Stock market volatility and jumps in times of uncertainty

Megaritis, Anastasios; Vlastakis, Nikolaos; Triantafyllou, Aggeliki

doi:10.1016/j.jimonfin.2021.102355

Cited by 46 publications

(23 citation statements)

References 56 publications

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“…( 2020 ) and Megaritis et al. ( 2021 ). The results indicate that the uncertainty information about the U.S. market could effectively impact the stock volatility in many international stock markets.…”

Section: Empirical Analysismentioning

confidence: 96%

“…Dissimilar to many studies that had investigated a single extant uncertainty indicator (Liu and Zhang 2015 ; Megaritis et al. 2021 ), we explored uncertainty from the equity market, investor, and economic policy levels. Thereafter, we constructed a composite UI based on the scaled principal component analysis (s-PCA) method that was introduced by Huang et al.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…Moreover, Megaritis et al. ( 2021 ) argued that the macroeconomic uncertainty sufficiently predicts the U.S. stock volatility. However, the foregoing mainly focused on low-frequency monthly data, even though it is crucial to consider the high-frequency (microcosmic) relationship between uncertainty and volatility.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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Uncertainty index and stock volatility prediction: evidence from international markets

et al. 2022

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This study investigates the predictability of a fixed uncertainty index (UI) for realized variances (volatility) in the international stock markets from a high-frequency perspective. We construct a composite UI based on the scaled principal component analysis (s-PCA) method and demonstrate that it exhibits significant in- and out-of-sample predictabilities for realized variances in global stock markets. This predictive power is more powerful than those of two commonly employed competing methods, namely, PCA and the partial least squares (PLS) methods. The result is robust in several checks. Further, we explain that s-PCA outperforms other dimension-reduction methods since it can effectively increase the impacts of strong predictors and decrease those of weak factors. The implications of this research are significant for investors who allocate assets globally.

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Section: Empirical Analysismentioning

confidence: 96%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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Uncertainty index and stock volatility prediction: evidence from international markets

et al. 2022

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“…COVID-19 has a dramatic impact on the investment and business environment in countries around the world, which in turn has huge impact on stock markets of various industries (Al-Awadhi et al, 2020 ). Among the recently proposed uncertainty indices, HCU can capture the level of uncertainty brought to the national economy by the occurrence of various public health events and various health care policies, and IDEMV can capture the level of uncertainty brought to stock market price volatility by the occurrence of various public health events, both of which generally have significant impact on the stock market, especially during COVID-19 (Bai et al, 2021 ; Megaritis et al, 2021 );…”

Section: Datamentioning

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%

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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Forecasting stock volatility with a large set of predictors: A new forecast combination method

Gong

Zhang

Zhao

et al. 2023

Journal of Forecasting

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This paper investigates how to improve the prediction accuracy of stock realized volatility using a large set of predictors. Exploiting normalized positive adjusted and significant statistic of predictor obtained from the in‐sample result as weight, we develop two simple and effective forecast combination methods. Using an array of 86 equity, bond, forex, futures, behavior, macroeconomic, and uncertainty predictors, we find that the proposed methodologies significantly improve stock realized volatility out‐of‐sample prediction performance relative to several extant forecast combinations. This result is robust for different individual forecast models, different dependent variables, and different out‐of‐sample periods. Furthermore, we explain that out‐of‐sample predictability varies significantly with changes in the number of predictors. And the existence of a strongly powerful volatility predictor affects this change.

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Stock market volatility and jumps in times of uncertainty

Cited by 46 publications

References 56 publications

Uncertainty index and stock volatility prediction: evidence from international markets

Uncertainty index and stock volatility prediction: evidence from international markets

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

Forecasting stock volatility with a large set of predictors: A new forecast combination method

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