trading process. In order to reduce the hidden risks, it is necessary to study the method of risk identification. This paper proposes user risk identification method of online shopping system based on Complex Event Process (CEP). In this paper, we use the Esper as the CEP engine and the risk behavior patterns are defined as the event pattern language. Firstly, the CEP system captures event streams by analyzing data streams in real-time. Secondly, the captured event streams are sent to the CEP's engine. Finally, the Esper intelligently analyzes user's online shopping risk behaviors in real-time according to the event pattern languages. User risk identification effectively guarantees the fund and account security of the shopping users