scholarly journals Stock Market Reactions during Different Phases of the COVID-19 Pandemic: Cases of Italy and Spain

Economies ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Greta Keliuotyte-Staniuleniene ◽  
Julius Kviklis
Keyword(s):  
Stock Market ◽  
Impulse Response ◽  
Stock Markets ◽  
Primary Response ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Market Reactions ◽  
Ols Regression ◽  
Short Period ◽  
The Impact

The COVID-19 pandemic and pandemic-induced lockdowns and quarantine establishments have inevitably affected individuals, businesses, and governments. At the same time, the spread of the COVID-19 pandemic had a dramatic impact on financial markets all over the world and caused an increased level of uncertainty; the stock markets were no exception either. Most of the studies on the impact of the COVID-19 pandemic on stock markets are based either on the analysis of a relatively short period (the beginning of pandemic) or a longer period, which, in turn, is very heterogeneous in terms of both the information available on the COVID-19 virus and the measures taken to contain the virus and address the consequences of the pandemic. However, it is very important to assess the impact not only at the beginning of the pandemic but also in the subsequent periods and to compare the nature of this impact; the studies of this type are still fragmentary. Therefore, this research aims to investigate the impact of the COVID-19 pandemic on stock markets of two of the most severely affected European countries—Italy and Spain. To reach the aim of the research OLS regression models, heteroscedasticity-corrected models, GARCH (1,1) models, and VAR-based impulse response functions are employed. The results reveal that the stock market reaction to the spread of the COVID-19 pandemic differs depending on the country and period analyzed: OLS regression and heteroscedasticity-corrected models have not revealed the statistically significant impact of the spread of the COVID-19 pandemic, while impulse response functions demonstrated the non-zero primary response of analyzed markets to the COVID-19 shock, and GARCH models (in the case of Spain) confirmed that the COVID-19 pandemic increased the volatility of stock market return. This research contributes to the literature by providing a comprehensive impact assessment both during the whole pre-vaccination period of the pandemic and during different stages of this period.

scholarly journals Variation in emission metrics due to variation in CO<sub>2</sub> and temperature impulse response functions

2013 ◽  
Vol 4 (2) ◽  
pp. 267-286 ◽  
Author(s):  
D. J. L. Olivié ◽  
G. P. Peters
Keyword(s):  
Temperature Change ◽  
Impulse Response ◽  
Effective Means ◽  
Global Temperature ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Climate Effect ◽  
Time Horizons ◽  
Global Temperature Change ◽  
The Impact

Abstract. Emission metrics are used to compare the climate effect of the emission of different species, such as carbon dioxide (CO2) and methane (CH4). The most common metrics use linear impulse response functions (IRFs) derived from a single more complex model. There is currently little understanding on how IRFs vary across models, and how the model variation propagates into the metric values. In this study, we first derive CO2 and temperature IRFs for a large number of complex models participating in different intercomparison exercises, synthesizing the results in distributions representing the variety in behaviour. The derived IRF distributions differ considerably, which is partially related to differences among the underlying models, and partially to the specificity of the scenarios used (experimental setup). In a second part of the study, we investigate how differences among the IRFs impact the estimates of global warming potential (GWP), global temperature change potential (GTP) and integrated global temperature change potential (iGTP) for time horizons between 20 and 500 yr. Within each derived CO2 IRF distribution, underlying model differences give similar spreads on the metrics in the range of −20 to +40% (5–95% spread), and these spreads are similar among the three metrics. GTP and iGTP metrics are also impacted by variation in the temperature IRF. For GTP, this impact depends strongly on the lifetime of the species and the time horizon. The GTP of black carbon shows spreads of up to −60 to +80% for time horizons to 100 yr, and even larger spreads for longer time horizons. For CH4 the impact from variation in the temperature IRF is still large, but it becomes smaller for longer-lived species. The impact from variation in the temperature IRF on iGTP is small and falls within a range of ±10% for all species and time horizons considered here. We have used the available data to estimate the IRFs, but we suggest the use of tailored intercomparison projects specific for IRFs in emission metrics. Intercomparison projects are an effective means to derive an IRF and its model spread for use in metrics, but more detailed analysis is required to explore a wider range of uncertainties. Further work can reveal which parameters in each IRF lead to the largest uncertainties, and this information may be used to reduce the uncertainty in metric values.

scholarly journals The Shape of Eurozone’s Uncertainty: Its Impact and Predictive Value on GDP

2020 ◽  
Vol 7 (6) ◽  
pp. 1
Author(s):  
Ralf Fendel ◽  
Nicola Mai ◽  
Oliver Mohr
Keyword(s):  
Standard Deviation ◽  
Business Cycle ◽  
Financial Markets ◽  
Impulse Response ◽  
Two Dimensions ◽  
Point Estimate ◽  
Impulse Response Functions ◽  
Response Functions ◽  
The Business Cycle ◽  
The Impact

This paper examines the role of uncertainty in the context of the business cycle in the euro area. To gain a more granular perspective on uncertainty, the paper decomposes uncertainty along two dimensions: First, we construct the four different moments of uncertainty, including the point estimate, the standard deviation, the skewness and the kurtosis. The second dimension of uncertainty spans along three distinct groups of economic agents, including consumers, corporates and financial markets. Based on this taxonomy, we construct uncertainty indices and assess the impact on real GDP via impulse response functions and further investigate their informational value in rolling out-of-sample GDP forecasts. The analysis lends evidence to the hypothesis that higher uncertainty expressed through the point estimate, a larger standard deviation among confidence estimates, positive skewness and a higher kurtosis are all negatively correlated with the business cycle. The impulse response functions reveal that in particular the first and the second moment of uncertainty cause a permanent effect on GDP with an initial decline and a subsequent overshoot. We find uncertainty in the corporate sector to be the main driver behind this observation, followed by financial markets’ uncertainty whose initial effect on GDP is comparable but receding much faster. While the first two moments of uncertainty improve GDP forecasts significantly, both the skewness and the kurtosis do not augment the forecast quality any further.

scholarly journals Assessing the reaction of the Baltic stock market to the spread of the COVID-19 pandemic

2021 ◽  
Vol 25 ◽  
pp. 260-272
Author(s):  
Greta Keliuotyte-Staniuleniene ◽  
Julius Kviklis
Keyword(s):  
Stock Market ◽  
Impulse Response Functions ◽  
Cumulative Number ◽  
Response Functions ◽  
Stock Market Index ◽  
Market Index ◽  
Dependent Variables ◽  
The Baltic ◽  
The Impact ◽  
Research Period

This research aims to assess the impact of the spread of the COVID-19 pandemic on the Baltic stock market. To reach this aim, the methods of bivariate (OLS) regression and VAR-based impulse response functions are employed. We use daily new cases of COVID-19 as well as the cumulative number of COVID-19 cases as independent and OMX Baltic Benchmark GI index as dependent variables for our research. The research period, covering data from 2020 March 1st  to 2020 November 21st, is divided into three separate periods, reflecting the different phases of the spread of the COVID-19 pandemic. The results of the research revealed that the market reaction differs depending on the period; moreover, the Baltic stock market index was affected by new cases and total cases in a slightly different manner.

Trade openness and economic development in the UAE: an asymmetric approach

2016 ◽  
Vol 43 (4) ◽  
pp. 587-597 ◽  
Author(s):  
Abdulrahman Al-Shayeb ◽  
Abdulnasser Hatemi-J
Keyword(s):  
Impulse Response ◽  
Trade Openness ◽  
Gdp Per Capita ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Content Type ◽  
Causality Tests ◽  
Per Capita ◽  
The Impact ◽  
Asymmetric Causality

Purpose The purpose of this paper is to offer a review of the trade policy in the UAE. It also investigates the dynamic interaction between trade openness and GDP per capita in this emerging economy. Design/methodology/approach The asymmetric generalized impulse response functions and the asymmetric causality tests developed by Hatemi-J are used. Findings The results from asymmetric generalized impulse response functions indicate that a positive permanent shock in the trade openness results in a significant positive response in the cumulative sum of the positive component of the GDP per capita. Such a response is not found for the negative shocks in the trade openness. Furthermore, neither a positive nor a negative shock in the GPD per capita results in any significant response in the trade openness. These empirical findings are also supported by the implemented asymmetric causality tests. Originality/value This is the first attempt that investigates the impact of trade openness on economic performance in the UAE. Unlike previous literature on the topic, this paper allows for asymmetric impacts in the empirical model.

scholarly journals The impact of model variation in CO<sub>2</sub> and temperature impulse response functions on emission metrics

2012 ◽  
Vol 3 (2) ◽  
pp. 935-977 ◽  
Author(s):  
D. J. L. Olivié ◽  
G. P. Peters
Keyword(s):  
Impulse Response ◽  
Life Time ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Climate Effect ◽  
Time Horizons ◽  
High Emission ◽  
Short Time ◽  
The Impact ◽  
Very High

Abstract. Emission metrics are necessary to determine the relative climate effect of emissions of different species, such as between CO2 and CH4. Most emission metrics are based on Impulse Response Functions (IRFs) derived from singe models. There is currently very little understanding on how IRFs vary across models, and how the model spread propagates into the metric values. In this study, we first derive three CO2 IRF distributions from Carbon-Cycle models in the inter-comparison projects C4MIP and LTMIP, and three temperature IRF distributions from AOGCMs in the inter-comparison projects CMIP3 and CMIP5. Each distribution is based on the behaviour of several models, and takes into account their spread. The derived IRF distributions differ considerably, which is partially related to differences among the underlying models, but also to the specific scenarios (experimental setup) used in the inter-comparison exercises. For example, the very high emission pulse in LTMIP leads to considerably higher CO2 IRFs, while the abrupt forcing scenario in CMIP5 leads to a relatively high temperature IRF the first four to five years. The spreads within the different IRF distributions are however rather similar. In a second part of the study, we investigate how differences among the IRFs then impact GWP, GTP and iGTP emission metric values for time horizons up to 100 yr. The spread in the CO2 IRFs causes rather similar impacts in all three metrics. The LTMIP IRF gives 20–35% lower metric values, while the C4MIP IRFs give up to 40% higher values for short time horizons shifting to lower values for longer time horizons. Within each derived CO2IRF distribution, underlying model differences give similar spreads on the metrics in the range of −15% to 25% (10–90% spread). The GTP and iGTP metrics are also impacted by spread in the temperature IRFs, and this impact differs strongly between both metrics. For GTP, the impact of the spread is rather strong for species with a short life time. For BC, depending on the time horizon, 50% lower to 85% higher values can be found using the CMIP5 IRF, and slightly lower variations are found when using the CMIP3 IRFs (10% lower to 40% higher). For CH4 the impact from spread in the temperature IRF is still considerable, but it becomes small for longer-lived species. On the other hand, the impact from spread in the temperature IRF on iGTP is very small for all species for time horizons up to 100 yr as it is an integrated metric. Finally, as part of the spread in IRFs is caused by the specific setup of the inter-comparison exercises, there is a need for dedicated inter-comparison exercises to derive CO2 and temperature IRFs.

scholarly journals The Response of Remittances Inflows to Asymmetric Oil Price Shocks in Egypt

2019 ◽  
Author(s):  
Mohamed Samir Zahran
Keyword(s):  
Impulse Response ◽  
Oil Prices ◽  
Variance Decomposition ◽  
Oil Price ◽  
Var Model ◽  
Impulse Response Functions ◽  
Oil Price Shocks ◽  
Response Functions ◽  
Price Shocks ◽  
The Impact

Purpose: The purpose of this paper is to explore and analyse the dynamic relationship between remittances inflows of Egyptians working abroad and asymmetric oil price shocks. Design: This study uses a vector autoregressive (VAR) model to explain the impulse response functions (IRFs) and the forecast error variance decomposition (FEVD). The rationale behind using these tools is its ability to examine the dynamic effects of our variables of interest. Findings: The impulse response functions confirmed that remittance inflows have various responses to asymmetric oil price shocks. For instance, inflowing remittances increase in response to positive oil price shocks, while it decreases in response to negative oil price shocks. Also, the results indicate that the responses are significant in the short and medium-run and insignificant in the long run. The magnitude of these responses reaches its peak or trough in the third year. Further, the variance decomposition reveals that oil price decreases are more influential than oil price increases. Originality: This means that remittances inflows in Egypt are pro-cyclical with oil price shocks. That explained by the fact that more than one-half of those remittances sent from GCC countries where real economic growth is very pro-cyclical with the oil prices. This empirical assessment will help policymakers to determine the behaviour of remittances and highlights the impact of different kinds of oil prices shocks on remittances. Unlike the little existing literature, this study is the first study applied the VAR model using a novel dataset spanning 1960-2016.

The impact of monetary policy on income inequality: evidence from Eurozone markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Konstantina Liosi ◽  
Spyros Spyrou
Keyword(s):  
Monetary Policy ◽  
Income Inequality ◽  
Impulse Response ◽  
Design Methodology ◽  
Total Population ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Content Type ◽  
The Impact ◽  
Panel Vector Autoregression

PurposeThis study examines the impact of monetary policy on income inequality in Eurozone countries for the period between 2005 and 2017.Design/methodology/approachThe authors use regression analysis, panel vector autoregression (PVAR) analysis and impulse response functions, in order to examine the impact of monetary policy on income inequality in Eurozone countries.FindingsThis study examines the impact of monetary policy on income inequality in Eurozone countries for the period between 2005 and 2017. The results indicate that, on average, monetary policy tends to increase income inequality. A closer examination indicates that for Ireland, Germany and the Netherlands monetary policy has no impact on income inequality or a weak impact (France), while for Spain, Portugal, Greece and Italy the impact is more pronounced. PVAR analysis and impulse response functions further indicate that female income inequality responds more significantly to monetary policy.Originality/valueIn contrast to previous studies the authors estimate separate models for males, females and the total population to evaluate whether gender is an important factor. Furthermore, the authors use two proxies for monetary policy: the shadow rate (SR) and the total assets (TAs) of the central bank.

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