The spillover of macroeconomic uncertainty between the U.S. and China

Huang, Zhuo; Tong, Chen; Qiu, Han; Shen, Yupeng

doi:10.1016/j.econlet.2018.07.018

Cited by 44 publications

(33 citation statements)

References 11 publications

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“…Based on factor models with stochastic volatility, Mumtaz and Theodoridis (2015) and Berger et al (2016) decompose time-varying macroeconomic volatility into OECD wide and national macroeconomic uncertainty, in which they find a substantially more important role for the common component. Colombo (2013) and Huang et al (2018) investigate the spillover of uncertainty from the US to Euro area countries and China, respectively, and find the effect of US uncertainty on national macroeconomic aggregates to be many times larger than its country-own counterpart. Similar to Berger et al (2016) which investigates the expe-riences of 20 OECD countries, we find global uncertainty to be a major driver of macroeconomic performance, especially through investment and trade flows, rather than consumption.…”

Section: The Impact Of Uncertainty On Real Activitymentioning

confidence: 99%

“…Theoretically, foreign uncertainty shocks from major economies can transmit to small open economies through channels such as global banks or cross-border lending (Fernàndez-Villaverde et al 2011;Benigno et al 2012;Luk 2017). Empirically, cross-border effects of uncertainty shocks that originate from the US have been shown to be significant drivers of important economic variables for the UK (Mumtaz and Theodoridis 2015), Europe (Colombo 2013), China (Huang et al 2018), Hong Kong (Luk et al 2017) and Australia (Moore 2017), and in most cases, they even dominate the effects of local uncertainty shocks. Recent studies have also shown that global uncertainty, as measured by the common component of the variance of a large set of international financial and macroeconomic variables, can exert significant impacts on key macro and financial variables in developed countries (Berger et al 2016;Mumtaz and Theodoridis 2017;Carriero et al 2018a;Mumtaz and Musso 2018;Cuaresma et al 2019;Kang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty in an emerging market economy: evidence from Thailand

Apaitan¹,

Luangaram

Manopimoke

2021

Empir Econ

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This paper constructs various measures of domestic and global uncertainty and provides a comprehensive study of their impacts on the Thai economy. Based on a small open economy VAR, global uncertainty delivers deeper and more long-lasting effects when compared to within-country ones. In addition, we find that uncertainty shocks first generate sudden and large declines for stock prices and foreign portfolio investment, before gradually affecting the real economy through investment and trade channels. There is also meaningful heterogeneity among different types of domestic uncertainty. While financial uncertainty matters most for the Thai economy overall, consumption demand largely responds to macroeconomic uncertainty, while economic policy and political uncertainty generates the most persistent effects on investment. Furthermore, fiscal policy uncertainty is a key driver of trade flows while monetary policy uncertainty plays an important role for capital markets.We would like to thank Patcharaporn Leepipatpiboon and Chaitat Jirophat for excellent research assistance. We also appreciate insightful and constructive comments by two anonymous referees. The views expressed in this paper are of the authors and do not necessarily reflect those of the Bank of Thailand.

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Section: The Impact Of Uncertainty On Real Activitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty in an emerging market economy: evidence from Thailand

Apaitan¹,

Luangaram

Manopimoke

2021

Empir Econ

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“…For larger developing economies, the only references are Huang et al. ( 2018 ) for China and Godeiro and Lima ( 2017 ) for Brazil. As can be seen, there is only a handful of applications of the JLN macroeconomic uncertainty index for other countries.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring uncertainty: A streamlined application for the Ecuadorian economy

2021

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This paper develops a macroeconomic uncertainty index based on the multistage procedure that combines maximum likelihood and Bayesian estimation methods proposed by Jurado et al. (Am Econ Rev 105(3):1177–1216, 2015). Our approach streamlines the computation of the macroeconomic uncertainty index by specifying a state-space model estimated by maximum likelihood that allows us to obtain in one step the parameters of the model, the dynamic factors, and the forecast errors of the macroeconomic variables used to construct the index. Moreover, we estimate stochastic volatility models on the forecast errors also by maximum likelihood using a density filter that proves to be faster than a Bayesian estimation. After showing that our methodology produces reasonable results for the USA, we apply it to compute a macroeconomic uncertainty index for Ecuador, becoming the first index of this kind for a small developing or middle-income country. The results show that the Ecuadorian economy is more volatile and less predictable during recessions. We also provide evidence that macroeconomic uncertainty is detrimental to economic activity, finding that the responses of non-oil output, employment in the formal sector, and consumer prices to macroeconomic uncertainty shocks are sizable and persistent. Supplementary Information The online version contains supplementary material available at 10.1007/s00181-021-02069-5.

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“…We have identified the following four most important research gaps in the existing literature. Firstly, in existing literature number of research articles have only explored linear linkages between stock indexes, exchange rate fluctuations, oil price volatilities, and gold prices by utilizing linear models like VECM (Badry, 2019;Keswani & Wadhwa, 2018;Neveen, 2018;Rajesh, 2019;Sahu et al, 2014;Shiva & Sethi, 2015), VAR (Areli Bermudez Delgado et al, 2018;Ghulam, 2018;Huang et al, 2018), ARDL modeling approach (Ho, 2018;Singhal et al, 2019) and limited efforts have been made to explore the asymmetrical impact of Exchange rates-Gold-Oil price volatility on stock indexes by using NARDL model by (Shin et al, 2014).…”

Section: Research Gapsmentioning

confidence: 99%

“…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%

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 spillover of macroeconomic uncertainty between the U.S. and China

Cited by 44 publications

References 11 publications

Uncertainty in an emerging market economy: evidence from Thailand

Uncertainty in an emerging market economy: evidence from Thailand

Measuring uncertainty: A streamlined application for the Ecuadorian economy

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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