The impact of China's macroeconomic determinants on commodity prices

Zhang, Tianding; Du, Tianwen; Li, Jie

doi:10.1016/j.frl.2019.101323

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…This research is in line with [39,65], marked by global prices in local economic conditions. Still, it is found that the effect of exchange rates is more dominant than the level of international prices and the number of imports.…”

Section: Discussionsupporting

confidence: 87%

Differences in Local Rice Price Volatility, Climate, and Macroeconomic Determinants in the Indonesian Market

et al. 2021

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Rice is a staple food in most Asian countries, and food security is often viewed as having a rice supply to avoid a food crisis. The large number of people who have low incomes means there is a risk of household economic conditions being threatened when food prices are not controlled. We present a different approach by measuring rice price volatility at the local market level with climate and macroeconomic variables. The model succeeds in representing the variables that affect rice prices by an average of 75% nationally. The influence of macroeconomic variables and climate dynamics was found to have a non-uniform pattern from one region to another. The factors that dominate the volatility of rice prices in each province were different. To help address these problems, it is suggested to model and integrate climate data and macroeconomic variables. The output can be used to determine national policies for controllable factors and anticipate climate dynamics to reduce the risk that communities will have difficulty accessing food.

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Section: Discussionsupporting

confidence: 87%

Differences in Local Rice Price Volatility, Climate, and Macroeconomic Determinants in the Indonesian Market

et al. 2021

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“…A perturbation on inflation level will not necessarily have the expected impact on commodity price and can even lead to wide distortions. Zhang et al (2019) further show that PPI has a negative effect on commodity prices in China. As we have seen in Figure 1, most price bubbles do not occur during the historical high price periods.…”

Section: Resultsmentioning

confidence: 83%

Price bubbles in agricultural commodity markets and contributing factors: evidence for corn and soybeans in China

Mao¹,

Ren

Loy³

2020

CAER

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PurposeThe purpose of this paper is to detect the existence of price bubbles and examine the possible contributing factors that associate with price bubble occurrences in China agricultural commodity markets.Design/methodology/approachUsing recently developed rolling window right-side augmented Dickey–Fuller test, we first detect the dates of price bubbles in China's two important agricultural commodity markets, namely corn and soybeans. Then, we use a penalized maximum likelihood estimation of a multinomial logistic model to estimate the contributing factors of price bubbles in both markets, respectively.FindingsResults from the bubble detection indicate that price bubbles account for 5.48% (3.91%) of the studied periods for corn (soybeans). More importantly, we find that market liquidity and speculation have opposite effects on the occurrences of bubbles in the corn and soybeans market. World stocks-to-use and exchange rates affect the occurrences of bubbles in a different way for each commodity, as well. Price bubbles are more likely associated with strong economic activity, high interest rates and low inflation levels.Originality/valueThis is the first study considering commodity-specific features into the formation of price bubbles. Through accurately identifying the bubble dates and fixing the estimation bias of rare events models, this study enables us to obtain robust results for each commodity. The results imply that China's corn and soybeans market respond differently to the speculative activity and external shocks from international markets. Therefore, future policy regulations on commodity markets should focus on more commodity-specific factors when aiming at avoiding bubble occurrences.

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“…Secondly, some of the research articles have explored linkages between macroeconomic fluctuations and stock indexes of the United states (Liang et al, 2020), Pakistan (Asad & Farooq, 2009;Tabash et al, 2020), Malaysia (Al-hajj et al, 2018Almansour et al, 2016), China (Fan et al, 2014;Huang et al, 2018;Lin & Chen, 2019;You et al, 2017;Zhang et al, 2019), Indonesia (Lentina Andriansyah & Messinis, 2019;Simbolon & Purwanto, 2018), Sri-Lanka (Wickremasinghe, 2011), Bangladesh (Choi et al, 2019) and no efforts have been made to explore Gold-Oil-Exchange rates and stock index nexus asymmetrically for Indian stock exchange.…”

Section: Research Gapsmentioning

confidence: 99%

“…Results have refuted EMH by establishing causal associations between selected macroeconomic variability and Sri-Lankan stock indexes for shorter and longer horizons. Zhang et al (2019) studied the macroeconomic volatility on commodity prices of china through employing the SVAR modeling approach. Findings suggested that variations in commodity prices can be explained by the fundamental macroeconomic variables of China.…”

Section: Linear and Nonlinear Impact Of Macroeconomic Volatility On Stock Indexesmentioning

confidence: 99%

Gold-oil-exchange rate volatility, Bombay stock exchange and global financial contagion 2008: Application of NARDL model with dynamic multipliers for evidences beyond symmetry

Asad

Tabash

Sheikh

et al. 2020

Cogent Business & Management

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The primary objective of this research article is to investigate the asymmetrical linkages between gold-oil-exchange rates and Bombay stock indexes by utilizing a nonlinear ARDL approach covering the period from April 2003 to May 2020. Time-series data is divided into three different types of regimes such as before the crisis regime, after the crisis regime, and over the entire period. Seasonality effects within the data series are identified through utilizing different types of unit root analysis such as Philips Peron (PP), augmented dickey-fuller test (ADF), and Kwiatkowski Philips Schmidt Shin (KPSS) test statistics followed by Zivot ABOUT THE AUTHORS Muzaffar Asad has completed his PhD in entrepreneurship from Malaysia and is now associated with the University of Bahrain as Assistant Professor. Previously, he has served Foundation University Islamabad as Associate Professor and the University of Central Punjab, Lahore as Assistant Professor. He is a renowned trainer of entrepreneurship and leadership.Mosab I. Tabash is the program director for Master of Business Administration at college of business, Al Ain University, UAE.Umaid A. Sheikh is an expert in econometrics and is having interest in exploring symmetrical and asymmetrical linkages between macroeconomic and stock indexes.

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The impact of China's macroeconomic determinants on commodity prices

Cited by 11 publications

References 14 publications

Differences in Local Rice Price Volatility, Climate, and Macroeconomic Determinants in the Indonesian Market

Differences in Local Rice Price Volatility, Climate, and Macroeconomic Determinants in the Indonesian Market

Price bubbles in agricultural commodity markets and contributing factors: evidence for corn and soybeans in China

Gold-oil-exchange rate volatility, Bombay stock exchange and global financial contagion 2008: Application of NARDL model with dynamic multipliers for evidences beyond symmetry

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