Price linkage between Chinese and international nonferrous metals commodity markets based on VAR-DCC-GARCH models

Using VAR-DCC-GARCH model,the literature on commodity price was extended by exploring the co-movement between Chinese nonferrous metal prices and global nonferrous metal prices represented by the nonferrous metal prices from London Metal Exchange(LME).The results show that LME nonferrous metals prices still have a greater impact on Chinese nonferrous metals prices.However,the impact of Chinese nonferrous metals prices on LME nonferrous metals prices is still weak except for lead price.The co-movement of nonferrous metal prices between LME and China presents hysteretic nature,and it lasts for 7-8trading days.Furthermore,the co-movement between LME nonferrous metals prices and Chinese nonferrous metals prices has the characteristics of time-varying,and the correlation of lead prices between LME and China is the more stable than all other nonferrous metals prices.

Alternative techniques for forecasting mineral commodity prices

Forecasting mineral commodity(MC) prices has been an important and difficult task traditionally addressed by econometric, stochastic-Gaussian and time series techniques. None of these techniques has proved suitable to represent the dynamic behavior and time related nature of MC markets. Chaos theory(CT) and machine learning(ML) techniques are able to represent the temporal relationships of variables and their evolution has been used separately to better understand and represent MC markets. CT can determine a system's dynamics in the form of time delay and embedding dimension. However, this information has often been solely used to describe the system's behavior and not for forecasting.Compared to traditional techniques, ML has better performance for forecasting MC prices, due to its capacity for finding patterns governing the system's dynamics. However, the rational nature of economic problems increases concerns regarding the use of hidden patterns for forecasting. Therefore, it is uncertain if variables selected and hidden patterns found by ML can represent the economic rationality.Despite their refined features for representing system dynamics, the separate use of either CT or ML does not provide the expected realistic accuracy. By itself, neither CT nor ML are able to identify the main variables affecting systems, recognize the relation and influence of variables though time, and discover hidden patterns governing systems evolution simultaneously. This paper discusses the necessity to adapt and combine CT and ML to obtain a more realistic representation of MC market behavior to forecast long-term price trends.

Evaluation of the impact of commodity price change on mine plan of underground mining

Fluctuations in commodity prices should influence mining operations to continually update and adjust their mine plans in order to capture additional value under new market conditions. One of the adjustments is the change in production sequencing. This paper seeks to present a method for quantifying the net present value(NPV) that may be directly attributed to the change in commodity prices. The evaluation is conducted across ten copper price scenarios. Discrete event simulation combined with mixed integer programming was used to attain a viable production strategy and to generate optimal mine plans. The analysis indicates that an increase in prices results in an increased in the NPV from$96.57M to $755.65M. In an environment where mining operations must be striving to gain as much value as possible from the rights to exploit a finite resource, it is not appropriate to keep operating under the same mine plan if commodity prices alter during the course of operations.

Co‑movement of commodity price indexes and energy price index:a wavelet coherence approach

This research sheds light on the causal link between commodity price indexes,i.e.,the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index,in the global market,using wavelet coherence,Toda–Yamamoto causality,and gradual shift causality tests over the period 1992M1 to 2019M12.Findings from the wavelet power spectrum and partial wavelet coherence reveal that:(1)there was significant volatility in the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index between 2004 and 2014 at different frequencies;and(2)commodity price indexes significantly caused the energy price index at different time periods and frequencies.It is noteworthy that the outcomes of the Toda–Yamamoto causality and gradual-shift causality tests are in line with the results of wavelet coherence.

Evaluating the exchange rate and commodity price nexus in Malaysia: evidence from the threshold cointegration approach

This paper examines the long-and short-run dynamics of asymmetric adjustment between the nominal exchange rate and commodity prices,namely oil,palm oil,rubber,and natural gas prices,in Malaysia using monthly data from January 1994 to December 2017.The relationship between exchange rate and each commodity price is examined in terms of Engle-Granger and threshold cointegrations.The estimated results provide evidence of long-run threshold cointegration and show that the adjustments towards the long-run equilibrium position are asymmetric in the short run.Furthermore,this study finds evidence of a unidirectional causal relationship running from the nominal exchange rate to oil price in the long and short run using a spectral frequency domain causality application.There is also empirical evidence of bidirectional causality between the nominal exchange rate and palm oil price,rubber price,and natural gas price in the long and short run.Overall,the findings have significant implications for the current debate on the future of primary commodities in Malaysia.

Do the RMB exchange rate and global commodity prices have asymmetric or symmetric effects on China’s stock prices?

With the rapid expansion of the RMB exchange rate’s floating range,the effects of the RMB exchange rate and global commodity price changes on China’s stock prices are likely to increase.This study uses both auto regressive distributed lag(ARDL)and nonlinear ARDL(NARDL)approaches to explore the symmetric and asymmetric effects of the RMB exchange rate and global commodity prices on China’s stock prices.Our findings show that without considering the critical variable of global commodity prices,there is no cointegration relationship between the RMB exchange rate and China’s stock prices,and the coefficient of the RMB exchange rate is not statistically significant.However,when we introduce global commodity prices into the NARDL model,the result shows that the RMB exchange rate has a negative effect on China’s stock prices,that there indeed exists a long-run cointegration relationship among the RMB exchange rate,global commodity prices,and stock prices in the NARDL model,and that global commodity price changes have an asymmetric effect on China’s stock prices in the long run.Specifically,China’s stock prices are more sensitive to increases than decreases in global commodity prices.Thus,increases in global commodity prices cause China’s stock prices to decline sharply.In contrast,the same magnitude of decline in global commodity prices induces a smaller increase in China’s stock prices.

Dynamics of price cooperating model in commodity market

For the problem of price fluctuation in the commodity market, some ordinary differential equation models are proposed and the stability of equilibrium price is studied. In this paper, we develop a mathematic model for price cooperation with diffusion and lag. When the economic parameters satisfy some conditions, the existence and stability of periodic price are investigated.

Applications of nonferrous metal price volatility to prediction of China's stock market

The aim of the present work is to examine whether the price volatility of nonferrous metal futures can be used to predict the aggregate stock market returns in China. During a sample period from January of 2004 to December of 2011, empirical results show that the price volatility of basic nonferrous metals is a good predictor of value-weighted stock portfolio at various horizons in both in-sample and out-of-sample regressions. The predictive power of metal copper volatility is greater than that of aluminum. The results are robust to alternative measurements of variables and econometric approaches. After controlling several well-known macro pricing variables, the predictive power of copper volatility declines but remains statistically significant. Since the predictability exists only during our sample period, we conjecture that the stock market predictability by metal price volatility is partly driven by commodity financialization.

Empirical study of speculation roles in international copper price bubble formation

By using GARCH（1,1）-M and EGARCH（1,1）-M models, the relationships among funds speculation transaction, arbitrage transaction and the fluctuation of international copper future price were studied. The news impact curve of copper future price fluctuation respectively introduced funds speculation position and arbitrage position was given, and the result is consistent with the empirical study conclusion. The results show that investment funds are not the factor that causes copper future price fluctuation, but can reduce the copper future price fluctuation; the copper future price fluctuation is more sensitive to negative information, and ftmd speculative positions can reduce asymmetric effect of copper price fluctuation, while fimds arbitrage position influences less.

Hybridized Intelligent Neural Network Optimization Model for Forecasting Prices of Rubber in Malaysia

Rubber producers,consumers,traders,and those who are involved in the rubber industry face major risks of rubber price fluctuations.As a result,decision-makers are required to make an accurate estimation of the price of rubber.This paper aims to propose hybrid intelligent models,which can be utilized to forecast the price of rubber in Malaysia by employing monthly Malaysia’s rubber pricing data,spanning from January 2016 to March 2021.The projected hybrid model consists of different algorithms with the symbolic Radial Basis Functions Neural Network k-Satisfiability Logic Mining(RBFNN-kSAT).These algorithms,including Grey Wolf Optimization Algorithm,Artificial Bee Colony Algorithm,and Particle Swarm Optimization Algorithm were utilized in the forecasting data analysis.Several factors,which affect the monthly price of rubber,such as rubber production,total exports of rubber,total imports of rubber,stocks of rubber,currency exchange rate,and crude oil prices were also considered in the analysis.To evaluate the results of the introduced model,a comparison has been conducted for each model to identify the most optimum model for forecasting the price of rubber.The findings showed that GWO with RBFNN-kSAT represents the most accurate and efficient model compared with ABC with RBFNNkSAT and PSO with RBFNN-kSAT in forecasting the price of rubber.The GWO with RBFNN-kSAT obtained the greatest average accuracy(92%),with a better correlation coefficient R=0.983871 than ABC with RBFNN-kSAT and PSO with RBFNN-kSAT.Furthermore,the empirical results of this study provided several directions for policymakers to make the right decision in terms of devising proper measures in the industry to address frequent price changes so that the Malaysian rubber industry maintains dominance in the international markets.

Analysis of Price and Industry Dynamics of Sustainable and Specialty Coffee in Costa Rica

Volatility of commodity prices has affected dramatically the coffee industry in recent years, particularly small holder farmers. Differentiation of coffee through certification, such as sustainahility and quality attributes, has been proposed as a strategy for protection of the farmers against volatility in the international prices. This research paper evaluated three different models to explore the effectiveness of the differentiation strategies in protecting the farmer against price volatility in recent years, focusing on the case of Costa Rica. Evidence showed important differences in the price dynamics over time when comparing three groups of coffee.

Uniformity Analysis for Index of Retail Price

Using the Hodges Ajne testing method, the uniformity of China retail price index was tested. The result, that population is submitting to uniform distribution, was obtained. The uniformity of CRPI indicates that the general price level is stable in the Ninth Five Year Plan. Finally, the reasons causing the uniformity was analyzed.

Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model

With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.

Rare earth prices set to see more declines

Prices of rare earths are set to fall further in the next few months as oversupply and lower prices for other commodities hurt offtake, said experts. "Traders are selling their existing stocks as the State Reserve Bureau, which manages China's strategic stockpiles, did not purchase any rare earths in September and dampened expectations of higher prices," said Xu Ruoxu, an analyst with Shenwan Hongyuan Securities.

商品期货市场套期保值与价格发现功能关联机制研究——基于市场流动性与参与者结构视角

基于市场流动性与参与者结构视角,探究在商品期货市场发展中套期保值与价格发现功能关联机制,并使用2011年1季度—2022年4季度大连商品交易所12个品种的面板数据进行实证检验。结果表明:期货市场价格发现功能通过市场流动性中介促进套期保值功能,而参与者结构调节期货市场功能关联强度;农产品期货市场的套期保值功能发挥整体较强,工业品期货市场的功能关联较强。为此,建议期货市场在提高市场流动性的同时优化参与者结构,进一步促进期货市场功能协同发挥。

Research on the Impact of Aging and New Generation in the Popula­tion Structure on China’s Real Estate Price Volatility

To clarify the internal mechanism of the influence of the aging population and the new generation on housing prices is helpful to scientifically analyze and predict the trend of housing prices and the aging population and the new generation.This paper uses the intergenerational overlap model of the two periods as the theoretical basis,and uses the provincial panel data from 1998 to 2018 to study the impact of the elderly population and the new generation on the price fluctuations of commercial housing.The results of the study show that on the whole,both the aging population and the new generation have promoted the rise in commodity housing prices.However,the regional heterogeneity is significant.The aging population has the most significant impact on housing price increases in developed and general developed areas,and has no significant impact on housing price increases in other places.The new generation has a negative impact on housing prices in backward areas and a positive impact on housing prices in other areas.Looking further,using the ARIMA model to predict housing prices in the next 10 years,it is concluded that housing prices will show a slow upward trend in the next 10 years.Therefore,the government can ensure the stable development of the real estate market by revitalizing the second-hand housing market and implementing housing projects.

中国式现代化视角下中国铁矿石类大宗商品对外贸易定价权研究

改革开放以来中国经济发展迅速,逐渐步入工业化中后期阶段,对各类大宗商品的需求与日俱增。在国际市场,中国作为全球大宗商品重要的贸易国与消费国,缺乏对大宗商品定价权的影响力,受大宗商品国际价格波动影响较大,在国际大宗商品贸易中较为被动。基于国际铁矿石贸易市场的基本现状,以铁矿石为切入点,分析了当前中国在国际大宗商品的定价权,根据国内大宗商品进口贸易情况,探讨了在国际贸易中中国大宗商品定价权缺失的原因,并提出了相关对策建议。

经济政策不确定性、金融化与农产品期货价格

基于大宗商品金融属性视角,选取2014年1月至2023年10月的月度数据,构建了大宗商品金融化指数,采用BDS检验、DCC-MGARCH模型及TVP-VAR模型,对经济政策不确定性冲击下农产品期货价格的动态演变关系及其传导机制展开研究。研究表明:在面对经济政策不确定性冲击时,农产品期货价格表现出明显的非线性时变波动特征,其中大宗商品金融化是经济政策不确定性影响农产品期货价格波动的重要传导机制。

全球大宗商品价格异常波动对中国金融市场的影响及系统性风险防范机制研究

全球大宗商品价格异常波动会影响中国金融市场的稳定,加大系统性风险。在此背景下,建立有效的金融市场系统性风险防范机制尤为重要。首先,分析了全球大宗商品价格现状,从供需关系、国际政治经济因素、货币政策方面探讨了引发其波动的主要原因,并深入研究了全球大宗商品价格异常波动对中国金融市场系统性风险带来的影响。其次,围绕政策调控、金融机构风险管理和监管,分析了中国金融市场系统性风险的防范机制。最后,从完善政策调控体系、加强金融机构风险管理和监管等方面提出了优化措施,以有效应对全球大宗商品价格异常波动,提高中国金融市场系统性风险防范能力。

Pass-through Effects of Global Commodity Prices on China's Inflation:An Empirical Investigation

The present paper uses a two-step approach to estimate the pass-through effects of changes in international commodity prices and the RMB exchange rate on domestic consumer price inflation in China. We first estimate the pass-through effects of international commodity prices on producer prices and then estimate the pass-through effects of producer price inflation on consumer price inflation. We find that a l O-percent increase in international commodity prices would lead to China＇ s producer prices increasing by 1.2 percent 3 months later, which in turn would increase China＇ s domestic inflation by 0.24 percent over the same period. However, a 10-percent appreciation of the RMB exchange rate against the US dollar would help to reduce increases in producer prices by 4.4 percent over the following 3 months, which in turn would lead to a 0. 89-percent decline in consumer price inflation over the same period. Our findings suggest that appreciation of the RMB in an environment of rising global commodity prices and a weak US dollar could be an effective instrument to help contain inflation in China.