This paper proposes a novel nonlinear model for calculating Value-at-Risk (VaR) when the market risk factors of an option portfolio are heavy-tailed. A multivariate mixture of normal distributions is used to depict the heavy-tailed market risk factors and accordingly a closed form expression for...

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To develop a low-carbon economy, China launched seven pilot programs for carbon emissions trading (CET) in 2011 and plans to establish a nationwide CET mechanism in 2015. This paper formulated a multi-agent-based model to investigate the impacts of different CET designs in order to find the most...

Due to the complexity of crude oil price series, traditional statistics-based forecasting approach cannot produce a good prediction performance. In order to improve the prediction performance, a novel compressed sensing based learning paradigm is proposed through integrating compressed sensing...

Due to the distinct seasonal characteristics of hydropower, this study tries to propose a seasonal decomposition (SD) based least squares support vector regression (LSSVR) ensemble learning model for Chinese hydropower consumption forecasting. In the formulation of ensemble learning model, the...

To improve the forecasting accuracy of crude oil price with deeper understanding of the market microstructure, this paper proposes a wavelet decomposed ensemble model. The proposed model follows the Heterogeneous Market Hypothesis that assumes the unstationarity and dynamic changing nature of...

Data communication service has an important influence on e-commerce. The key challenge for the users is, ultimately, to select a suitable service provider. It is a multi-criteria decision-making (MCDM) problem where the user must weigh up the relative importance of factors such as costs and...

Due to the unique features of nuclear energy market, this paper tries to propose a novel data-characteristic-driven modeling methodology based on the principle of “data-characteristic-driven modeling”, aiming at formulating appropriate forecasting model closely in terms of sample data’s...

In this paper, a novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting, based on the principle of “decomposition and ensemble”. This hybrid...

The main purpose of this study is to devise a general regression neural network (GRNN)-based currency crisis forecasting model for Southeast Asian economies based upon the disastrous 1997–1998 currency crisis experience. For this some typical indicators of currency exchange rates volatility...