| Sum [33] |
EU |
1985–11 |
PU, stock return |
VAR model with Granger-causality |
PU has a negative impact on stock market returns |
| Tang and Wan [34] |
China |
2005–2017 |
PU, stock price |
Panel regression with fixed effects and instrumental variables |
The positive relationship between PU and stock price informativeness is found, and unexpected PU is important. |
| Du, Sui [35] |
China |
2005–2017 |
PU, stock price crash risk |
Panel regression with fixed effects and two-stage least squares |
The increasing of PU will significantly increase stock price crash risk. Economic policy affects stock price crash risk mainly by influencing information asymmetry. |
| Ono [36] |
OECD and non-OECD countries |
1997–2019 |
PU, stock indices |
Lag-augmented VAR model with time-varying Granger causality test |
The causal relationship between the two variables is not present for the entire period, but it is present for some sub-periods in many countries. |
| Xu, Wang [37] |
China and US |
2000–2019 |
PU, stock market returns |
VAR model with impulse response function and variance decomposition analysis |
US PU has a significant negative impact on Chinese stock market returns in the short term but not in the long term. Chinese PU has a significant negative impact on US stock market returns in both the short term and the long term. |
| Cheng, Wang [38] |
China |
2009–2018 |
PU exposure, stock price bubbles |
Panel regression with fixed effects and instrumental variables |
There exists a significantly positive relationship between PU exposure and stock price bubbles. Retail trades play an important role in the transmission mechanism of PU exposure to stock price bubbles. |
| Liao, Hou [39] |
Western Balkan countries |
2006–2018 |
PU, stock returns |
GARCH models with dummy variables for PU shocks |
PU has a negative and significant impact on stock returns and a positive and significant impact on volatility |
| Fang, Bouri [40] |
Global |
2010–2016 |
Bitcoin returns and volatility, global PU |
GARCH models |
GEPU has a positive and significant impact on Bitcoin volatility and reduces the hedging effectiveness of Bitcoin against major currencies |
| Chen, Jiang [41] |
China |
2000–2014 |
PU, stock market expected returns |
Fama-French three-factor model and GARCH-M model |
PU has a negative and significant impact on stock market expected returns and a positive and significant impact on conditional volatility |
| Aydin, Pata [42] |
Brazil, Russia, India, China |
2003–2021 |
PU, stock prices |
Asymmetric and symmetric frequency domain causality tests |
There exists a significantly positive relationship between PU exposure and stock price bubbles. |
| Chiang [43] |
USA, EU, China, Japan |
1990–2018 |
US categorical PU, stock returns |
GED-GARCH model |
US policy uncertainty changes have a negative impact on markets in the US and its spillovers to the EU, China and Japan. |
| Balcılar, Demirer [44] |
GCC countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, UAE) |
2005–2013 |
Equity sector returns, global and regional PU |
VAR model with Cholesky decomposition for the impulse responses and variance decompositions |
PU has a negative and significant impact on equity sector returns; the impact varies across sectors and countries; the spillovers from global and regional PU are significant but asymmetric |
| Bannigidadmath and Narayan [45] |
18 emerging markets |
1990–2014 |
Stock returns, dividend yield, PU |
Predictive regressions with Newey-West standard errors and Hansen-Hodrick standard errors |
PU has a negative and significant impact on stock returns; the impact is stronger for countries with higher financial development and lower political risk |
| Das and Kumar [46] |
17 Emerging market and developed countries |
1998–2017 |
Domestic PU, US PU, Stock Prices |
Multiple and partial wavelet coherence techniques |
The study finds that the impact of both domestic and US PU are less significant in emerging markets than in developed markets. The study also finds that the stock prices in Canada and Australia are less sensitive to US PU. |
| Yang and Jiang [47] |
China |
1995–2014 |
PU, Stock Prices |
VAR and SVAR |
The study finds that PU and Chinese stock returns are negatively correlated, and this negative impact of PU on stock returns lasts about eight months after policy is announced |
| Antonakakis, Chatziantoniou [48] |
US |
1985–2013 |
VIX data, S7P500 Returns, Stock Prices |
Dynamic conditional correlation (DCC), univariate GARCH |
The findings suggest that except for the 2008 financial crisis, the relationship between PU and the stock returns is negative. |
| Li, Balcilar [49] |
India and China |
1995–2013 in China and 2003–2013 in India |
PU, Stock Prices |
bootstrap Granger full sample causality testing and sub-sample rolling window estimation |
The study finds a weak relationship that goes bidirectional for many sub-periods. |
| Škrinjarić and Orlović [50] |
Nine eastern European Countries |
2001–2019 |
PU, Stock Returns |
Rolling estimation of the VAR model and spillover indices |
The study finds that the stock markets of Lithuania, Slovenia, and the Czech Republic are more sensitive to PU shocks while the Bulgarian stock market is least sensitive. Other markets have individual reactions, according to the study. |
| Ehrmann and Fratzscher [51] |
Multi-Country |
1994–2004 |
US monetary policy and stock return |
OLS, and PCSE |
The study finds that there is a weak association between US monetary policy shocks and stock market returns of India, China, and Malaysia and strong between US monetary policy shocks and stock market returns of Korea, Turkey, Hong Kong, Indonesia, Sweden, Finland, Canada, and Australia. |
| Debata and Mahakud [52] |
India |
2003–2016 |
PU, stock market liquidity |
VAR, Granger causality tests, impulse response functions and variance decomposition analysis |
The empirical findings suggest that economic policy uncertainty moderately influences stock market liquidity during normal market conditions. However, the role of economic policy uncertainty for determining stock market liquidity is significant in times of financial crises. |
| Christou, Cunado [53] |
Australia, Canada, China, Japan, Korea and the US |
1998–2014 |
PU, Stock Returns |
Stochastic search specification selection, Bayesian panel VAR model |
The study finds that PU impacts the stock market returns negatively. The study also finds that PU in the US also impacts the stock markets of other countries negatively, except Australia |
| Dakhlaoui and Aloui [54] |
Brazil, Russia, India, China |
1997–2011 |
US PU, BRIC Stock Indices |
(i) the GARCH(1,1), (ii) the EGARCH(1,1) (exponential GARCH model) of Nelson (1991), (iii) the T-GARCH(1,1) (Threshold GARCH model), (iv) the TS-GARCH(1,1), and (v) the P-ARCH(1,1). |
The research reveals that the average return spillover from the BRIC stock indices to US uncertainty is unfavorable, while the spillover of volatility fluctuates between negative and positive values. |
