Artículos de revistas: "Economic policy uncertainty. Volatility. Media"

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Krol, Robert. "Economic Policy Uncertainty and Exchange Rate Volatility". International Finance 17, n.º 2 (junio de 2014): 241–56. http://dx.doi.org/10.1111/infi.12049.

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Liu, Li y Tao Zhang. "Economic policy uncertainty and stock market volatility". Finance Research Letters 15 (noviembre de 2015): 99–105. http://dx.doi.org/10.1016/j.frl.2015.08.009.

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3

Baker, Scott R., Nicholas Bloom y Steven J. Davis. "Measuring Economic Policy Uncertainty*". Quarterly Journal of Economics 131, n.º 4 (11 de julio de 2016): 1593–636. http://dx.doi.org/10.1093/qje/qjw024.

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Abstract We develop a new index of economic policy uncertainty (EPU) based on newspaper coverage frequency. Several types of evidence—including human readings of 12,000 newspaper articles—indicate that our index proxies for movements in policy-related economic uncertainty. Our U.S. index spikes near tight presidential elections, Gulf Wars I and II, the 9/11 attacks, the failure of Lehman Brothers, the 2011 debt ceiling dispute, and other major battles over fiscal policy. Using firm-level data, we find that policy uncertainty is associated with greater stock price volatility and reduced investment and employment in policy-sensitive sectors like defense, health care, finance, and infrastructure construction. At the macro level, innovations in policy uncertainty foreshadow declines in investment, output, and employment in the United States and, in a panel vector autoregressive setting, for 12 major economies. Extending our U.S. index back to 1900, EPU rose dramatically in the 1930s (from late 1931) and has drifted upward since the 1960s.

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4

Cai, Dongliang, Tong Zhang, Kefei Han y Jingqi Liang. "Economic Policy Uncertainty Shocks and Chinese Stock Market Volatility: An Empirical Analysis with SVAR". Complexity 2022 (10 de octubre de 2022): 1–22. http://dx.doi.org/10.1155/2022/6944318.

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This paper proposes a framework for examining the interaction between stock market volatilities and economic uncertainty shocks, aiming to understand better the influence of economic uncertainty shocks on the Chinese stock market. The major empirical results include the followings. First, the economic policy uncertainty shocks push the Chinese stock volatility up, increasing the market risk. A 1-standard-deviation shock of economic policy uncertainty will enhance the stock volatility of the two composite indices by approximately 7% in 12 months. Second, the stock volatility reacted more intensely to fiscal and monetary economic policy uncertainty shocks, with a 1-standard-deviation shock that can enhance the stock volatility of the two composite indices by more than 10% in 12 months. Third, different stock indices exhibit different patterns of cumulative impulse responses, and the reaction of the volatility of the SSE real estate index to economic policy uncertainty shocks is more significantly intense than other indices. Besides, we have proved the robustness of empirical results by reestimating the models with a lag order of 2. Overall, our research results can provide policy and managerial insights for the sustainable development of the Chinese stock market and beyond.

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Balcilar, Mehmet, Rangan Gupta y Charl Jooste. "South Africa’s economic response to monetary policy uncertainty". Journal of Economic Studies 44, n.º 2 (8 de mayo de 2017): 282–93. http://dx.doi.org/10.1108/jes-07-2015-0131.

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Purpose The purpose of this paper is to study the evolution of monetary policy uncertainty and its impact on the South African economy. Design/methodology/approach The authors use a sign restricted SVAR with an endogenous feedback of stochastic volatility to evaluate the sign and size of uncertainty shocks. The authors use a nonlinear DSGE model to gain deeper insights about the transmission mechanism of monetary policy uncertainty. Findings The authors show that monetary policy volatility is high and constant. Both inflation and interest rates decline in response to uncertainty. Output rebounds quickly after a contemporaneous decrease. The DSGE model shows that the size of the uncertainty shock matters – high uncertainty can lead to a severe contraction in output, inflation and interest rates. Research limitations/implications The authors model only a few variables in the SVAR – thus missing perhaps other possible channels of shock transmission. Practical implications There is a lesson for monetary policy: monetary policy uncertainty, in isolation from general macroeconomic uncertainty, often creates unintended adverse consequences and can perpetuate a weak economic environment. The tasks of central bankers are incredibly difficult. Their models project output and inflation with relatively large uncertainty based on many shocks emanating from various sources. It matters how central bankers react to these expectations and how they communicate the underlying risks associated with setting interest rates. Originality/value This is the first study that looks into monetary policy uncertainty into South Africa using a stochastic volatility model and a nonlinear DSGE model. The results should be very useful for the Central Bank as it highlights how uncertainty, that they create, can have adverse economic consequences.

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Ma, Feng, Yangli Guo, Julien Chevallier y Dengshi Huang. "Macroeconomic attention, economic policy uncertainty, and stock volatility predictability". International Review of Financial Analysis 84 (noviembre de 2022): 102339. http://dx.doi.org/10.1016/j.irfa.2022.102339.

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Yu, Miao y Jinguo Song. "Volatility forecasting: Global economic policy uncertainty and regime switching". Physica A: Statistical Mechanics and its Applications 511 (diciembre de 2018): 316–23. http://dx.doi.org/10.1016/j.physa.2018.07.056.

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Cai, Yuxin. "A Study of the Impact of Economic Policy Uncertainty in the US and China on the Volatility of the RMB Exchange Rate". Advances in Economics and Management Research 3, n.º 1 (30 de diciembre de 2022): 77. http://dx.doi.org/10.56028/aemr.3.1.77.

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This paper examines both theoretically and empirically how RMB exchange rate volatility will be affected by China and US economic policy uncertainty. It finds that RMB exchange rate volatility increases when economic policy uncertainty increases in China and the US; Besides, market uncertainty has a significant positive impact on RMB exchange rate volatility. The findings of this paper can provide useful references for financial regulation and forecasting the role of policy.

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Wang, Li, Huimin Wang y Jian Wang. "Research on the Influence of Economic Policy Uncertainty on the Supply Chain Finance". E3S Web of Conferences 214 (2020): 03004. http://dx.doi.org/10.1051/e3sconf/202021403004.

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Based on the background of continuous increasing external economic uncertainty, this paper builds GARCH-MIDAS model to explore the volatility of copper price caused by global economic policy uncertainty in copper supply chain finance and analyzes the changes of refined copper supply and demand caused by this volatility. It is found that the increases of economic policy uncertainty will enhance the long- term volatility of copper. Moreover, the violent fluctuation of copper price caused by the impact of powerful economic policy uncertainty will weaken the demand confidence of refined copper market and lead to the phenomenon of oversupply. On the contrary, the moderate fluctuation of copper price due to the impact of weak economic policy uncertainty will boost the demand confidence of refined copper market and lead to the phenomenon of short supply.

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Wu, Xinyu, Tianyu Liu y Haibin Xie. "Economic Policy Uncertainty and Chinese Stock Market Volatility: A CARR-MIDAS Approach". Complexity 2021 (30 de septiembre de 2021): 1–10. http://dx.doi.org/10.1155/2021/4527314.

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Intraday range (the difference between intraday high and low prices) is often used to measure volatility, which has proven to be a more efficient volatility estimator than the return-based one. Meanwhile, a growing body of studies has found that economic policy uncertainty (EPU) has important impact on stock market volatility. In this paper, building on the range-based volatility model, namely, the conditional autoregressive range (CARR) model, we introduce the CARR-mixed-data sampling (CARR-MIDAS) model framework by considering intraday information to investigate the impact of EPU on the volatility of Chinese stock market and to explore the predictive ability of EPU for Chinese stock market. The empirical results show that both the China EPU (CEPU) and global EPU (GEPU) have a significantly negative effect on the long-run volatility of Chinese stock market. Furthermore, we find that taking into account the CEPU and GEPU leads to substantial improvement in the ability to forecast the volatility of Chinese stock market. We also find that the CEPU provides superior volatility forecasts compared to the GEPU. Our findings are robust to different forecasting windows.

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Li, Wei. "Forecasting Chinese stock market volatility under uncertainty". BCP Business & Management 26 (19 de septiembre de 2022): 1109–16. http://dx.doi.org/10.54691/bcpbm.v26i.2076.

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As the trend of world economic integration intensifies, global uncertainty also has a certain impact on the stock markets of various countries. To explore the volatility of China's stock market under uncertainty, we use a mixed-frequency data model and introduce uncertainty variables, namely the Global Economic Policy Uncertainty Index (GEPU), the United States (US EPU) and China (Chia EPU) economic policy uncertainty indices, the implied volatility index (VIX) and the geopolitical risk index (GPR), to build an extended GARCH- MIDAS model to analyse the impact of uncertainty indices on Chinese stock market volatility. The empirical results show that, except for the US EPU index, all other uncertainty indices have some impact on the Chinese stock market, and the out-of-sample forecasting results indicate that the introduction of these variables improves the forecasting effect of the model, with China's economic policy uncertainty index showing significant advantages in forecasting both weekly and monthly volatilities.

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12

Shaikh, Imlak. "On the Relationship between Economic Policy Uncertainty and the Implied Volatility Index". Sustainability 11, n.º 6 (18 de marzo de 2019): 1628. http://dx.doi.org/10.3390/su11061628.

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This article examines the effects of economic policy uncertainty (EPU) on the implied volatility index. The implied volatility index of various markets has been analyzed in relation to scheduled macroeconomic announcements, such as EPU and equity market policy uncertainty (EMPU) indices. The study highlights that EPU contains important information to explain the diverse market effects of the U.S., which is gauged into the volatility index. Estimates obtained in an autoregressive conditional heteroscedasticity framework indicate the persistence of volatility during spikes in the EPU. More importantly, the lagged values of the policy uncertainty index also contains market-related information to explain the markets’ future volatility. Major political and economic events have also contributed positively in that a presidential election contains information to explain various asset classes. Commodities, such as crude oil, gold, corn, and soybean, have been impacted significantly followed by EPU. Moreover, interest rate market volatility has also been moved adversely due to tight monetary policy. The Markov regime switching regression manifests that the implied volatility index (VIX) behaves abruptly in two different regimes followed by EPU.

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13

Paule-Vianez, Jessica, Camilo Prado-Román y Raúl Gómez-Martínez. "Economic policy uncertainty and Bitcoin. Is Bitcoin a safe-haven asset?" European Journal of Management and Business Economics 29, n.º 3 (11 de marzo de 2020): 347–63. http://dx.doi.org/10.1108/ejmbe-07-2019-0116.

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PurposeThe goal of this work is to determine whether Bitcoin behaves as a safe-haven asset. In order to do so, the influence of Economic Policy Uncertainty (EPU) on Bitcoin returns and volatility was studied.Design/methodology/approachIt is evaluated whether, when compared with the evolution of EPU, Bitcoin's returns and volatility show behaviours typical of safe havens or rather, those of conventional speculative assets. When faced with an increase in EPU, safe havens – such as gold – can be expected to increase their returns and volatility, while conventional speculative assets will increase their volatility and reduce their returns. This study uses simple linear regression and quantile regression models on a daily data sample from 19 July 2010 to 11 April 2019, to analyse the influence of EPU on the returns and volatility of Bitcoin and gold.FindingsBitcoin's returns and volatility increase during more uncertain times, just like gold, showing that Bitcoin acts not only as a means of exchange but also shows characteristics of investment assets, specifically of safe havens. These findings provide useful information to investors by allowing Bitcoin to be considered as a tool to protect savings in times of economic uncertainty and to diversify portfolios.Originality/valueThis study complements and expands current research by aiming to answer the question of whether Bitcoin is a simple speculative asset or a safe haven. The most significant contribution is to show that Bitcoin is not a mere speculative asset but behaves like a safe haven.

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KOCAARSLAN, Barış. "VOLATILITY TRANSMISSION BETWEEN US ECONOMIC POLICY UNCERTAINTY AND BIST (BORSA ISTANBUL) MAJOR SECTOR INDICES". Business & Management Studies: An International Journal 8, n.º 3 (25 de septiembre de 2020): 3221–38. http://dx.doi.org/10.15295/bmij.v8i3.1572.

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The significant effects of global economic policy uncertainties on world markets have been revealed in the related literature recently. The primary purpose of this study is to examine the volatility interaction (the causality in variance relationship) between uncertainty in US economic policies and BIST (Borsa Istanbul) major sector indices (financial, industrial, and technology indices). To satisfy this purpose, the causality in variance approach proposed by Hafner and Herwartz (2006) is utilized. The findings of the implemented volatility model show that the US economic policy uncertainty and BIST (Borsa Istanbul) major sector indices are strongly influenced by long-term volatility. According to the main findings of the causality invariance test, it is observed that there are significant and robust volatility transmissions from the US economic policy uncertainty to the BIST significant sector returns (financial, industrial, and technology sector returns). The test findings indicate that the BIST significant sector returns are quite sensitive to shocks in the US economic policy uncertainty. The results of the analysis present considerable implications for market participants in terms of developing effective economic policies and constructing optimal portfolios.

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Aye, Goodness y Lydia Kotur. "Effect of economic policy uncertainty on agricultural growth in Nigeria". African Journal of Agricultural and Resource Economics 17, n.º 2 (30 de junio de 2022): 106–14. http://dx.doi.org/10.53936/afjare.2022.17(2).7.

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This study analysed the long- and short-run effect of economic policy uncertainty on agricultural growth in Nigeria. Annual data was collected from secondary sources and analysed using the autoregressive distributed lag (ARDL) model and the associated bounds test. The highest volatility was exhibited by monetary policy uncertainty (MPU) (2.522), followed by consumer price index (CPI) (1.968). The fiscal policy uncertainty had the lowest volatility (0.179). The result of the bounds test showed that economic policy uncertainty shares a long-run relationship with agricultural growth. The effect of economic policy uncertainty on agricultural growth in the long run is negative, with the coefficient of MPU, FPU and TPU being -0.004, -0.218 and -0.507 respectively. In the short run, the effects of all the economic policy uncertainty variables on agricultural growth and welfare are negative and significant, both in contemporary (current) and in lags. A stable economic policy encourages agricultural growth.

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Aye, Goodness y Lydia Kotur. "Effect of economic policy uncertainty on agricultural growth in Nigeria". African Journal of Agricultural and Resource Economics 17, n.º 2 (30 de junio de 2022): 106–14. http://dx.doi.org/10.53936/afjare.2022.17(2).7.

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This study analysed the long- and short-run effect of economic policy uncertainty on agricultural growth in Nigeria. Annual data was collected from secondary sources and analysed using the autoregressive distributed lag (ARDL) model and the associated bounds test. The highest volatility was exhibited by monetary policy uncertainty (MPU) (2.522), followed by consumer price index (CPI) (1.968). The fiscal policy uncertainty had the lowest volatility (0.179). The result of the bounds test showed that economic policy uncertainty shares a long-run relationship with agricultural growth. The effect of economic policy uncertainty on agricultural growth in the long run is negative, with the coefficient of MPU, FPU and TPU being -0.004, -0.218 and -0.507 respectively. In the short run, the effects of all the economic policy uncertainty variables on agricultural growth and welfare are negative and significant, both in contemporary (current) and in lags. A stable economic policy encourages agricultural growth.

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Adam, Norashikin, Noor Zahirah Mohd Sidek y Arshian Sharif. "The Impact of Global Economic Policy Uncertainty and Volatility on Stock Markets: Evidence from Islamic Countries". Asian Economic and Financial Review 12, n.º 1 (17 de enero de 2022): 15–28. http://dx.doi.org/10.18488/5002.v12i1.4400.

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This study investigates the impact of uncertainty and volatility on ten Islamic stock returns using monthly data for from 2011:M5 to 2021:M5. We rely on the continuous wavelet transform and wavelet coherence ratios, which allows decomposition of time series across time scales to investigate the causal relationship between stock market returns, economic policy uncertainty and volatility. Our results provide some interesting insights. First, economic policy uncertainty, in general, has a negative effect on the majority of Islamic stock returns, except for the Dow Jones Islamic Market (DJIM). Second, volatility has a significant positive impact on most of the Islamic stock returns in various countries. Third, both economic policy uncertainty and volatility have a greater impact on the Islamic stock returns post-COVID-19 outbreak. These results should assist investors to re-evaluate their portfolios to fully maximize the potentials of these Islamic stock markets. Policymakers could use these results to design policies to reduce economic policy uncertainty as well as to cushion the impact of externally generated uncertainties.

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Marcella Roring y Rita Juliana. "Uncertainty Volatility, Investment, And Cash Holding In ASEAN Countries". Jurnal Akuntansi 26, n.º 2 (31 de mayo de 2022): 176–91. http://dx.doi.org/10.24912/ja.v26i2.905.

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This study aims to show the effect of uncertainty on firms’ corporate investment and cash holding in five ASEAN countries, namely Malaysia, Indonesia, Singapore, Thailand, and Vietnam. The study was conducted using data from non-financial public listed firms in these five ASEAN countries during the 2006 to 2020 period. The uncertainty volatility is measured using the standard deviation of the economic policy uncertainty index. The results of this study find that uncertainty volatility increases corporate investment and decreases corporate cash holding. This result indicates that high uncertainty economic condition cause firms to invest to reduce information asymmetry by giving signals to external investors through investment, and as result, it reduces the corporate cash holdings. Moreover, we also find that uncertainty volatility is more economically significant than Economic Policy Uncertainty it self. It is possible that uncertainty volatility can better capture the real uncertain condition in the economy.

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19

Rehman, Mobeen Ur y Nicholas Apergis. "Sensitivity of economic policy uncertainty to investor sentiment". Studies in Economics and Finance 36, n.º 2 (24 de junio de 2019): 114–29. http://dx.doi.org/10.1108/sef-01-2019-0040.

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Purpose This paper aims to explore the impact of investor sentiments on economic policy uncertainty (EPU). The analysis also considers the momentum effect, stock market returns volatility and equity pricing inefficiencies across markets, which, to the best of the authors’ knowledge, has not been addressed in the literature. The role of these control variables has collectively been considered to have important behavioral implications for international investors Design/methodology/approach Quantile regressions are used for estimation purpose, as it provides robust and more efficient estimates rather than those coming from the traditional regression model. Findings The momentum effect is negative and significant only at higher quantiles, while oil prices are positive and significant across all quantiles. The exchange rate exerts a negative and significant effect on EPU, whereas equity price volatility (i.e. investor sentiment) exerts a negative and significant impact on EPU in most of the quantiles. Research limitations/implications The results have important implications for international investors and policymakers, especially in terms of the breakdown of economic policy uncertainty across different sample markets. The breakdown of complete sample period into sub-samples acts as a robust analysis and documents the similarity of the results for the Asian and developed markets cases, but not in the case of the European markets. Practical implications The findings imply the importance of financial stability that impacts the accumulation of systemic risks and adds smoothness to the financial cycle in particular geographical areas. Originality/value The contribution of this paper is threefold. First, existing literature highlights and empirically tests the impact of economic policy uncertainty on different market, macro-economic and global control variables. The analysis, however, performs it in the reverse order, i.e. analyzing the impact of the momentum effect (investor sentiment variables), equity market inefficiencies and volatility (market variables) and exchange rates and Brent oil (control variables). Second, to check the sensitivity of economic policy uncertainty, the analysis analyzes a wide range of markets, segregated as emerging, developed and European regions over the sample period to generate region-wise implications. Finally, the analysis explores the relationship of aforementioned variables with economic policy uncertainty keeping in view the non-linear structure and prior evidence and investor sentiments and economic policy uncertainty in the regression model.

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Bartsch, Zachary. "Economic policy uncertainty and dollar-pound exchange rate return volatility". Journal of International Money and Finance 98 (noviembre de 2019): 102067. http://dx.doi.org/10.1016/j.jimonfin.2019.102067.

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Antonopoulou, Hera, Vicky Mamalougou y Leonidas Theodorakopoulos. "The Role of Economic Policy Uncertainty in Predicting Stock Return Volatility in the Banking Industry: A Big Data Analysis". Emerging Science Journal 6, n.º 3 (19 de abril de 2022): 569–77. http://dx.doi.org/10.28991/esj-2022-06-03-011.

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The research aims to study the effects of economic policy uncertainty on the return volatility of stocks with data from the largest banking institutions in Greece. Volatility is constructed using intraday data, whereas the research period extends over a period of about thirteen years, more specifically from January 5, 2001, to June 30, 2014. This period includes various phases of the market, such as stock market crashes along with stock market booms (e.g. the financial crisis of 2007 and 2008 in the United States and the European sovereign debt crisis). The estimated regressions were used to indicate the direct effects of economic policy uncertainty on the return volatility of the stocks of the four large Greek banks. The volatility index is constructed based on intraday data, whereas four different estimators of volatility were used. There is a statistically significant "direct" effect of economic policy uncertainty on the volatility of stock returns of the largest Greek banks, which are (a) Alpha Bank, (b) Eurobank, (c) National Bank of Greece, and (d) Piraeus Bank. Such findings are highly important for specific groups of people, such as investors, policymakers, and regulators. This study is the first research that seeks to identify the effect of economic policy uncertainty on the stock return volatility of the Greek banking system, constructed from intraday data. Doi: 10.28991/ESJ-2022-06-03-011 Full Text: PDF

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Sohag, K., S. Husain, K. Chukavina y Md Al Mamun. "Policy Uncertainty, Oil Price, Stock Market and Precious Metal Markets Volatility Spillovers in the Russian Economy". Economy of Region 18, n.º 2 (2022): 383–97. http://dx.doi.org/10.17059/ekon.reg.2022-2-6.

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The Russian economy is emerging, meaning that natural resources play a dominant role in economic development. Given the considerable volatility in resource prices, we investigate the volatility spillovers among policy uncertainty, international oil prices, exchange rate, stock index and metal prices covering the period of 2 July 2008 to 15 May 2020 for the Russian economy applying Dynamic Connectedness based on Time-Varying Parameter Vector Autoregression (TVP-VAR). Our empirical investigation demonstrates that gold price, Russian policy uncertainty, oil price and stock index are net volatility contributors, whereas palladium, platinum, silver and exchange rate are net volatilities receivers. Market capitalisation and silver market are found to be the highest net contributor and net receiver, respectively. The palladium appears as a net volatility receiver initially, just after the global financial crisis. The Russian economic policy uncertainty appears to be the dominant volatility contributor from 2008 to 2014, but onward it turned to be a net volatility receiver. Over the year 2014, gold price was the prominent volatility contributor to another market when the oil price dropped significantly. The total connectivity of the markets are highly anchored with several exogenous shocks, including economic sanction, adoption of floating exchange rate, oil price plunge. Our empirical findings provide several policy implications to portfolio managers and Russian regional stakeholders.

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Bossman, Ahmed, Ştefan Cristian Gherghina, Emmanuel Asafo-Adjei, Anokye Mohammed Adam y Samuel Kwaku Agyei. "EXPLORING THE ASYMMETRIC EFFECTS OF ECONOMIC POLICY UNCERTAINTY AND IMPLIED VOLATILITIES ON ENERGY FUTURES RETURNS: NOVEL INSIGHTS FROM QUANTILE-ON-QUANTILE REGRESSION". Journal of Business Economics and Management 23, n.º 6 (29 de diciembre de 2022): 1351–76. http://dx.doi.org/10.3846/jbem.2022.18282.

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This study examined the asymmetric effects of major uncertainty and volatility indices (economic policy uncertainty, Chicago Board Options Exchange crude oil volatility, CBOE volatility index, CBOE VIX volatility, and NASDAQ 100 volatility target) on the returns of global energy and its constituents (global energy index, Brent, heating oil, natural gas, and petroleum). The causalityin-quantiles test and the quantile-on-quantile regression technique were employed on daily data covering the period between April 2012 and March 2022. The findings evidenced asymmetries and heterogeneity in the causal effects of global uncertainty and market volatilities on energy markets. For all uncertainty and volatility measures, we found strong negative relationships with energy commodities at stressed conditions, signalling some hedging benefits for market participants. The current research is among the first investigations to explore the asymmetric relationships between major uncertainty and volatility indices, as well as global energy and its constituents. Essential portfolio implications of our findings are discussed.

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Xiao, Jihong y Yudong Wang. "Investor attention and oil market volatility: Does economic policy uncertainty matter?" Energy Economics 97 (mayo de 2021): 105180. http://dx.doi.org/10.1016/j.eneco.2021.105180.

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Scarcioffolo, Alexandre R. y Xiaoli L. Etienne. "Regime-switching energy price volatility: The role of economic policy uncertainty". International Review of Economics & Finance 76 (noviembre de 2021): 336–56. http://dx.doi.org/10.1016/j.iref.2021.05.012.

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Yu, Xiaoling, Yirong Huang y Kaitian Xiao. "Global economic policy uncertainty and stock volatility: evidence from emerging economies". Journal of Applied Economics 24, n.º 1 (1 de enero de 2021): 416–40. http://dx.doi.org/10.1080/15140326.2021.1953913.

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Xiao, Xiaoyong, Qingsong Tian, Shuxia Hou y Chongguang Li. "Economic policy uncertainty and grain futures price volatility: evidence from China". China Agricultural Economic Review 11, n.º 4 (25 de octubre de 2019): 642–54. http://dx.doi.org/10.1108/caer-11-2018-0224.

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Purpose The purpose of this paper is to investigate the influence of economic policy uncertainty (EPU) on China’s grain futures prices. Related literature has discussed several factors contributing to the dramatic boom and bust in China’s grain futures prices, but has overlooked the influence of EPU. Design/methodology/approach The study employs a newly developed time-varying parameter vector autoregressive model to study and contrast the impact of different types of uncertainty on China’s grain futures prices. The directional volatility spillover index is used to measure the impact of EPU on China’s grain futures prices and compare the differences among commodities. Findings The results show that EPU affects China’s grain futures prices significantly. The 2008 global financial crisis had stronger influence on China’s grain futures prices than other types of uncertainty. Furthermore, EPU has smaller influence on wheat futures price than on maize and soybean. The Chinese Government interventions may be the reason for this difference. Originality/value This study addresses the lack of empirical investigation on the influence of EPU on China’s grain futures price volatility.

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Li, Yu, Feng Ma, Yaojie Zhang y Zuoping Xiao. "Economic policy uncertainty and the Chinese stock market volatility: new evidence". Applied Economics 51, n.º 49 (6 de mayo de 2019): 5398–410. http://dx.doi.org/10.1080/00036846.2019.1613507.

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Duan, Yinying, Wang Chen, Qing Zeng y Zhicao Liu. "Leverage effect, economic policy uncertainty and realized volatility with regime switching". Physica A: Statistical Mechanics and its Applications 493 (marzo de 2018): 148–54. http://dx.doi.org/10.1016/j.physa.2017.10.040.

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Mei, Dexiang, Qing Zeng, Yaojie Zhang y Wenjing Hou. "Does US Economic Policy Uncertainty matter for European stock markets volatility?" Physica A: Statistical Mechanics and its Applications 512 (diciembre de 2018): 215–21. http://dx.doi.org/10.1016/j.physa.2018.08.019.

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Belcaid, Karim y Ahmed El Ghini. "U.S., European, Chinese economic policy uncertainty and Moroccan stock market volatility". Journal of Economic Asymmetries 20 (noviembre de 2019): e00128. http://dx.doi.org/10.1016/j.jeca.2019.e00128.

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Chen, Liming, Ziqing Du y Zhihao Hu. "Impact of economic policy uncertainty on exchange rate volatility of China". Finance Research Letters 32 (enero de 2020): 101266. http://dx.doi.org/10.1016/j.frl.2019.08.014.

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Li, Tao, Feng Ma, Xuehua Zhang y Yaojie Zhang. "Economic policy uncertainty and the Chinese stock market volatility: Novel evidence". Economic Modelling 87 (mayo de 2020): 24–33. http://dx.doi.org/10.1016/j.econmod.2019.07.002.

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Shaikh, Imlak. "DOES POLICY UNCERTAINTY AFFECT EQUITY, COMMODITY, INTEREST RATES, AND CURRENCY MARKETS? EVIDENCE FROM CBOE’S VOLATILITY INDEX". Journal of Business Economics and Management 21, n.º 5 (17 de agosto de 2020): 1350–74. http://dx.doi.org/10.3846/jbem.2020.13164.

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Economic policy drives investment, production, employment, and other macroeconomic indicators of the economy. The study examines the equity, commodity, interest rates, and currency markets, taking into consideration the US economic policy uncertainty (EPU) index. The present work determines the association among policy uncertainty and volatility index, expressed in terms of generalized autoregressive conditional heteroscedasticity and period of empirical work spanning from 2000 to 2018. The results suggest that equity markets’ volatility tends to be very high based on a high degree of policy uncertainty. The findings on the commodity market indicate that crude oil and gold prices remain more volatile during the presidential election and financial crisis. One of the essential results shows that the 2000s boom, early credit crunch, Lehman’s collapse and recession, and fiscal policy battles have significantly affected the equity, currency, and commodity markets. The interest rates and currency markets have responded considerably to Feds’ and EPU index. The empirical outcome provides evidence that implied volatility index is a forward looking expectation of future stock market volatility, and it uncovers that policy uncertainty affects investor sentiment. The present work holds some practical implications for the government to formulate policies to regulate the US market.

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SADHWANI, RANJEETA, SURESH KUMAR OAD RAJPUT, ASAD ALI-RIND y MUHAMMAD TAHIR SULEMAN. "DOES CHANGE IN ECONOMIC POLICY UNCERTAINTY AFFECT REAL ESTATE INVESTMENT TRUSTS (REITs)?" Annals of Financial Economics 14, n.º 04 (diciembre de 2019): 1950016. http://dx.doi.org/10.1142/s2010495219500167.

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This study aims to find the impact of change in economic policy uncertainty (EPU) on the returns and volatilities of 11 CRSP Ziman value-weighted US real estate investment trusts (REITs) during 1985–2016. The results indicate that the change in EPU has a positive relationship with volatility and a negative one with the REITs returns. Among EPU components, news-based component has the major impact than the others. Change in economic policy uncertainty has a significant impact on the returns of all the indices except hybrid, healthcare and unclassified REITs after controlling for macroeconomic variables. Whereas, the volatility is mainly explained by its own past values and macroeconomic variables.

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Fernández-Villaverde, Jesús, Pablo Guerrón-Quintana, Keith Kuester y Juan Rubio-Ramírez. "Fiscal Volatility Shocks and Economic Activity". American Economic Review 105, n.º 11 (1 de noviembre de 2015): 3352–84. http://dx.doi.org/10.1257/aer.20121236.

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We study how unexpected changes in uncertainty about fiscal policy affect economic activity. First, we estimate tax and spending processes for the United States with time-varying volatility to uncover evidence of time-varying volatility. Second, we estimate a VAR for the US economy using the time-varying volatility found in the previous step. Third, we feed the tax and spending processes into an otherwise standard New Keynesian model. Both in the VAR and in the model, we find that unexpected changes in fiscal volatility shocks can have a sizable adverse effect on economic activity. An endogenous increase in markups is a key mechanism. (JEL E12, E23, E32, E52, E62)

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Haq, Inzamam Ul, Paulo Ferreira, Derick David Quintino, Nhan Huynh y Saowanee Samantreeporn. "Economic Policy Uncertainty, Energy and Sustainable Cryptocurrencies: Investigating Dynamic Connectedness during the COVID-19 Pandemic". Economies 11, n.º 3 (24 de febrero de 2023): 76. http://dx.doi.org/10.3390/economies11030076.

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The purpose of the research is to explore the dynamic multiscale linkage between economic policy uncertainty, equity market volatility, energy and sustainable cryptocurrencies during the COVID-19 period. We use a multiscale TVP-VAR model considering level (EPUs and IDEMV) and returns series (cryptocurrencies) from 1 December 2019 to 30 September 2022. The data are then decomposed into six wavelet components, based on the wavelet MODWT method. The TVP-VAR connectedness approach is used to uncover the dynamic connectedness among EPUs, energy and sustainable cryptocurrency returns. Our findings reveal that CNEPU (USEPU) is the strongest (weakest) NET volatility transmitter. IDEMV is the most consistent volatility NET transmitter among all uncertainty indices across the original returns and wavelet scales (D1~D6). Energy cryptocurrencies, i.e., GRID, POW and SNC, are more likely to receive volatility spillovers than sustainable cryptocurrencies during a turbulent period (COVID-19). XLM (XNO) is least (most) affected by volatility spillover in system-wide connectedness, and XLM (ADA and MIOTA) showed a consistent (heterogeneous) non-recipient behavior across the six wavelet (D1~D6) scales and original return series. This study uncovers the dynamic connectedness across multiscale, which will support investors considering different investment horizons (D1~D6).

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38

Spyrou, Spyros. "Momentum return volatility, uncertainty, and energy prices: evidence from major international equity markets". Review of Behavioral Finance 12, n.º 4 (8 de abril de 2020): 411–33. http://dx.doi.org/10.1108/rbf-09-2019-0133.

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PurposeThis paper examines the impact of macroeconomic and risk factors on the profitability and volatility of professional momentum portfolios for the US, the UK, Japan and Germany, for the period 1998–2018. Many of the factors employed, such as energy price changes and economic policy uncertainty, have been largely neglected in the relevant literature.Design/methodology/approachRegression analysis, VECTOR AUTOREGRESSION (VAR), Panel-VAR, Variance Decomposition AnalysisFindingsThe results indicate that, since the financial crises in the US and the EU, energy prices and economic-policy uncertainty have become important return determinants, along with market-related uncertainty that seems to have a stable impact over time, especially for the U.S. and U.K. portfolios.Research limitations/implicationsEconomic policy uncertainty significantly affects contemporaneous momentum returns in the US, UK and Japan, mainly between 2007 and 2018, while market-related uncertainty affects all markets during all subperiods. In addition, the variance of market-related uncertainty (VIX) explains a large percentage of the variance in the momentum returns for the US, UK and Germany.Practical implicationsThe main implication of the findings for portfolio managers is that a manager may increase (decrease) exposure to the momentum factor during optimistic (pessimistic) periods and during periods of rising energy prices (high economic policy and market-related uncertainty).Originality/valueThe paper examines the impact of factors, such as energy prices and economic policy uncertainty, which have been largely neglected in the relevant literature on the possible drivers of the momentum strategies. It employs professional portfolios that are often used in practice as benchmark indexes.

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Su, Xiaqing y Zhe Liu. "Sector Volatility Spillover and Economic Policy Uncertainty: Evidence from China’s Stock Market". Mathematics 9, n.º 12 (17 de junio de 2021): 1411. http://dx.doi.org/10.3390/math9121411.

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Following generalized variance decomposition, we identify the transmission structure of financial shock among ten sectors in China. Then, we examine whether economic policy uncertainty (EPU) affects it through GARCH-MIDAS regression. We find that consumer discretionary, industrials, and materials sectors are systemically important industries during the sample period. Further research of dynamic analysis shows that each sector acts in a time-varying role in this structure. The results of the GARCH-MIDAS regression indicate that none of the selected EPU indexes has a significant long-term impact on the total volatility spillover of the inter-sector stock market in China. However, the EPUs do affect some sectors’ spillover indexes in the long run, and they are significantly heterogeneous. This paper can provide regulatory suggestions for policymakers and reasonable asset allocation and risk avoidance methods for investors.

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Ahmed, Maruf Yakubu y Samuel Asumadu Sarkodie. "COVID-19 pandemic and economic policy uncertainty regimes affect commodity market volatility". Resources Policy 74 (diciembre de 2021): 102303. http://dx.doi.org/10.1016/j.resourpol.2021.102303.

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41

Liu, Zhicao, Yong Ye, Feng Ma y Jing Liu. "Can economic policy uncertainty help to forecast the volatility: A multifractal perspective". Physica A: Statistical Mechanics and its Applications 482 (septiembre de 2017): 181–88. http://dx.doi.org/10.1016/j.physa.2017.04.076.

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42

Shahzad, Syed Jawad Hussain, Naveed Raza, Mehmet Balcilar, Sajid Ali y Muhammad Shahbaz. "Can economic policy uncertainty and investors sentiment predict commodities returns and volatility?" Resources Policy 53 (septiembre de 2017): 208–18. http://dx.doi.org/10.1016/j.resourpol.2017.06.010.

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43

Yao, Yanyun, Haijing Yu, Huimin Wang y Tsung-Kuo Tien-Liu. "Impact of Economic Policy Uncertainty on the Distribution of China’s Stock Returns: An External Perspective". Journal of Advanced Computational Intelligence and Intelligent Informatics 23, n.º 4 (20 de julio de 2019): 667–77. http://dx.doi.org/10.20965/jaciii.2019.p0667.

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This study examines the impact of external economic policy uncertainty on the distribution of China’s stock returns. The Chinese Economic Policy Uncertainty (CEPU) and global EPU (GEPU) indexes compiled by [1] are employed as a measurement of the external uncertainty. An empirical study is conducted using the GARCH-MIDAS framework. The first innovation of this study is extending the symmetric GARCH-MIDAS model to the case of GJR; the leverage effect is therefore considered. The second innovation is considering the impact of EPU on the overall distribution of returns, rather than on the mean or volatility. Full-sample fitting shows that CEPU can explain around 14% of the return volatility, and CEPU together with GEPU can explain about 17%. Out-of-sample recursive forecasting demonstrates that it is meaningful to extend the models to GJR; the EPU information improves the return distribution forecasting. However, the impact of EPUs is limited, which implies that external uncertainty is quite different from the “internal” economic policy uncertainty directly driving the China’s stock market.

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Feng, Yanhong, Dilong Xu, Pierre Failler y Tinghui Li. "Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation". Sustainability 12, n.º 16 (12 de agosto de 2020): 6523. http://dx.doi.org/10.3390/su12166523.

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Due to multiple properties, the international crude oil price is influenced by various and complex interrelated factors from different determinants in different periods. However, the previous studies on crude oil price fluctuation with economic policy uncertainty (EPU) haven’t taken a wider range of volatility sources into their analysis frameworks. In this paper, the time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model is introduced in order to avoid important information loss, as well as capture the time-varying impact on crude oil price fluctuation by EPU. Furthermore, the differences on crude oil fluctuations from net-oil exporting and net-oil importing country’s EPU are also elaborated. Here are three findings as follows. First, the impacts of global EPU on the crude oil price volatility show time-varying characteristics both in time duration and time-points. Second, the instantaneous impacts of global EPU on the price volatility of crude oil are directly relevant to major events, and the impacts are different in event types as well. Third, the time-varying characteristics depicting the impacts of EPU in countries who are net-oil exporter and net-oil importer on price volatility of crude oil show heterogeneity in fluctuation range, fluctuation intensity, and stage.

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45

Naser, Hanan. "COVID-19, Oil Price, Bitcoin, and US Economic Policy Uncertainty: Evidence from ARDL Model". International Journal of Economics and Finance 13, n.º 11 (25 de octubre de 2021): 92. http://dx.doi.org/10.5539/ijef.v13n11p92.

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The pandemic of coronavirus (COVID-19) creates fear and uncertainty causing extraordinary disruption to financial markets and global economy. Witnessing the fastest selloff in the American stock market in history with a plunge of more than 28% in S&P 500 has increased the volatility of global financial market to exceed the level observed during the financial crisis of 2008. On the other hand, Bitcoin value has shown considerable stability in the last couple of months peaking at $10,367.53 in the mid of February 2020. In this context, the aim of this paper is to investigate the impact of COVID-19 numbers on Bitcoin price taking into consideration number of controlling variables including WTI-oil price, S&P 500 index, financial market volatility, gold prices, and economic policy uncertainty of the US. To do so, ARDL estimation has been applied using daily data from December 31, 2019 till May 20, 2020. Key findings reveal that the daily reported cases of new infections have a marginal positive impact on Bitcoin price in the long term. However, the indirect impact associated with the fear of COVID-19 pandemic via financial market stress cannot be neglected. Bitcoin can also serve as a hedging tool against the economic policy uncertainty in the long term. In the short run, while the returns of economic policy uncertainty have no impact on Bitcoin price, the growth in the new cases of COVID-19 infection and returns of financial market volatility have more positive significant impact on Bitcoin returns.

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46

Naser, Hanan. "COVID-19, Oil Price, Bitcoin, and US Economic Policy Uncertainty: Evidence from ARDL Model". International Journal of Economics and Finance 13, n.º 11 (25 de octubre de 2021): 88. http://dx.doi.org/10.5539/ijef.v13n11p88.

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The pandemic of coronavirus (COVID-19) creates fear and uncertainty causing extraordinary disruption to financial markets and global economy. Witnessing the fastest selloff in the American stock market in history with a plunge of more than 28% in S&P 500 has increased the volatility of global financial market to exceed the level observed during the financial crisis of 2008. On the other hand, Bitcoin value has shown considerable stability in the last couple of months peaking at $10,367.53 in the mid of February 2020. In this context, the aim of this paper is to investigate the impact of COVID-19 numbers on Bitcoin price taking into consideration number of controlling variables including WTI-oil price, S&P 500 index, financial market volatility, gold prices, and economic policy uncertainty of the US. To do so, ARDL estimation has been applied using daily data from December 31, 2019 till May 20, 2020. Key findings reveal that the daily reported cases of new infections have a marginal positive impact on Bitcoin price in the long term. However, the indirect impact associated with the fear of COVID-19 pandemic via financial market stress cannot be neglected. Bitcoin can also serve as a hedging tool against the economic policy uncertainty in the long term. In the short run, while the returns of economic policy uncertainty have no impact on Bitcoin price, the growth in the new cases of COVID-19 infection and returns of financial market volatility have more positive significant impact on Bitcoin returns.

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Fasanya, Ismail O., Oluwasegun B. Adekoya y Johnson A. Oliyide. "Economic uncertainty of pandemic and international airlines behaviour". PLOS ONE 17, n.º 5 (26 de mayo de 2022): e0266842. http://dx.doi.org/10.1371/journal.pone.0266842.

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This paper examines the role of uncertainty due to infectious diseases in predicting twenty International airline stocks within a nonparametric causality-in-quantiles framework. We observe that: First, the BDS test shows that nonlinearity is very important when examining the causal relationship between EMV-ID and airline stock returns and its volatility. Second, the nonparametric quantiles-based causality test shows that airline stocks predictability driven by pandemic-based uncertainty is stronger mostly around the lower quantiles, with weak evidences in middle and higher quantiles. Relevant policy implications can be drawn from these findings.

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48

Abaidoo, Rexford. "Policy uncertainty and dynamics of international trade". Journal of Financial Economic Policy 11, n.º 1 (1 de abril de 2019): 101–20. http://dx.doi.org/10.1108/jfep-02-2018-0034.

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PurposeThis study aims to empirically examine how economic policy uncertainty emanating from three major global economic blocks (the US, the Chinese and the European Union) and volatility in global oil prices influence international trade.Design/methodology/approachThe study uses quarterly data spanning the period between 1995 and 2014 in an autoregressive distributed lag framework.FindingsThis study finds that economic policy uncertainty conditions associated with the US and the Chinese economies tend to have significant negative or constraining impact on key components of international trade. Further analysis suggests that between the two leading economies (the US and the Chinese economies), economic policy uncertainty emanating from the US economy tend to have much more constraining impact on dynamics of international trade than the Chinese economy all things being equal.Practical implicationsThis study’s findings carry significant strategic planning and policy implications for international trade dependent firms or corporations and economies. For instance, for multi-national corporations or firms whose products and services depend heavily on cross-border trade, understanding and taking into consideration prevailing economic policy dynamics emanating from the US and the Chinese economies in product and services demand forecast, and other strategic moves could be critical in minimizing potential adverse effects on projected performance or growth targets.Originality/valueThe uniqueness of this study’s approach stems from its assessment of how perception of uncertainty among economic agents about economic policies originating from three noted global economic blocks impacts international trade. In other words, instead of traditional factors or conditions surmised to influence variability in trend associated with international trade found in related studies, this study rather examines how perceptions of uncertainty about prevailing or yet to be enacted economic policy within specific global economic block impacts international trade.

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Tissaoui, Kais, Taha Zaghdoudi, Abdelaziz Hakimi, Ousama Ben-Salha y Lamia Ben Amor. "Does Uncertainty Forecast Crude Oil Volatility before and during the COVID-19 Outbreak? Fresh Evidence Using Machine Learning Models". Energies 15, n.º 15 (8 de agosto de 2022): 5744. http://dx.doi.org/10.3390/en15155744.

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This paper uses two competing machine learning models, namely the Support Vector Regression (SVR) and the eXtreme Gradient Boosting (XGBoost) against the Autoregressive Integrated Moving Average ARIMAX (p,d,q) model to identify their predictive performance of the crude oil volatility index before and during COVID-19. In terms of accuracy, forecasting results reveal that the SVR model dominates the XGBoost and ARIMAX models in predicting the crude oil volatility index before COVID-19. However, the XGBoost model provides more accurate predictions of the crude oil volatility index than the SVR and ARIMAX models during the pandemic. The inverse cumulative distribution of residuals suggests that both ML models produce good results in terms of convergence. Findings also indicate that there is a fast convergence to the optimal solution when using the XGBoost model. When analyzing the feature importance, the Shapley Additive Explanation Method reveals that the SVR performs significantly better than the XGBoost in terms of feature importance. During the pandemic, the predictive power of the CBOE Volatility Index and Economic Policy Uncertainty index for forecasting the crude oil volatility index is improved compared to the pre-COVID-19 period. These findings imply that investor fear-induced uncertainty in the financial market and economic policy uncertainty are the most significant features and hence represent substantial sources of uncertainty in the oil market.

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Phan, Dinh Hoang Bach y Solikin M. Juhro. "CAN ECONOMIC POLICY UNCERTAINTY PREDICT EXCHANGE RATE AND ITS VOLATILITY? EVIDENCE FROM ASEAN COUNTRIES". Buletin Ekonomi Moneter dan Perbankan 21, n.º 2 (31 de octubre de 2018): 265–82. http://dx.doi.org/10.21098/bemp.v21i2.974.

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This paper studies whether the global economic policy uncertainty (EPU) predicts the exchange rate and its volatility in 10 ASEAN countries using monthly data from January 1997 to December 2017. Applying the recently developed predictive regression model of Westerlund and Narayan (2012, 2015), we discover that the EPU positively and statistically significantly predicts the exchange rate of six out of ten currencies. One standard deviation increase in the EPU index leads to a depreciation of between 0.050% and 2.047% in these currencies. Moreover, the EPU predicts the exchange rate volatility for all 10 ASEAN countries. Their exchange rate volatilities increase by between 0.107% and 0.645% as a result of a one standard deviation increase in the EPU index. These results are robust to different forecasting horizons, different sub-sample periods, and after controlling for the global financial crisis.

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