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Archives of Business Research – Vol. 12, No. 2

Publication Date: February 25, 2024

DOI:10.14738/abr.122.16548.

Chiang, T. C., & Chen, P.-Y. (2024). Evidence of Geopolitical Risk and Economic Policy Uncertainty on Equity Prices in Emerging

Markets. Archives of Business Research, 12(2). 105-128.

Services for Science and Education – United Kingdom

Evidence of Geopolitical Risk and Economic Policy Uncertainty

on Equity Prices in Emerging Markets

Thomas C. Chiang

Drexel University, Department of Finance

Pei-Ying Chen

College of Finance, Feng Chia University

No.100, Wenhwa Road, Taichung City, Taiwan, R.O.C.407224

ABSTRACT

This paper presents evidence of the impact of changes in downside risk(ΔVaRt),

economic policy uncertainty (∆EPUt

), the geopolitical risk (ΔGPRt

) in six Asian

equity markets while controlling for COVIDE-19. Applying the month data for the

period of 1990.01– 2021.12 to Asian markets, evidence shows that national stock

returns are negatively correlated with ΔVaRt

, ∆EPUt ∆GPRt and ∆GPRt

2

, suggesting

that these risk/uncertainty factors harmfully affect stock markets. Evidence shows

that a rise in U.S. geopolitical risk adversely spillovers to Asia stock markets.

Spreads of COVID-19 damage local stock returns via interacting with changes in EPU

and GPR. Purpose: Present evidence of COVIDE-19 and the impact of changes in

downside risk(ΔVaRt), economic policy uncertainty (∆EPUt

), the geopolitical risk

(ΔGPRt

) with different forms of nonlinear specifications in six Asian equity markets.

Design/methodology/approach: The rolling regression method is used to derive

time-varying coefficients of investors’ response to changes of policy uncertainty,

which is sensitive to react to other uncertainty variables. Findings: Evidence shows

that national stock returns are negatively correlated with ΔVaRt

, ∆EPUtand∆GPRt

2

,

suggesting that these risk/uncertainty factors adversely affect local stock markets.

In addition, the evidence shows that a rise in U.S. geopolitical risk (∆GPRt

2,US) can

adversely spillover to domestic stock markets. A spillover effect is found from

COVID_19 development. Research limitations/implications: The findings are based

on data from six Asian markets. More data may be collected, which would expand

the analysis to a broader coverage of the global market. Practical implications:

Evidence suggests that in addition to the risk from financial markets, the

geopolitical risk (GPR) and COVID-19 are important factors in determining whether

a risk premium should be received. From a policymaker’s point of view, geopolitical

stability is the foundation for maintaining financial market stability.

Originality/value: This paper presents evidence on the time-varying behavior of

investors’ response to ∆EPUt

. In addition to ∆EPUt and the ΔGPRt

, the nonlinear

components of ∆GPRt

2

, U.S.∆GPRt

2,US and interaction to COVID-19 have a harmful

effect on national stock prices in Asian markets.

Keywords: Downside risk, Economic policy uncertainty, Geopolitical risk, Safe haven,

Asset pricing, Asian markets, JEL Classification: G10, G11, G14, G15

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Archives of Business Research (ABR) Vol. 12, Issue 2, February-2024

Services for Science and Education – United Kingdom

INTRODUCTION

The mean-variance approach has been one of most crucial relations in analyzing capital

investments. The attractiveness of this model lies in its simplicity in focusing on two key

parameters: return and variance (risk). An advance of this approach is due to Merton’s

(1973,1980) Intertemporal Capital Asset Pricing model (ICAPM), which implies a positive risk- return tradeoff relation in stock markets. Studies from French, Schwert, and Stambaugh (1987),

Ghysels, Santa-Claraand Valkanov (2005), Kinnunen (2014), and Cederburg and O’Doherty

(2019) provide supporting evidence on the positive tradeoff hypothesis. However, an extensive

literature review shows that the conditional volatility and conditional mean move in an

opposite direction as reported in the publications of Glosten, Jagannathan and Runkle (1993),

Lettau and Ludvigson (2001) and Brandt and Kang (2004). Therefore, the evidence on the issue

of risk-return relation displays mixed signs.

As noted by Guo and Whitelaw (2006), the conventional approach in conducting empirical

analysis is likely to miss a component for the changes in investment opportunities that are

expounded in Merton’s (1973) model. It follows that some of the model specifications are likely

to commit an omitted variable problem. The researchers thus have reoriented their direction

by focusing on the impact of economic policy uncertainty (EPU) on stock prices. Bloom (2009,

2014) and Baker, Bloom and Davis (2016) and Chiang (2019a) demonstrate that heightened

EPU will impede economic activity and delay business investment decision, which would

produce a harmful impact on stock prices. The studies by Caggiano, Castelnuovo and Groshenny

(2014), Li (2017), Christou, Cunado, Gupta and Hassapis (2017) and Chiang (2020) provide

supporting evidence for the impact of EPU on the stock returns.

Despite the researchers’ positive contributions, their attention is placed on the risk associated

with financial sources. However, it is well recognized that geopolitical stability is the foundation

for maintaining financial market stability. Investors tend to pay attention to information that is

revealed in the daily news associated with nuclear tensions, war threats, terrorist threats, etc.

All of these activities and their related uncertainties no doubt have a profoundly disturbing

investors’ psychology and, hence, have significant impact on investors’ behavior, which would

affect their judgment in making portfolio decisions. Along this line, Pástor and Veronesi (2013)

suggest that political uncertainty commands a risk premium. In their recent study of U.S.

political policy risk on world asset prices, Brogaard, Dai, Ngo, and Zhang et al. (2020) state that:

“political uncertainty can spillover from one country and influence the financial outcomes in

other countries”. Caldara and Iacoviello (2022) provide various components of measuring

geopolitical risk and conflict of related tensions.

The outbreak of COVID-19 has threatened human health and impeded economic activities since

the spring of 2020. Its ensuing damages can be seen in the significant financial losses suffered

by many investors in a short period of time (Zhang et al., 2020). In response, governments have

provided assistance, by using fiscal programs and easing monetary supplies. These government

interventions, however, can induce further policy uncertainty that interacts with the often

rising and erratic spread of coronavirus infections. Thus, an investigation of stock returns and

policy uncertainty needs to factor into the interacting effects of EPU and GPR with changes in

COVID-19 infections.

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Chiang, T. C., & Chen, P.-Y. (2024). Evidence of Geopolitical Risk and Economic Policy Uncertainty on Equity Prices in Emerging Markets. Archives of

Business Research, 12(2). 105-128.

URL: http://doi.org/10.14738/abr.122.16548

This paper expands on the empirical studies of stock return behavior in several ways. First,

instead of using conditional volatility to model financial risk as a way of explaining stock market

variations, this study employs VaR as a measure of risk because of its information content,

which covers the variance and higher stock return moments in describing asset pricing. Second,

this study employs a news-based EPU index to describe stock market performance. Third, this

paper uses a news-based GPR index to capture the impact arising from military-tensions,

nuclear threats, or changes of governments that cause a harmful effect on stock prices. Fourth,

to factor in the global influence, this study also includes U.S. GPR in the test equation. This study

also incorporates the interacting effects of EPU and GPR with changes in COVID-19 infections.

Lagged dividend yield and time series patterns are used as control variables. Thus, the model

in this study is more inclusive because the arguments are not restricted to a single measure of

risk variable, an approach that is typically used in conventional studies.

The remainder of this paper is organized as follows. Section 2 provides a brief literature review

that leads to the specification of an econometric model for testing stock returns. Section 3

presents the testable hypothesis. Section 4 describes the data and variable definitions in the

empirical analysis. Section 5 presents empirical evidence for the test equation and extends the

analysis by applying rolling repression estimations. Section 6 tests the impact of uncertainty by

incorporating the C) VID-19 effect on stock returns. Section 7 concludes the empirical findings.

LITERATURE REVIEW

Empirical analysis of risk-return relation usually employs a regression model by relating stock

return to a measure of risk. French et al. (1987) pioneers this research and demonstrate the

(excess) stock return is positively correlated with the conditional variance or standard

derivation. Tests of the null hypothesis performed by Ghysels et al. (2005), (2010), Bali and

Engle (2010) and Chiang, Li and Zheng (2015) that relate the conditional mean of stock returns

to the conditional variance and find evidence of a positive and statistically significant relation.

However, Nelson (1991), Glosten et al. (1993), Brandt and Kang (2004) and Ang, Hodrick, Xing

and Zhang (2006) test the same hypothesis and fail to find supporting evidence.

The cause of these mixed signs stems from a number of reasons, but most noticeable are the

measure of risk variable and a possible missing variable. Focusing on the characteristic that

conditional variance models fail to deal with the extreme tail risk has prompted researchers to

shift their attention and consider downside risk as measured by using Value-at-Risk (VaR) in

their approach. Bali, Demirtas and Levy (2009) report that downside risk can significantly

predict stock returns in the U.S. market; Chen, Chiang, and Härdle (2018) document that stock

returns are positively correlated with the lagged downside risk in G7 markets, and Chiang

(2019b) uncovers evidence that lagged downside risk presents a significant predictive power

in Chinese stocks.

A shortcoming of using historical information to construct a risk variable is based on a premise

of parametric stability, which usually fails to capture updated information. A news-based index

such as an EPU index, however, appears to more effectively reveal on-going disruptive events

that could affect decision-making. Following the studies by Bloom (2009, 2014), Baker et al.

(2016), we employ a news-based EPU index that highlights three words: {Economic, Policy,