个人简介

姓名：张珣
性别：女

出生年月：?1982?年?3?月?19?日

工作单位： 中国科学院数学与系统科学研究院

职称：副研究员





教育经历：


2004.9—2009.7：中国科学院数学与系统科学研究院，硕博连读，管理科学与工程专业，导师：汪寿阳。
2000.9—2004.7：中国人民大学信息学院，本科，信息管理与信息系统专业。


获奖情况：


[1]2016年，中国系统工程学会系统科学与系统工程科学技术奖之青年科技奖
[2]2014年，中国管理学青年奖
[3]2013年，北京市科学技术奖二等奖，经济预测预警理论方法及决策支持系统
[4]2012年，入选中国科学院数学与系统科学研究院“陈景润未来之星”特殊人才计划
[5]2011年，全国百篇优秀博士论文提名奖
[6]2010年，中国科学院优秀博士论文奖
[7]Best Paper Award for the 8th International Conference on Risk Management and Financial Systems Engineering, The 8th International Conference on Risk Management and Financial Systems Engineering, 2010, Beijing, China
[8]2009年，中国科学院院长奖学金特别奖
[9]The Green Group Award of Computational Finance and Business Intelligence，2009，Louisiana, U.S.A.
[10] 2009年，中国企业运筹学第四届学术年会优秀论文奖
[11] 2009年，中国科学院博士奖学金特等奖
[12] 2008年，中国系统工程学会第15届年会最佳论文奖
[13] 2008年，中国科学院数学与系统科学研究院院长奖学金特别奖
[14] 2008年，中国科学院研究生院BHP Billiton奖学金
[15] 2008年，中国科学院英达奖学金特等奖
[16] 2007年，中国科学院格林经济金融奖学金特等奖
[17] 2007年，中国科学院数学与系统科学研究院院长奖学金优秀奖
研究项目：


[1]能源价格波动与宏观经济管理，国家自然科学基金优秀青年基金项目**，2015年1月至2017年12月，项目主持人
[2]基于景气分析框架的原油价格周期波动分析及拐点预测，国家自然科学基金青年科学基金项目**，2012年1月至2015年12月，项目主持人
[3]宏观经济多维景气分析：理论、方法及实证研究，中国科学院“优秀博士学位论文、院长奖获得者科研启动专项资金”，2010年7月至2013年6月，项目主持人
[4]宏观经济分析与预测，中国科学院青年创新促进会资助项目，2011年1月至2014年12月，项目主持人
[5]欧洲主权债务危机及其对中国实体经济的影响与对策研究，国家自然科学基金应急科学研究专项，2012年5月至2013年2月，项目参与人
[6]我国金融安全综合管理研究，国家自然科学基金重点项目，2010年1月至2013年12月，项目参与人
[7]客户风险预警研究项目，国家开发银行与银监会重点合作项目，2012年4月至2013年6月，项目核心成员
[8]与商务部合作进出口监测、预测、预警及政策分析系列项目，2008年至今
[9]全球经济监测与政策模拟仿真平台建设预研项目，中国科学院知识创新工程重要方向项目，项目核心人员
[10] 与国家发展改革委员会合作中国宏观经济监测、预测、预警及政策模拟系列重点项目，2008年至2013年，项目核心人员
[11] 与中国人民银行总行及其分支行合作中国宏观经济与金融监测预警系列项目，2006年至2013年，项目核心人员
专著：


[1]汪寿阳，张珣，尚维，郑桂环，宏观经济监测预警方法及预警系统. 2016. 北京：科学出版社
[2]张珣，汪寿阳，DAC方法论及其在国际原油价格波动分析与预测中的应用. 2010，北京：科学出版社
[3]郑桂环，张珣，韩艾，张嘉为，汪寿阳. 经济景气分析方法的新进展. 2010，北京：科学出版社
[4]郑桂环，汪寿阳，徐山鹰，张珣，陈曦，谢雯，庞叶，张嘉为，中国进出口贸易分析与预测，2010，北京：科学出版社


论著章节：


[1]张珣等，2016年我国对外贸易形势分析，2016年中国经济预测与展望, 40-49, 2016年，北京: 科学出版社
[2]张珣，尚妍，杨晓光，汪寿阳，2014年我国对外贸易形势展望，2014年中国经济预测与展望, 38-51,2014年，北京:科学出版社
[3]张珣，张莉莉等，2013年我国进出口形势分析与预测：2013年中国经济预测与展望，2013年，北京：科学出版社
[4]欧变玲，张珣等，2012年我国进出口分析与预测：2012年中国经济预测与展望，2012年，北京科学出版社
[5]张戈，赵琳，张珣，张嘉为，杨晓光，2011年世界及中国经济景气分析：2011年中国经济预测与展望，北京：科学出版社
[6]张嘉为，齐晓楠，赵琳，张珣等，2011年我国进出口预测：2011年中国经济预测与展望，2011年，北京：科学出版社
[7]张嘉为，齐晓楠，赵琳，张珣等，2010年我国进出口预测：2010年中国经济预测与展望，2010年，北京：科学出版社
[8]张嘉为，齐晓楠，张珣，郑桂环，徐山鹰，汪寿阳，2009年我国进出口预测：2009中国经济预测与展望，北京：科学出版社
部分政策研究报告：

[1]2016年我国进出口形势分析与预测，2016年1月
[2]2015年我国进出口形势分析与预测，2015年1月
[3]中科院预测中心专家关于我国新的经济增长点的政策建议，2014年2月
[4]2014年我国进出口形势分析与预测，2014年1月
[5]2013年我国进出口形势分析与预测，2012年11月
[6]2012年6至12月份我国进出口形势分析与预测，2012年6月
[7]2012年我国进出口形势分析与预测，2012年4月
[8]欧债危机对我国进出口的影响分析，2012年1月
[9]输入型通胀压力缓解，但政策宜微调忌过度放松，2012年6月
[10] 2011年我国进出口预测和形势分析，2010年11月
[11] “十二五”期间我国国际收支形势预测与分析，2011年5月

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研究方向 能源经济学

宏观经济分析与预测

决策支持系统

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学术论文 The effects of oil price shocks on output and inflation in China 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.

Construction and analysis of common foreign trade cycle based on MS-VAR: An empirical study of global experience 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.

How does Google search affect trader positions and crude oil prices? 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.

Exploring the relationship between crude oil spot and futures prices: New perspective from multi-scale decomposition Read More: http://www.worldscientific.com/doi/abs/10.1142/S00185 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/S00185

How Does Public Attention Influence Natural Gas Price?: New Evidence with Google Search Data 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.

A New Approach to Forecasting Container Throughput of Guangzhou Port with Domain Knowledge 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.

Estimating multi-country prosperity index: A two-dimensional singular spectrum analysis approach 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.

The more the better: forecasting oil price with decomposition-based vector autoregressive model 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/S0004X

Transport costs and China’s exports: Some empirical evidences 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.

How does China’s macro-economy response to the world crude oil price shock: a structural dynamic factor model approach 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.

An integrated model using wavelet decomposition and least squares support vector machines for monthly crude oil prices forecasting 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/S**01949

Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method 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.

An integrated decision support framework for macroeconomic policy making based on early warning theories 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/S02**442

Nonlinear clustering-based support vector machine for large data sets 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.

A new approach for crude oil price analysis based on Empirical Mode Decomposition 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.

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联系方式 地址：
电话：北京市海淀区中关村东路55号思源楼514
邮箱：zhangxun@amss.ac.cn

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