Measuring uncertainty in the stock market

Chuliá, Helena; Guillén, Montserrat; Uribe, Jorge M.

doi:10.1016/j.iref.2016.11.003

Cited by 61 publications

(46 citation statements)

References 87 publications

(68 reference statements)

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“…In this regard, some mixed, primarily in-sample, international empirical evidence can be found in Antonakakis et al, (2013), Kang and Ratti (2013), Gupta et al, (2014), Bekiros, Gupta and Majumdar (2016), Brogaard and Detzel (2015), Chang et al, (2015), Chuliá et al, (2015), Jurado et al, (2015), Kang and Ratti (2015), Redl (2015), Bekiros, Gupta and Kyei (2016), Li et al, (2016), and Sum (2012c). All these above studies have related the own-country uncertainty with own-country stock returns.…”

Section: Introduction and Related Literaturementioning

confidence: 99%

The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea

Balcılar

Gupta

Kim

et al. 2019

International Review of Economics & Finance

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Highlights• We analyze the predictability of stock return and its volatility of Hong Kong, Malaysia and South Korea • We base on the measure of domestic and global economic policy uncertainties (EPU) • Linear Granger causality tests fail to find evidence of predictability from EPU • A nonparametric causality-in-quantiles test, however, finds strong evidence of causality • Nonparametric test is found to be more robust relative to the standard linear causality test. AbstractThis paper analyzes whether we can predict stock return and its volatility of Hong Kong, Malaysia and South Korea based on measures of domestic and global (China, the European Area, Japan, and the US) economic policy uncertainties (EPU). While, linear Granger causality tests fail to find evidence of predictability, barring the case of South Korean EPU predicting its own stock returns, when we use a nonparametric causality-in-quantiles test, strong evidence of causality is detected from the EPUs for stock return volatility of Malaysia, and both returns and volatility at certain parts of the conditional distributions for South Korea. There is no evidence of predictability from domestic and global EPUs for return and volatility of the Hong Kong stock market. Given the statistical evidence of nonlinearity in our data set, we consider the results from the nonparametric test as more robust relative to the standard linear causality test. * We would like to thank anonymous referees for many helpful comments. However, any remaining errors are solely ours.

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Section: Introduction and Related Literaturementioning

confidence: 99%

The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea

Balcılar

Gupta

Kim

et al. 2019

International Review of Economics & Finance

View full text Add to dashboard Cite

show abstract

“…Uncertainty is a latent variable, but, in order to quantify the impact of uncertainty on the macroeconomy, one requires ways to measure uncertainty. Besides the various alternative measures of uncertainty associated with financial markets (see , Giglio et al, (2016), and Dew-Becker et al, (2017) for detailed discussions of alternative measures), such as the implied-volatility indices (popularly called the VIX), realized volatility, idiosyncratic volatility of equity returns, corporate spread associated, a related strand in the literature has developed, primarily three broad approaches to quantify the effect of uncertainty on the economy: (1) The news-based approach proposed by Brogaard and Detzel (2015), Baker et al (2016), and Larsen (2017).4 F 4 The main idea behind this approach is to perform searches of newspapers for terms related to economic and policy uncertainty (EPU) and to use the results of this search to construct measures of uncertainty; (2) Mumtaz and Zanetti (2013), Mumtaz and Surico (2013), Alessandri and Mumtaz (2014), Carriero et al (2015, forthcoming), Jurado et al (2015), Ludvigson et al (2015), Mumtaz et al, (2016), Shin and Zhong (2016), Chuliá et al (2017), Mumtaz and Theodoridis (2017a, b) and Creal and Wu (forthcoming) recover measures of uncertainty from estimates of various types of small and large-scale structural models related to macroeconomics and finance. Specifically speaking, the uncertainty measure is the average time-varying variance in the unpredictable component of a large set of real and financial time-series, i.e., it attempts to capture the average volatility in the shocks to the factors that summarize real and financial conditions,5 F 5 and; (3) Bali et al (2015), Sekhposyan (2015, 2017), Rossi et al (2016), and Scotti (2016) construct measures of uncertainty based on dispersion of professional forecaster disagreement.…”

Section: Introductionmentioning

confidence: 99%

Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty

Gupta

Risse

et al. 2018

Journal of Macroeconomics

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developed measures of partisan conflict, geopolitical risks and news-based VIX (NVIX). 5 These authors indicate that although in a general sense uncertainty is defined as the conditional volatility of an unforecastable disturbance, the empirical literature has usually relied on, which in turn can pick up fluctuations that are actually predictable and hence, can erroneously be attributable to uncertainty. Thus, it is important to distinguish between uncertainty in a series and its conditional volatility, i.e., properly measuring uncertainty would require one to remove the forecastable component of the considered series before computing the conditional volatility. In this sense, uncertainty in a series is not necessarily equivalent to the conditional volatility of the raw

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“…Theoretical studies have developed optimal monetary policy rules, and empirical studies have investigated uncertainty as a potential driver of business cycles (see Taylor, 1993;Faia, 2008;Binici, Erol, Özlü, Ünalmış, 2013;Fendoğlu, 2014;Grimme, 2015;Chuliá, Guillén, Uribe, 2015). Taylor's (1993) optimum monetary policy rule is formulated on the premise that the United States (US) Federal Reserve Bank adjusts the policy rate in response to past inflation and the output gap (actual minus potential output).…”

Section: Introductionmentioning

confidence: 99%

Monetary Policy and Industrial Output in the BRICS Countries: A Markov-Switching Model

Kutu

Ngalawa

2017

Folia Oeconomica Stetinensia

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This paper examines whether the five BRICS countries share similar business cycles and determines the probability of any of the countries moving from a contractionary regime to an expansionary regime. The study further examines the extent to which changes in monetary policy affect industrial output in expansions relative to contractions. Employing the Peersman and Smets (2001) Markov-Switching Model (MSM) and monthly data from 1994.01-2013.12, the study reveals that the five BRICS countries have similar business cycles. The results further demonstrate that the BRICS countries' business cycles are characterized by two distinct growth rate phases: a contractionary regime and an expansionary regime. It can also be observed that the area-wide monetary policy has significantly large effects on industrial output in recessions as well as in booms. It has also been established that there is a high probability of moving from state one (recession) to state two (expansion) and that on average, the probabilities of staying in state 2 (expansion) are high for each of the five countries. It is, therefore, recommended that the BRICS countries should sustain uniform policy consistency (monetary policy), especially as they formulate and implement economic policies to stimulate industrial output.

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Measuring uncertainty in the stock market

Cited by 61 publications

References 87 publications

The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea

The role of economic policy uncertainties in predicting stock returns and their volatility for Hong Kong, Malaysia and South Korea

Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty

Monetary Policy and Industrial Output in the BRICS Countries: A Markov-Switching Model

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