Abstract:Measuring economic uncertainty is extremely important for evaluating its role in economic activity. Nevertheless, measuring uncertainty is a difficult task since we do not know when economic agents perceive uncertainty and which type of uncertainty affects them. This paper introduces the economic uncertainty related queries (EURQ) index, computed for both the USA and Italy, which measures economic, political and normative uncertainty through large‐scale searches on the internet. We show that the EURQ index cap… Show more
“…(9) US economic uncertainty related queries (EURQ): EURQ reflects the uncertainty which are similar to GEPU and EPU, but it constructed in a different way from GEPU and EPU. As discussed by Bontempi et al ( 2021 ), it affects stock market price volatility with similar channels to GEPU and EPU. 4 Fig.…”
Section: Datamentioning
confidence: 90%
“…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.…”
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.
“…(9) US economic uncertainty related queries (EURQ): EURQ reflects the uncertainty which are similar to GEPU and EPU, but it constructed in a different way from GEPU and EPU. As discussed by Bontempi et al ( 2021 ), it affects stock market price volatility with similar channels to GEPU and EPU. 4 Fig.…”
Section: Datamentioning
confidence: 90%
“…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.…”
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.
“…Google search volumes and uncertainty Our work is most closely connected to the growing literature that employs Google Trends data to study the relationship between investors/consumers sentiment and macroeconomic/financial dynamics. Bontempi et al (2021), for instance, use the frequency of internet searches to build an index of economic policy uncertainty (EURQ). In line with the seminal work of Baker et al (2016), they rely on internet search intensity for 183 policy-relevant terms.…”
Section: Related Literaturementioning
confidence: 99%
“…In line with the seminal work of Baker et al (2016), they rely on internet search intensity for 183 policy-relevant terms. Bontempi et al (2021) show that their Google Trends-based index captures people's interest to acquire more information on one or more topics. Hence, EURQ can serve as a proxy for the level of perceived economic uncertainty.…”
Section: Related Literaturementioning
confidence: 99%
“…Thus, retrieving SVIs for more than one search term or topic with the same query, it is possible to compare the distribution and the average search volume of multiple keywords.7 An example of the employed vertical aggregation is provided in the Appendix (see Table7).8 Note that SVIs retrieved from Google Trends have some drawbacks. In particular, Google SVIs for the same search downloaded at different points in time may differ(Bontempi et al 2021;Da et al 2011;Donadelli and Gerotto 2019;Shields and Tran 2019). In the spirit ofDonadelli and Gerotto (2019), we fix this issue by downloading the same SVI several times across different days.…”
We propose a novel index of global risks awareness (GRAI) based on the most concerning risks—classified in five categories (economic, environmental, geopolitical, societal, and technological)—reported by the World Economic Forum (WEF) according to the potential impact and likelihood occurrence. The degree of public concern toward these risks is captured by Google search volumes on topics having the same or similar wording of that one of the WEF Global Risk Report. The dynamics of our GRAI exhibits several spillover episodes and indicates that concerns on the five different categories of global risks are—on average—highly interconnected. We further examine the interconnection between global risks perceptions and the macroeconomy and find that concerns on economic-, geopolitical-, and societal-related risks are net shock transmitters, whereas the macroeconomic variables are largely net receivers. Finally, we perform standard cross-sectional asset pricing tests and provide evidence that rising interconnection among global risks awareness commands a positive and statistically significant risk premium.
This study proposes a new high-frequency indicator to measure economic policy uncertainty in the context of India, a large emerging market economy. Based on internet search intensity data, the proposed index tends to peak around domestic and global events associated with uncertainty that may prompt economic agents to alter their decisions to spend, save, invest and hire. Using an external instrument with structural vector autoregression (SVAR-IV) framework, we provide fresh evidence on the causal impact of uncertainty on the Indian macroeconomy. We show that surprise increases in uncertainty lead to a fall in output growth and an increase in inflation. This effect is found to be mainly driven by a fall in private investments vis-à-vis consumption indicating a dominant supply-side impact of uncertainty. Lastly, taking the case of output growth, we show that adding our uncertainty index to standard forecasting models leads to better forecasting accuracy compared to other alternate indicators of macroeconomic uncertainty.
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