Coal is the principal form of energy used in China. Hence, coal price variations are expected to have some influence on merchandise prices. Monthly data from January, 2002, to October, 2010, were used to construct a v...Coal is the principal form of energy used in China. Hence, coal price variations are expected to have some influence on merchandise prices. Monthly data from January, 2002, to October, 2010, were used to construct a varying-parameter state space model, and an error correction model, to estimate the influence of coat prices on Chinese merchandise prices. The time lag and the dynamic relationship were determined from the data. A long term equilibrium relationship between coal price and the PPI, and the CPI, can be observed. The long term influence of coal price fluctuations on the PPI is 0.263%. The corresponding value for the CPI is 0.157%. The PPI shows an influence from coal price change in the first period of observation： by eight periods the influence is obvious, after which it diminishes. The effect of coal price change on the CPI is rather weak and has no long term memory. Analysis of variance shows a similar situation. The elas- ticity coefficient of coal prices on the CPI, or the PPI, fluctuates over the 2002-2004 period. From 2002 to 2007 the influence elasticity on the CPI declined and subsequently levelled off after 2009.展开更多
This paper adopts the concept of dynamic feedback systems to model the behavior of financial markets, or more specifically, the stock market from a dynamic system point of view. Based on a feedback adaptation scheme, ...This paper adopts the concept of dynamic feedback systems to model the behavior of financial markets, or more specifically, the stock market from a dynamic system point of view. Based on a feedback adaptation scheme, the authors model the movement of a stock market index within a framework that is composed of an internal dynamic model and an adaptive filter. The output-error model is adopted as the internal model whereas the adaptive filter is a time-varying state space model with instrumental variables. Its input-output behavior, and internal as well as external forces are then identified. Special attention has also been paid to the recent financial crisis by examining the movement of Dow Jones Industrial Average （DJIA） as an example to illustrate the advantage of the proposed framework. Supported by time-varying causality tests, five influential factors from economic and sentiment aspects are introduced as the input of this framework. Testing results show that the proposed framework has a much better prediction performance than the existing methods, especially in complicated economic situations. An application of this framework is also presented with focuses on forecasting the turning periods of the market trend. Realizing that a market trend is about to change when the external force begins to exhibit clear patterns in its frequency responses, the authors develop a set of rules to recognize this kind of clear patterns. These rules work well for stock indexes from US, China and Singapore.展开更多
基金Supported by the National Natural Science Foundation of China(61903322,61773335)the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(LAPS 19003)+3 种基金the State Key Laboratory of Mechanics and Control of Mechanical Structures(MCMS-E-0520G01)the Six Talent Peaks Foundation of Jiangsu Provincial(KTHY2018038)the Natural Science Foundation of Yangzhou City for Outstanding Young Scholars(YZ2017099)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX20-0890)。
基金support for this work, provided by the National Natural Science Foundation of China (No. 71003097)Jiangsu Province Social Science Foundation (No. 10EYD025)2008 China University of Mining and Technology Youth Foundation Program (No.2008W04)
文摘Coal is the principal form of energy used in China. Hence, coal price variations are expected to have some influence on merchandise prices. Monthly data from January, 2002, to October, 2010, were used to construct a varying-parameter state space model, and an error correction model, to estimate the influence of coat prices on Chinese merchandise prices. The time lag and the dynamic relationship were determined from the data. A long term equilibrium relationship between coal price and the PPI, and the CPI, can be observed. The long term influence of coal price fluctuations on the PPI is 0.263%. The corresponding value for the CPI is 0.157%. The PPI shows an influence from coal price change in the first period of observation： by eight periods the influence is obvious, after which it diminishes. The effect of coal price change on the CPI is rather weak and has no long term memory. Analysis of variance shows a similar situation. The elas- ticity coefficient of coal prices on the CPI, or the PPI, fluctuates over the 2002-2004 period. From 2002 to 2007 the influence elasticity on the CPI declined and subsequently levelled off after 2009.
文摘This paper adopts the concept of dynamic feedback systems to model the behavior of financial markets, or more specifically, the stock market from a dynamic system point of view. Based on a feedback adaptation scheme, the authors model the movement of a stock market index within a framework that is composed of an internal dynamic model and an adaptive filter. The output-error model is adopted as the internal model whereas the adaptive filter is a time-varying state space model with instrumental variables. Its input-output behavior, and internal as well as external forces are then identified. Special attention has also been paid to the recent financial crisis by examining the movement of Dow Jones Industrial Average （DJIA） as an example to illustrate the advantage of the proposed framework. Supported by time-varying causality tests, five influential factors from economic and sentiment aspects are introduced as the input of this framework. Testing results show that the proposed framework has a much better prediction performance than the existing methods, especially in complicated economic situations. An application of this framework is also presented with focuses on forecasting the turning periods of the market trend. Realizing that a market trend is about to change when the external force begins to exhibit clear patterns in its frequency responses, the authors develop a set of rules to recognize this kind of clear patterns. These rules work well for stock indexes from US, China and Singapore.
