Commodity price volatility and U.S. monetary policy: Commodity price overshooting revisited

Siami‐Namini, Sima; Hudson, Darren; Trindade, A. Alexandre; Lyford, Conrad P.

doi:10.1002/agr.21564

Cited by 12 publications

(10 citation statements)

References 32 publications

(31 reference statements)

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“…With respect to Brueckner et al (2012), we considered crude oil price as one of the control variables. We assumed that changes in the crude oil price have direct effects on global commodity prices and indirect effects on the exchange rates as these commodities are traded in terms of US dollars (Siami-Namini et al , 2019; Siami-Namini, 2019). We employed data on openness to international trade (% GDP) following Acemoglu et al (2008).…”

Section: Methodsmentioning

confidence: 99%

The impacts of sector growth and monetary policy on income inequality in developing countries

Siami‐Namini

Hudson

2019

JES

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PurposeThe purpose of this paper is to explore the effect of growth in different sectors of the economy of developing countries on income inequality and analyze how inflation, as a proxy for monetary policy, makes a proportionate contribution for setting a binding national target for reducing income inequality. The paper examines the existence of a linear or nonlinear effect of inflation and sectoral economic growth on income inequality using a balanced panel data of 92 developing countries for the period of 1990–2014.Design/methodology/approachMethods section includes several steps as below: first, the functional form of the model using panel data for investigating the contribution of economic sectors in income inequality; second, to estimate the relationship between income inequality and sector growth: testing the Kuznets hypothesis; third, to estimate the relationship between inflation and income inequality base on general functional form of the model proposed by Amornthum (2004); fourth, a panel Granger causality analysis based on a VECM approach.FindingsThe statistically significant finding shows that first agricultural growth and then industrial growth have a dominate impact in reducing income inequality in our sample. But, the service sector growth has positive effects. The results confirm the existence of Kuznets inverted “U” hypothesis for industry growth and Kuznets “U” hypothesis for service sector growth. The findings show that sector growth and inflation affect income inequality in the long-run.Originality/valueThis research is an original paper which analyzes the effect of growth in different sectors of the economy of developing countries (agriculture, manufacturing and services sectors) on income inequality and test the Kuznets hypothesis in terms of sector growth and at the same time, examine the existence of a linear/nonlinear effect of inflation and sectoral economic growth on income inequality and test Granger causality relationship between income inequality and sector growth and inflation.

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Section: Methodsmentioning

confidence: 99%

The impacts of sector growth and monetary policy on income inequality in developing countries

Siami‐Namini

Hudson

2019

JES

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“…High ARCH parameters imply high short-run volatility, whilst high GARCH parameters indicate high long-run volatility returns (Siami-Namini et al, 2019). The results from the EGARCH model estimations clearly show that the volatility processes of the agricultural commodities return in question is dominated by the ARCH and GARCH effect for two-time periods, but the impact of ARCH and GRACH effect have increased for the post-crisis period.…”

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confidence: 86%

“…Overall, a negative return (or shocks) for the asymmetric effect parameter, γ i , shows a greater impact on future volatility than positive returns (shocks), and a positive sign shows that a positive shock has a higher impact on future volatility than negative shocks. In the other words, positive and negative shocks have asymmetric effects with positive shocks increasing volatility more than a negative shock (Siami-Namini et al, 2019). For example, corn and soybean oil has a non-significant negative and significant positive return for the pre-and post-crisis period, respectively.…”

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confidence: 99%

Volatility Transmission Among Oil Price, Exchange Rate and Agricultural Commodities Prices

Siami‐Namini¹

2019

AEF

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The aim of this article is to examine the interdependence relationship among the volatilities of crude oil price, U.S. dollar exchange rate, and a set of agricultural commodities prices. An autoregressive (AR) with an exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model or AR-EGARCH process and vector error correction model (VECM) approach was used on monthly data spanning from Jan 1986 to Dec 2005 as the pre-crisis period and from Jan 2006 to Nov 2015 as the post-crisis period. The results show that volatility in the agricultural commodity returns for most cases are affected by the volatility of the crude oil returns in the post-crisis period. Also, the volatility of the U.S. dollar exchange rate highly affects the agricultural commodities returns in the pre-crisis than the post-crisis periods. Furthermore, crude oil returns volatility does affect the U.S. dollar exchange rate volatility in the post-crisis period, which in turn affects the volatility of the agricultural commodities returns through changes in prices. The results of impulse response function (IRFs) are significant for most agricultural commodities volatility in the post-crisis period than the pre-crisis period.

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“…Using a time‐varying causal test, Zhang, Liu, Hang, and Yao (2018) obtained a conclusion that there was a causal relationship from money supply to commodity price fluctuations. García and Mejía (2018) and Siami‐Namini, Hudson, Trindade, and Lyford (2019) got a similar conclusion that monetary policy changes are closely related to commodity price fluctuations. International oil price fluctuations. Coronado, Rojas, Romero‐Meza, Serletis, and Chiu (2018) used the nonlinear Granger test to investigate the impact of international oil price fluctuations on US agricultural commodity prices.…”

Section: Literature Reviewmentioning

confidence: 67%

Large fluctuations of China's commodity prices: Main sources and heterogeneous effects

Lin

2020

Int. J Fin Econ

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Assuming that the economic variables obey the normal distribution, existing studies often use traditional mean models to explore commodity prices. In fact, the distribution of economic data is often skewed. When the economic data is left‐biased or right‐biased, the quantile regression can more fully characterize the distribution of economic variables, and thus obtain a comprehensive analysis result. Moreover, the coefficient estimates of the quantile regression are more robust than those in the ordinary least squares regression. Therefore, this paper uses the quantile regression method to investigate commodity price fluctuations. The results show that the money supply has a greater impact on commodity prices in the lower 10th and 10th–25th quantile groups. Among all quantile groups, the impact of international oil prices on commodity prices in the upper 90th quantile group is the highest. The effect of the exchange rate in the 25th–50th quantile group is greater than those in other quantile groups. However, the real economy has a minimal impact on the 25th–50th quantile group. Thus, the heterogeneous effects of these influencing factors should be taken into consideration when stabilizing commodity prices.

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Commodity price volatility and U.S. monetary policy: Commodity price overshooting revisited

Cited by 12 publications

References 32 publications

The impacts of sector growth and monetary policy on income inequality in developing countries

The impacts of sector growth and monetary policy on income inequality in developing countries

Volatility Transmission Among Oil Price, Exchange Rate and Agricultural Commodities Prices

Large fluctuations of China's commodity prices: Main sources and heterogeneous effects

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