The predictability of stock market volatility in emerging economies: Relative roles of local, regional, and global business cycles

Bouri, Elie; Demirer, Rıza; Gupta, Rangan; Sun, Xiaojin

doi:10.1002/for.2672

Cited by 21 publications

(12 citation statements)

References 37 publications

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“…We use the Campbell and Thompson (2008) out‐of‐sample R 2 (

R_{OS}^{2}

) statistic to evaluate the predictive performance of the models. The

R_{OS}^{2}

statistic is widely employed by studies on financial predictability (see, e.g., Bouri et al, 2020; Jiang et al, 2019; Lin et al, 2018; Liu et al, 2021; Neely et al, 2014; Rapach et al, 2010; Rapach et al, 2016; Wang et al, 2018; Wang et al, 2019), and it measures the percent reduction in the MSFE of a model of interest relative to the MSFE of the benchmark model (i.e., the AR model in our case). Statistically,

R_{OS}^{2}

is computed as

R_{os}^{2} = 1 - \frac{\sum_{t = q}^{T - 1} {({italicLV}_{t + 1} - {\overset{LV}{true}}_{t + 1}^{M})}^{2}}{\sum_{t = q}^{T - 1} {({italicLV}_{t + 1} - {\overset{LV}{true}}_{t + 1}^{B})}^{2}},

where LV t +1 ,

{\overset{truê}{italicLV}}_{t + 1}^{M}

, and

{\overset{truê}{italicLV}}_{t + 1}^{B}

denote the actual volatility, the volatility forecast based on the model of interest, and the volatility forecast based on the benchmark model for the ( t + 1)th month, respectively.…”

Section: Methodsmentioning

confidence: 99%

Forecasting stock return volatility using a robust regression model

Hao

Zhang

et al. 2021

Journal of Forecasting

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This paper aims to accurately forecast stock return volatility based on a robust regression model. The robust regression model is developed by replacing the mean squared error (MSE) in the autoregressive (AR) model with the Huber loss function, and the resulting model is called the ARH model. The empirical results show that the ARH model displays significantly stronger predictive power than the AR benchmark model for different evaluation periods and forecasting horizons. From an asset allocation perspective, a mean–variance investor can obtain sizeable utility gains based on the volatility forecasts produced by the ARH model. Furthermore, we find that the superior performance of the ARH model comes from assigning small weights for the extreme values, which are mainly found during recessions and periods of high volatility. Finally, our results are robust to various settings.

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“…We use the Campbell and Thompson (2008) out‐of‐sample R 2 (

R_{OS}^{2}

) statistic to evaluate the predictive performance of the models. The

R_{OS}^{2}

R_{OS}^{2}

is computed as

R_{os}^{2} = 1 - \frac{\sum_{t = q}^{T - 1} {({italicLV}_{t + 1} - {\overset{LV}{true}}_{t + 1}^{M})}^{2}}{\sum_{t = q}^{T - 1} {({italicLV}_{t + 1} - {\overset{LV}{true}}_{t + 1}^{B})}^{2}},

where LV t +1 ,

{\overset{truê}{italicLV}}_{t + 1}^{M}

, and

{\overset{truê}{italicLV}}_{t + 1}^{B}

denote the actual volatility, the volatility forecast based on the model of interest, and the volatility forecast based on the benchmark model for the ( t + 1)th month, respectively.…”

Section: Methodsmentioning

confidence: 99%

Forecasting stock return volatility using a robust regression model

Hao

Zhang

et al. 2021

Journal of Forecasting

View full text Add to dashboard Cite

show abstract

“…Quantifying and analyzing the relationship between GCC financial markets and the global economy has received less attention in previous studies, which have already focused on developed countries (Laborda and Olmo, 2021;Umar et al, 2021;Mensi et al, 2022;Mensi et al, 2023) and, to a lesser extent, on large emerging economies (Balcilar et al, 2018;Bouri et al, 2020;Xie et al, 2020;Urom et al, 2022). It should also be noted that financial markets can be affected by a variety of factors during global economic crises, including oil prices (Lin and Wu, 2022;Mensi et al, 2015Mensi et al, , 2022Mensi et al, , 2023, geopolitical risks (GPRs) (Balcilar et al, 2018;Das et al,2019), uncertainty (Baker et al, 2016), commodity prices (Akinsola and Odhiambo, 2020), interest rate changes (Gu et al, 2022) and gold prices (Gokmenoglu and Fazlollahi, 2015;Arfaoui and Ben Rejeb, 2017).…”

Section: Introductionmentioning

confidence: 99%

Global hidden factors predicting financial distress in Gulf Arab states: a quantile–time–frequency analysis

Trabelsi

2023

JFEP

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Purpose This study aims to uncover the main predictors of financial distress in the Gulf Cooperation Council (GCC) countries using a wide range of global factors and asset classes. Design/methodology/approach This study uses novel approaches that take into account extreme events as well as the nonlinear behavior of time series over various time intervals (i.e. short, medium and long term) and during boom and bust episodes. This study primarily uses the conditional value at risk (CoVaR), the quantile multivariate causality test and the partial wavelet coherence method. The data collection period ranges from March 2014 to September 2022. Findings US T-bills and gold are the primary factors that can increase financial stability in the GCC region, according to VaRs and CoVaRs. More proof of the predictive value of the oil, gold and wheat markets, as well as geopolitical tensions, uncertainty over US policy and volatility in the oil and US equities markets, is provided by the multivariate causality test. When low extreme quantiles or cross extreme quantiles are taken into account, these results are substantial and sturdy. Lastly, after adjusting for the effect of crude oil prices, this study’s wavelet coherence results indicate diminished long-run connections between the GCC stock market and the chosen global determinants. Research limitations/implications Despite the implications of the author’s research for decision makers, there are some limitations mainly related to the selection of Morgan Stanley Capital International (MSCI) GCC ex-Saudi Arabia. Considering the economic importance of the Kingdom of Saudi Arabia (KSA) in the region, the author believes that it would be better to include this country in the data to obtain more robust results. In addition, there is evidence in the literature of the existence of heterogeneous responses to global shocks; some markets are more vulnerable than others. This is another limitation of this study, as this study considers the GCC as a bloc rather than each country individually. These limitations could open up further research opportunities. Originality/value These findings are important for investors seeking to manage their portfolios under extreme market conditions. They are also important for government policies aimed at mitigating the impact of external shocks.

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“…In recent years, economic policy uncertainty (EPU) has been one of the most debated topics among researchers , policymakers, and financial analysts due to its potential effect on various economic activities . Recent studies investigate the impact of economic policy uncertainty on stock market volatility , unemployment , real housing returns , and exchange rate fluctuations (Liu & Zhang, 2015 ; Caggiano et al, 2014 ; Beckmann & Czudaj, 2017 ; Bouri et al, 2020 ; Afzali et al, 2021 ; Yuan et al, 2022 ). As evident from past studies, a rise in EPU increases stock market volatility and results in declines in stock returns (Antonakakis e al., 2013 ; Christou et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Economic Policy Uncertainty and Emerging Stock Market Volatility

Ghani

2023

Asia-Pac Financ Markets

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This research examines the effect of economic policy uncertainty (EPU) indices on Pakistan's stock market volatility. Particularly, we examine the impact of the economic policy uncertainty index for Pakistan and bilateral global trading partner countries, the US, China, and the UK. We employ the GARCH-MIDAS model and combination forecast approach to evaluate the performance of economic uncertainty indices. The empirical findings show that the US economic policy uncertainty index is a more powerful predictor of Pakistan stock market volatility. In addition, the EPU index for the UK also provides valuable information for equity market volatility prediction. Surprisingly, Pakistan and China EPU indices have no significant predictive information for volatility forecasting during the sample period. Lastly, we find evidence of all uncertainty indices during economic upheaval from the COVID-19 pandemic. We obtained identical results even during the Covid-19. Our findings are robust in various evaluation methods, like MCS tests and other forecasting windows. Supplementary Information The online version contains supplementary material available at 10.1007/s10690-023-09410-1.

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The predictability of stock market volatility in emerging economies: Relative roles of local, regional, and global business cycles

Cited by 21 publications

References 37 publications

Forecasting stock return volatility using a robust regression model

Forecasting stock return volatility using a robust regression model

Global hidden factors predicting financial distress in Gulf Arab states: a quantile–time–frequency analysis

Economic Policy Uncertainty and Emerging Stock Market Volatility

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