The impact of mixed-frequency geopolitical risk on stock market returns

Yang, Jianlei; Yang, Chunpeng

doi:10.1016/j.eap.2021.08.008

Cited by 39 publications

(16 citation statements)

References 41 publications

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“…Jeopolitik risklerin ülke borsaları üzerindeki etkilerinin getiri ve oynaklık boyutuyla araştırıldığı görülmektedir (Akdağ vd., 2018;Alqahtani vd., 2020;Oloko vd., 2021;ve Yang, 2021;Salisu vd., 2022;Emsen, 2022). Akdağ vd.…”

Section: International Journal Of Economic and Administrative Studiesunclassified

Comparison of the Effect of Global and Turkish Geopolitical Risks on Bist Tourism Index

Vurur

ÖZDEMİR>

2023

Uluslararası İktisadi Ve İdari İncelemeler Dergisi

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Çalışmada küresel ve Türkiye jeopolitik risklerin BİST Turizm endeksi üzerinde etkilerinin olup olmadığı ve hangisinin etkisinin daha fazla olduğu araştırılmaktadır. Bu amaçla, Küresel jeopolitik risk (Küresel GPR), Türkiye jeopolitik riski (Türkiye GPR) ve BİST Turizm endeksi arasındaki ilişki Engle-Granger eşbütünleşme analizi ve FMOLS eşbütünleşme regresyon testi ile analiz edilmiştir. Analizde ilgili endekslerin Ocak 2000 ile Ağustos 2022 dönemine ait aylık veriler kullanılmıştır. Eşbütünleşme analizi sonuçları GPR endeksleri ile BİST Turizm endeksi arasında uzun dönem eşbütünleşme ilişkisi olduğunu göstermektedir. Regresyon testi bulguları küresel jeopolitik risklerin turizm endeksi üzerinde etkisinin daha fazla olduğunu göstermektedir. Küresel GPR’deki bir birimlik artış BİST Turizm endeksini % 4,45 oranında etkilemektedir. Türkiye’ye özgü jeopolitik riskler ile BİST Turizm endeksi arasında negatif bir korelasyon ilişkisi bulunmaktadır. Ancak regresyon testi bulgularında Türkiye GPR’sinin BİST Turizm endeksini olumsuz etkilediği söylenebilirse de etkileme düzeyi istatiksel olarak anlamlı bulunmamıştır

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Section: International Journal Of Economic and Administrative Studiesunclassified

Comparison of the Effect of Global and Turkish Geopolitical Risks on Bist Tourism Index

Vurur

ÖZDEMİR>

2023

Uluslararası İktisadi Ve İdari İncelemeler Dergisi

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“…(7) US equity market volatility index (EMV): EMV can reflect the uncertainty level in the price volatility of the US stock market and is directly related to the volatility of the S&P 500 index (Alqahtani et al, 2020b ; Yang & Yang, 2021 );…”

Section: Datamentioning

confidence: 99%

“…(STLFSI). All of these indicators are proved in the existing literatures to have significant impact on the price volatility of stock market, indicating that although not all of these indicators are directly related to the stock market, they all contain the important information that is significant enough to influence the stock market price volatility (Alqahtani et al, 2020b ; Arouri et al, 2016 ; Bai et al, 2021 ; Bekaert & Hoerova, 2014 ; Das et al, 2019 ; Dua & Tuteja, 2016 ; Gupta et al, 2014 ; He et al, 2021a ; Liang et al, 2020a ; Piñeiro-Chousa et al, 2022 ; Smales, 2017 ; Wang et al, 2020a ; Wen et al, 2022 ; Yang & Yang, 2021 ; Yu et al, 2018 ). Economic policy uncertainty indicators other than EMV and IDEMV capture the level of macroeconomic uncertainty caused by different types of economic policies or major events (Baker et al, 2016 ; Caldara & Iacoviello, 2018 ; Husted et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…Economic policy uncertainty indicators other than EMV and IDEMV capture the level of macroeconomic uncertainty caused by different types of economic policies or major events (Baker et al, 2016 ; Caldara & Iacoviello, 2018 ; Husted et al, 2018 ). In general, the release of economic policies or the occurrence of major events often lead to stock market price volatility by affecting real economic conditions (Al-Awadhi et al, 2020 ; Alqahtani et al, 2020b ; Arouri et al, 2016 ; Bai et al, 2021 ; Bontempi et al, 2021 ; He et al, 2021a ; Hoque & Zaidi, 2019 ; Megaritis et al, 2021 ; Wen et al, 2022 ; Yang & Yang, 2021 ; Yang et al, 2021 ), so exploring the predictive power of different economic policy uncertainty indicators is helpful for distinguishing the role of macroeconomic uncertainty caused by different types of economic policies or major events in driving stock market price volatility. Market sentiment is also closely related to stock market price volatility because it can reflect the investment intentions or expectations of market participants.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model

Liang

2022

Ann Oper Res

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This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.

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“…Notably, mixed‐frequency models have demonstrated their capacity to enhance the accuracy of forecasts across diverse economic sectors. This includes, for instance, GDP (Hubera & Rossinib, 2020), CPI (Schorfheide & Song, 2015), carbon emissions (Xu & Liao, 2022), and financial markets (Yang & Yang, 2021). In addressing the significance and superiority of mixed‐frequency models over uniform frequency models, it is essential to consider the wider literature on the subject.…”

Section: Introductionmentioning

confidence: 99%

Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs

You,

Huang,

Lee

2023

Journal of Forecasting

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It is widely recognized that the tourism industry is susceptible to crisis or natural disaster. Although some literature has studied the consequences of the crisis and disaster, there remains a lack of study on the effect of COVID‐19. Against this background, this paper investigates the tourist flow forecasting by adopting an advanced nonparametric mixed‐frequency vector autoregressions model using Bayesian additive regression trees. This is particularly suitable for forecasting the presence of extreme observations, for example, the COVID‐19 pandemic. We investigate tourism demand forecasting using a large number of predictors, including industrial production index, CPI, exchange rate, economic policy uncertainty, Google trends index, and COVID‐19 infection rate. The data used for this study relate to tourist flows in Chinese Hong Kong, Japan, and South Korea. Empirical study demonstrates that this novel model significantly outperforms the traditional mixed‐frequency vector autoregressions model to quarterly tourist flow forecasting. Therefore, this model can significantly enhance tourism forecast accuracy in the face of extreme events. This study contributes to the literature on tourism forecasting and provides policymakers with policy implications.

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The impact of mixed-frequency geopolitical risk on stock market returns

Cited by 39 publications

References 41 publications

Comparison of the Effect of Global and Turkish Geopolitical Risks on Bist Tourism Index

Comparison of the Effect of Global and Turkish Geopolitical Risks on Bist Tourism Index

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model

Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs

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