Research on the Factors of Asset Prices in the Inflation Mechanism

With the acceleration of economic globalization and financial liberalization,factors of asset prices such as stock,bond and real estate and so on become important economic variables that affect inflation. After a brief review of the latest literature,this paper analyzes the specific conduction mechanism from different aspects of consumption,investment,credit and exchange rate channels in which asset prices affect inflation. Then,this paper analyzes the monthly data from January,2002 to December,2013 with the PLS method(partial least squares regression method)and discusses whether a structural change has taken place in the inflation mechanism during this period. Finally,policy recommendations are provided.

Regulating and Controlling the Rising Prices of Geographical Indication Products in Inflation——taking Geographical Indication Products from Chongqing as an Example

Since July 2010,the prices of geographical indication products have risen with the occurrence of inflation.In contrast with other products,geographical indication products are distinct.The increasing prices of geographical indication goods have their own necessities.From the perspective of the products,they are equipped with appreciation potential due to the cultural pe culiarities.From the historical standpoint,their prices have not been high as well as other agricultural products.Realistically,the increase in their prices results from the costs of labors and raw materials.Therefore,the prices of geographical indication products rise to catch up.Not only are their prices at a reasonable level,but also they are beneficial to increase peasants'income.In con clusion,it is necessary that the strategies which are different from those of other products be adopted to regulate and control the prices of geographical indication products.

Inflationary effects of oil prices and domestic gasoline prices:Markov-switching-VAR analysis

The purpose of this study is to contribute to the literature by studying the effects of sudden changes both on crude oil import price and domestic gasoline price on inflation for Turkey, an emerging country. Since an inflation targeting regime is being carried out by the Central Bank of Turkey, determination of such effects is becoming more important. Therefore empirical evidence in this paper will serve as guidance for those countries, which have an in- flation targeting regime. Analyses have been done in the period of October 2005-December 2012 by Markovswitching vector autoregressive （MS-VAR） models which are successful in capturing the nonlinear properties of variables. Using MS-VAR analysis, it is found that there are 2 regimes in the analysis period. Furthermore, regime changes can be dated and the turning points of economic cycles can be determined. In addition, it is found that the effect of the changes in crude oil and domestic gasoline prices on consumer prices and core inflation is not the same under different regimes. Moreover, the sudden increase in gasoline price is more important for consumer price infla- tion than crude oil price shocks. Another finding is the presence of a pass-through effect from oil price and ga- soline price to core inflation.

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.

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.

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.

An Analysis of China's Overall Price Trend During the 12th Five-year Plan Period and Suggestions

In this paper,the author first summarizes China’s price operation situation and characteristics since the 1990s,and thenanalyzes inflation shaping factors and types.Based on that,the author raises six inflation early-warning indexes andfully dissects influencing factors of the overall price trend and inflation risks during the 12th Five-year Plan period.Afterthat,the author explains some aspects of price fluctuation that warrants attention during the 12th Five-year Plan period.Finally,the author puts forward policies and suggestions for stabilizing the overall price during the transformation of theeconomic development mode based on our actual situation.

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.

Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing

In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.

Influential Factors in the Econometric Modeling of the Price of Wheat in the United States of America

Wheat is a staple agricultural grain commodity used within the United States and is grown in nearly every state. Modeling the price of Hard Winter Red wheat (the most common type of wheat) is of extreme economic and social importance. The 2008 financial crisis had a drastic effect on the price of food in real terms, tightening household budgets and increasing the US percentage of citizen classed below the poverty line. Understanding the influential factors in the econometric modeling of the price of wheat allows for more effective governmental intervention and price stabilization. Results indicate that the price of wheat is influenced by a combination of 5 separate functions: “supply”, “demand”, “macroeconomic”, “climate” and “natural resource” related functions. These functions derive from a wide variety of different data sources. The functions were determined and then incorporated into an Ordinary Least Squares (OLS) regression model taking into account variable interaction, variable transformation and time. This regression exercise resulted in a good model, explaining just over 90% of the variation in the price of wheat. Yet, results indicate that the model though sensitive to sharp decreases in the price of wheat is insensitive to sharp increases in the price of wheat. Ideas are discussed of ways of improving the price model. These include the addition of other variables, such as financial speculation/increased use of climate related variables and the idea of using alternative statistical modeling techniques in place of robust OLS regression modeling, such as SVAR models and Spline GARCH models. This research implies that further research into the modeling of the price of wheat within the US has useful potential for a more productive outcome.

Empirical Analysis on the Influencing Factors of China’s Total Foreign Trade

On the occasion of the 40th anniversary of reform and opening up, China’s economic growth level has steadily risen and its international status has gradually improved. However, as the global economic downturn, China is facing tremendous challenges and pressures in the international economic and financial markets. The turbulence of foreign trade and international financial markets has put forward greater requirements for the flexibility of China’s financial strategy. Based on the annual data of China’s total foreign trade, exchange rate, commodity retail price index and the proportion of tertiary industry from 2002 to 2017, this paper constructs a multiple regression model to analyze the main influencing factors of China’s total foreign trade. The empirical results show that there is a positive relationship between the commodities retail price index and the total foreign trade, a negative relationship between the proportion of tertiary industry and the total foreign trade, and a positive relationship between the exchange rate of RMB (taking US dollar as an example) and the total foreign trade. On the basis of empirical analysis, the paper puts forward suggestions on relevant financial policies.

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.

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.

The Relationship between the Ethiopian Economic Growth and the Decrease in Purchasing Power of Consumers

There is no clear theory which states fixed relationship between inflation and growth.Controversy by quantity and institutional inflation theories also confirm this.According to quantity theorists,there is a long-run trade-off between inflation and economic growth but the supporters of institutional theory of inflation,are less sure about presence of negative relationship about inflation and growth.Thus,the relationship between inflation and economic growth is debatable both in the world and specifically to Ethiopia.Therefore,the objective of this critical review is to determine the relationship between the current status of the Ethiopian economy and the consumer price index by considering the economic development indictors and consumer price index.The nexus of inflation and economic growth is one of the most important macroeconomic policy problems that take the attention of researchers,policy makers and different scholars.Conducting this review will benefit developing countries by discovering what their current status is,as far as a person with a higher level of consumption is regarded as having a higher level of economic wellbeing than someone with a lower level of consumption.This study falls within the ambit of the pragmatism philosophical stance and exploratory in nature.This study applied the inductive method of reasoning and used secondary data.The Study found that there is a negative relationship between the Ethiopian economic growth and the purchasing power of consumer(consumer price index)synonymously measured by the inflation-macroeconomic growth trade-off.The review reveals that Ethiopian economy is highly growing and the consumer price index(purchasing power of consumers)is decreasing.This shows that the purchasing power consumer(consumer price index)in Ethiopia is not solely determined by the macroeconomic development,which in turn requires further investigation.It is recommended therefore that future research works will explore more on the relationship between the Ethiopian economic growth and the purchasing power of consumer or clearly can explore the effect of economic growth on the purchasing power of consumers(consumer price index).

A Precis of Inflation

Inflation may be described as a condition of substantially rising prices butthe measuring devices are inexact and questionable. When such conditions ex-ist,people lose purchasing power by saving money.Higher rate of inflationwould price a nation out of world markets and workers would lose jobs as thelogical consequence.Inflation is disruptive to people under such uncertainty.

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.

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。