Crude oil price shocks derive from many sources, each of which may bring about different effects on macro-economy variables and require completely different designs in macro-economic policy; thus, distinguishing the sources of oil price fluctuations is crucial when evaluating these effects. This paper establishes an open-economy dynamic stochastic general equilibrium (DSGE) model with two economies: China and the rest of the world. To assess the effects of oil price shocks, the CES production function is extended by adding oil as an input. Based on the model, the effects of four types of oil price fluctuations are evaluated. The four types of oil price shocks are supply shocks driven by political events in OPEC countries, other oil supply shocks, aggregate shocks to the demand for industrial commodities, and demand shocks that are specific to the crude oil market. Simulation results indicate the following: Oil supply shocks driven by political events mainly produce short-term effects on China's output and inflation, while the other three shocks produce relatively long-term effects; in addition, demand shocks that are specific to the crude oil market contribute the most to the fluctuations in China's output and inflation.

This paper proves the co-movement of foreign trade in different countries or areas which belong to ten economic regions by MS-VAR model. The studies show that trade crisis lags behind economic crisis and spreads from the core of the economic crisis to its periphery which is closely-related with it. The trade crisis corresponding to the US subprime crisis spreads faster than before, which has struck worldwide foreign trade. In order to get the main factors affecting trade crisis, the authors construct composite indices which are proxies of economic growth and price levels of internal and external regions. The results of logistic and linear panel models show that economic growth affects more to trade cycle than price level. The results of panel models with dummy variable of trade crisis show that the outside economic growth do bad to the recovery of internal foreign trade during trade crisis corresponding to Mexican peso crisis, the Asian financial crisis and the Russian debt crisis, while the opposite is true during the internet bubble burst and the US subprime crisis.

Novel data series constructed from Internet-based platforms such as Google have been widely applied to analyze economic and financial indicators and have been demonstrated to be effective in short-term forecasts. However, few studies have demonstrated the role of Google search data in analyzing trader positions and energy price volatility. This paper uses the Google search volume index (GSVI) to measure investor attention, and investigate the relationships among the GSVI, different trader positions, and crude oil prices from January 2004 to June 2014. The empirical results present some new evidences. First, the GSVI measures investor attention from noncommercial and nonreporting traders, rather than commercial traders. Second, the feedback loop between GSVI and crude oil price is verified. Third, the GSVI improves the forecast accuracy of crude oil price in recursive one-week-ahead forecasts. This paper contributes to existing literature by incorporating open source Internet-based data into the analysis and prediction of crude oil prices, as well as other prices in financial markets in the Big Data Era.

The price discovery mechanism between futures prices and spot prices in oil markets has been a controversial subject for decades. Due to the different patterns between various scales of oil prices, this paper targets this issue from a multi-scale perspective, which is different from previous studies. With the help of MEMD and PDC, we re-examine the relationships between spot and futures oil prices of WTI. The MEMD method decomposes the prices into short-run oscillatory modes and long-run trend modes, and then PDC is applied to explore causality in the frequency domain. In the empirical analysis, the role of futures markets in providing an efficient price for the spot market is verified. This price discovery mechanism varies according to the analyzing scale: 3-month futures prices lead the spot and 1-month futures prices primarily in high-frequency oscillatory modes, while 1-month futures prices lead the two other prices in lower-frequency oscillatory modes. Our findings reveal a deeper dependence structure between spot and futures prices for crude oil and hence may provide helpful guidance for investors and policy makers. Read More: http://www.worldscientific.com/doi/abs/10.1142/S2335680414500185

Public attention on natural gas price, which reflects the demand dynamics, is considered as a new factor to influence the movement of price. So investigate the impact of public attention on natural gas price is an innovative research issue in energy economics. This paper innovatively constructs a measure of public attention and examines its impact on natural gas price. A data set generated from Google Trends is used to measure public attention and then rigorous econometric models are applied to evaluate its predictive ability. The empirical study shows that (i) public attention is closely related to natural gas price, with contemporaneous positive correlation coefficient being 0.59, (ii) public attention leads natural gas price, (iii) the model including public attention data outperforms benchmark model. By using a more direct and representative way of forecasting based on the knowledge collected from the users, this paper also has important implications for applying Internet knowledge to improve the forecast accuracy of other energy price.

Although judgmental models are widely applied in practice to alleviate the limitation of statistical models ignoring domain knowledge, they are still suffering from many kinds of biases and inconsistencies inherent in subjective judgments. Moreover, most of the prior studies are often concentrated on making judgmental adjustments to statistical projections and ignore incorporating domain knowledge in other forecasting steps. This paper proposes a framework under which domain knowledge are integrated with the whole forecasting process and a new forecasting method is developed. The new method is applied to forecasting the container throughput of Guangzhou Port, one of the most important ports of China. In order to test the effectiveness of the new method, the authors compare its performance with that of the ARIMAX model. The results show that the new method significantly outperforms the ARIMAX model.

With the development of the global economy, interaction among different economic entities from one region is intensifying, which makes it significant to consider such interaction when constructing composite index for each country from one region. Recent advances in signal extraction and time series analysis have made such consideration feasible and practical. Singular spectrum analysis (SSA) is a well-developed technique for time series analysis and proven to be a powerful tool for signal extraction. The present study aims to introduce the usage of the SSA technique for multi-country business cycle analysis. The multivariate SSA (MSSA) is employed to construct a model-based composite index and the two dimensional SSA (2D-SSA) is employed to establish the multi-country composite index. Empirical results performed on Chinese economy demonstrate the accuracy and stability of MSSA-based composite index, and the 2D-SSA based composite indices for Asian countries confirm the efficiency of the technique in capturing the interaction among different countries.

Decomposition-based vector autoregressive model (DVAR) is a new modeling technique which is more efficient in information employment. In this paper, the performance of the DVAR model has been considered by applying it to the monthly spot WTI crude oil price time series data. The results are compared with those obtained using Box-Jenkins ARIMA model. Different evaluation criteria are employed to compare the performance of the DVAR and ARIMA models, and the results show that the DVAR modeling technique gives a much more accurate forecast than the classic ARIMA model. Read More: http://www.worldscientific.com/doi/abs/10.1142/S233568041350004X

Based on a new panel data set covering exports of four Chinese port cities to 32 countries and Hong Kong over the period of 1997–2008, this paper extends the conventional gravity model to assess the impacts of transport costs and port efficiency on China’s exports. The results can be summarized as three main findings: (1) The improvements of port efficiency and reduction of road transport costs play a vital role in China’s export competitiveness in the global market. The coefficient estimates on them are relatively large, around 0.72 and −0.89, respectively; (2) The effect of transport costs and port efficiency on China-to-Asia exports significantly exceeds that on China-to-Europe and China-to-America exports; (3) The overall estimated elasticity of road, railroad and port measure is 1.66, which is almost three times that of the average wage of port cities. The empirical results provide strong evidences that upgrading China’s transport service networks should offer greater scope for maintaining and increasing its competitive edge in low cost productions. The findings offer some insights and priorities for government policy making.

This paper investigates how does China’s macro-economy response to the world crude oil price shock, applying a structural dynamic factor model approach based on 71 China monthly macroeconomic indicators from January 1997 to August 2011. Main conclusions are the following. First, China’s prices are raised, and moreover, the responses of China’s crude oil price, import price index, producer price index, retail price index and consumer price index to the WTI crude oil price shock weaken gradually, which accords with price transmission mechanism. Second, because there are probably China’s high demand for import products, lower added-value or technology of China’s export products and many venture capital in China’s stock market, foreign trade and stock market have sensitivities to price, and then the responses of China’s foreign trade and stock market are larger than one of investment, consumption and industrial production to the WTI crude oil price shock. Finally, after the WTI crude oil price shock, China’s interest rates and interbank offered rates are raised, the growth rate of Money supply (M1) does not immediately fall and begins to decrease after 3 months due to time delay, and furthermore there is RMB real effective exchange rate appreciation.

In this paper, a hybrid model integrating wavelet decomposition and least squares support machines (LSSVM) is proposed for crude oil price forecasting. In this model, the Haar à trous wavelet transform is first selected to decompose an original time series into several sub-series with different scales. Then the LSSVM is used to predict each sub-series. Subsequently, the final oil price forecast is obtained by reconstructing the results of the sub-series forecasts. The experimental results show that the integrated model, based on multi-scale wavelet decomposition, outperforms the traditional single-scale models. Furthermore, the proposed hybrid model is the best among all the models compared in this study. To fully integrate the advantages of several models, a combined forecasting model is presented. The study shows that the combined forecasting model is clearly better than any individual model for crude oil price forecasting. Read More: http://www.worldscientific.com/doi/abs/10.1142/S1793005711001949

The impact of extreme events on crude oil markets is of great importance in crude oil price analysis due to the fact that those events generally exert strong impact on crude oil markets. For better estimation of the impact of events on crude oil price volatility, this study attempts to use an EMD-based event analysis approach for this task. In the proposed method, the time series to be analyzed is first decomposed into several intrinsic modes with different time scales from fine-to-coarse and an average trend. The decomposed modes respectively capture the fluctuations caused by the extreme event or other factors during the analyzed period. It is found that the total impact of an extreme event is included in only one or several dominant modes, but the secondary modes provide valuable information on subsequent factors. For overlapping events with influences lasting for different periods, their impacts are separated and located in different modes. For illustration and verification purposes, two extreme events, the Persian Gulf War in 1991 and the Iraq War in 2003, are analyzed step by step. The empirical results reveal that the EMD-based event analysis method provides a feasible solution to estimating the impact of extreme events on crude oil prices variation.

Macroeconomic policy making is a complex systematic process, which requires in-depth understanding of current economic situation, prediction of future economic trend, and proper policy evaluation measurements. Instruments of early warning, and policy simulation are often employed in macroeconomic policy making. However, no matter how well it is developed, any single instrument is often inadequate for policy making support, because of the gap between theories and practice. In this paper, macroeconomic early warning theories are integrated with the policy decision support concepts. Three stages are involved in the macroeconomic policy making process: monitoring, forecasting, and policy simulation. Based on this idea, an integrated alert–response framework is proposed with one corresponding module for each stage. Within this framework, not only information can be exchanged freely among these modules, but the monitoring–forecasting–simulation process can run smoothly to realize timeliness and efficient policy making support. Moreover, a knowledge base is incorporated into the framework to support the economic early warning and policy making support process. Therefore, this framework is featured in integration and a final all-round report, including current economic status, future trend prediction, policy making suggestions, external information, and expert opinions, can be generated. An implementation of this framework was developed for China's macroeconomic adjustment and has been put into practice since early 2006. A case of national economic growth analysis based on the proposed framework is given to demonstrate how the framework serves for government policy making routines. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219622009003442

This paper presents a kernel clustering-based support vector machine (KCB-SVM) that generalizes the linear clustering-based support vector machine (CB-SVM) to solve nonlinear classification problems in a novel way. It can not only handles large data sets, but can also have nonlinear discriminant power. By introducing kernel clustering, KCB-SVM unifies the metrics in the clustering and training stages. Elaborately designed clustering features summarize all the information required for further clustering and training, which allows only one scan of the total data sets. Experiments on both artificial and real large data sets show that the KCB-SVM not only achieves better classification accuracy than random sampling, active learning and CB-SVM, but also retains the ability to handle large data sets.

The importance of understanding the underlying characteristics of international crude oil price movements attracts much attention from academic researchers and business practitioners. Due to the intrinsic complexity of the oil market, however, most of them fail to produce consistently good results. Empirical Mode Decomposition (EMD), recently proposed by Huang et al., appears to be a novel data analysis method for nonlinear and non-stationary time series. By decomposing a time series into a small number of independent and concretely implicational intrinsic modes based on scale separation, EMD explains the generation of time series data from a novel perspective. Ensemble EMD (EEMD) is a substantial improvement of EMD which can better separate the scales naturally by adding white noise series to the original time series and then treating the ensemble averages as the true intrinsic modes. In this paper, we extend EEMD to crude oil price analysis. First, three crude oil price series with different time ranges and frequencies are decomposed into several independent intrinsic modes, from high to low frequency. Second, the intrinsic modes are composed into a fluctuating process, a slowly varying part and a trend based on fine-to-coarse reconstruction. The economic meanings of the three components are identified as short term fluctuations caused by normal supply-demand disequilibrium or some other market activities, the effect of a shock of a significant event, and a long term trend. Finally, the EEMD is shown to be a vital technique for crude oil price analysis.