Ling Tang

Oil-importing optimal decision considering country risk with extreme events: A multi-objective programming approach

From perspective of energy security, this study focuses on oil-importing optimal decision based on multi-objective programming approach. Different from other models, country risk is considered as the main objective to minimize risk exposure of importing disruption. What is more, this model connects emergency management with programming, and optimal decisions are solved under different scenarios of emergency, where one given kind of extreme events break out and impact exporting regions to different degrees. Specifically, two main steps are involved in the proposed methodology, including impact analysis of the extreme events and optimization programming under scenarios of emergency. The first step is to statistically analyze whether and to what extent the given extreme events impact country risk of oil-exporting sources. Secondly, a multi-objective programming model is formulated, and optimal decision is simulated under different scenarios with extreme events. For illustration, Chinas oil-importing optimization is performed to verify the practicability of the novel methodology. The experimental results suggest that wars in Middle East may significantly enhance country risk of Middle East; and Chinas oil-importing optimal plan should be changed correspondingly. This further indicates that the proposed methodology can be utilized as an effective tool to adjust oil-importing plan according to certain extreme events.

MODELING DYNAMIC CORRELATIONS AND SPILLOVER EFFECTS OF COUNTRY RISK: EVIDENCE FROM RUSSIA AND KAZAKHSTAN

Oil economies in the Former Soviet Union (FSU) region, with geographical proximity to each other, are usually impacted by some common risk factors, which make their country risks closely correlated. This paper focuses on correlation between country risks and investigates the spillovers of country risk returns (CRR). Taking Russia and Kazakhstan for example, firstly, this paper identifies the structural breaks in CRR series, using iterated cumulative sums of squares (ICSS) algorithm. Secondly, on the assumption that there may be similarity in structural breaks of CRR series of the two countries, Vector Autoregression (VAR) process and Granger causality test are used to identify whether there are mean spillovers of CRR series. Finally, the volatility spillovers are captured by using multivariate conditional volatility models in the framework of the BEKK models. Empirical results show that (1) there are significant unidirectional mean spillovers from Russia to Kazakhstan; (2) there are asymmetric bidirectional volatility spillovers between Russia and Kazakhstan; and volatility spillover effects from Russia to Kazakhstan are stronger.

A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting

We propose a novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting. Our method is based on the principles of “data-characteristic-based modeling” and “decomposition and ensemble”. The model improves on existing decomposition ensemble learning techniques (with “decomposition and ensemble”) by using “data-characteristic-based modeling” to forecast the decomposed modes. Ensemble empirical mode decomposition is first used to decompose the original nuclear energy consumption data into a series of comparatively simple modes, reducing the complexity of the data. Then, the extracted modes are thoroughly analyzed to capture hidden data characteristics. These characteristics are used to determine appropriate forecasting models for each mode. Final forecasts are obtained by combining these predicted components using an effective ensemble tool, such as least squares support vector regression. For illustration and verification purposes, we have implemented the proposed model to forecast nuclear energy consumption in China. Our numerical results demonstrate that the novel method significantly outperforms all considered benchmarks. This indicates that it is a very promising tool for forecasting complex and irregular data such as nuclear energy consumption.

AN INTEGRATED DATA CHARACTERISTIC TESTING SCHEME FOR COMPLEX TIME SERIES DATA EXPLORATION

In this paper, an integrated data characteristic testing scheme is proposed for complex time series data exploration so as to select the most appropriate research methodology for complex time series modeling. Based on relationships across different data characteristics, data characteristics of time series data are divided into two main categories: nature characteristics and pattern characteristics in this paper. Accordingly, two relevant tasks, nature determination and pattern measurement, are involved in the proposed testing scheme. In nature determination, dynamics system generating the time series data is analyzed via nonstationarity, nonlinearity and complexity tests. In pattern measurement, the characteristics of cyclicity (and seasonality), mutability (or saltation) and randomicity (or noise pattern) are measured in terms of pattern importance. For illustration purpose, four main Chinese economic time series data are used as testing targets, and the data characteristics hidden in these time series data are thoroughly explored by using the proposed integrated testing scheme. Empirical results reveal that the natures of all sample data demonstrate complexity in the phase of nature determination, and in the meantime the main pattern of each time series is captured based on the pattern importance, indicating that the proposed scheme can be used as an effective data characteristic testing tool for complex time series data exploration from a comprehensive perspective.

Modeling on Oil-Importing Risk under Risk Correlation

As the world economic power and military strengthchange, a new round of contention for energy has started. What will be the sticking point is whether China can access to adequate oil resources safely in the new redistribution of energy resources which may impact Chinas oil imports and energy supply in the future. Based on the Herfindahl-Hirschman Index, in this paper we propose an improved methodology applicable to evaluating the country risk that oil importing countries face. The OICR index for the worlds major oil-importing countries against 17 major oil-producing countries is calculated and analyzed, and then some suggestions about Chinas oil importing strategy are given.

Discovering the impact of systemic and idiosyncratic risk factors on credit spread of corporate bond within the framework of intelligent knowledge management

This paper exploits the implied information of data collected from credit spreads of Chinese corporate bonds and systemic and idiosyncratic risk factors. We compute contribution of risk factors to credit spreads of Chinese corporate bonds by establishing the unbalanced panel data model, identify the key factors impacting the size of credit spreads of corporate bonds. Knowledge extracted by data mining is helpful to investors for reasonable pricing of bonds and making rational investment decisions. When selecting variables, the unbalanced panel data model is used to calculate the Zero-volatility credit spreads, which are more accurate. We use term structure adjusted return of bond index as the systemic risk factor of corporate bond market, the three Fama/French systemic factors as the systemic risk factors of stock markets and idiosyncratic stock/bond volatility and idiosyncratic bond value-at-risk as the idiosyncratic risk factors. Empirical analysis of corporate bonds sampling China’s listing Corporation issued and traded on Shanghai Stock Exchange from 2008 to 2011 shows that the size of credit spreads is mainly determined by the systemic risk factors of bond market, i.e. risk factors of stock market make very little contribution to the spread; the idiosyncratic risk factors also contribute. An interesting phenomenon is that we find that the relationship between idiosyncratic stock volatility and credit spread is negative, which is contrary to extant research while the relationship is positive and mainly focuses on impact of risk factors on credit spread of corporate bond.

Exploring the Value at Risk of Oil-exporting Country Portfolio: An Empirical Analysis from the FSU Region

Abstract In the perspective of oil-importers, this paper considers an extension of the Value at Risk approach incorporated with time-varying conditional volatility model to trace the actual dynamic risk of regional oil-importing portfolio caused by the country risk volatility. With an application to oil economies in the Former Soviet Union (FSU) region, empirical results show that the country portfolio risk of oil-imports and country risk volatility in the FSU region has more significant influence on Chinas oil-importing risk than that on EUs.

SD-LSSVR-Based Decomposition-and-Ensemble Methodology with Application to Hydropower Consumption Forecasting

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 hydropower consumption forecasting. In the SD-LSSVR-based decomposition and ensemble model, the original hydropower consumption series are first decomposed into trend cycle, seasonal factor and irregular component. Then the LSSVR is used to predict the three different components independently. Finally, these prediction results of the three components are combined with another LSSVR to formulate an ensemble result as the final prediction. Experimental results reveal that the proposed novel method is very promising for time series forecasting with seasonality and nonlinearity for that it outperforms all the other benchmark methods listed in our study in both level accuracy and directional accuracy.