The 31 reference contexts in paper Christopher F. Baum, Dorothea Schäfer, Andreas Stephan (2013) “Credit Rating Agency Downgrades and the Eurozone Sovereign Debt Crises” / RePEc:boc:bocoec:841

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    3726
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    This reaction may worsen the fundamental economic situation and trigger even further withdrawals. The rating announcement would then become a self-full-filling prophecy and trigger a currency crisis. Such a sequence of events was observed in the Asian crisis
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    (Morris & Shin 1998).
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    Moreover, downgrades could initiate speculation against the Euro, placing even more pressure on its value. Although some problems of the Eurozone are similar to the ones observed in the Asian crisis, there exists one decisive difference.
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    The role of credit rating agencies in the global financial system as well as quality of their credit risk assessments has been widely debated. Credit rating agencies have often been criticized for violating their primary function of minimizing information uncertainty in financial markets. In line with this conclusion,
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    Carlson & Hale (2005),
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    using a global games framework, find that the existence of credit rating agencies may threaten market stability as it increases the incidence of multiple equilibria. Bannier & Tyrell (2005) report that a unique equilibrium can be restored by making the rating process more transparent, enabling market participants to independently assess quality and validity of credit ratings.
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    In line with this conclusion, Carlson & Hale (2005), using a global games framework, find that the existence of credit rating agencies may threaten market stability as it increases the incidence of multiple equilibria.
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    Bannier & Tyrell (2005)
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    report that a unique equilibrium can be restored by making the rating process more transparent, enabling market participants to independently assess quality and validity of credit ratings. The more accurate are credit rating announcements, the greater is the efficiency of investor decisions, and hence, is the market outcome.
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    Moody’s bases its rating decisions on the expected loss, which is a function of both the probability of default and the expected recovery rate. Finally, Fitch takes into consideration both the probability of default and the recovery rate
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    (Elkhoury 2009).
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    Prior studies report that sovereign credit ratings are primarily affected by the following economic indicators: GDP per capita, GDP growth, public debt as a percentage of GDP, budget deficit as a percentage of GDP and inflation level within the country (Cantor & Packer 1996).
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    Prior studies report that sovereign credit ratings are primarily affected by the following economic indicators: GDP per capita, GDP growth, public debt as a percentage of GDP, budget deficit as a percentage of GDP and inflation level within the country
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    (Cantor & Packer 1996).
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    A history of sovereign default, the level of economic development and government effectiveness within the country have also been identified as important in determining sovereign credit ratings (Afonso et al. 2012).
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    by the following economic indicators: GDP per capita, GDP growth, public debt as a percentage of GDP, budget deficit as a percentage of GDP and inflation level within the country (Cantor & Packer 1996). A history of sovereign default, the level of economic development and government effectiveness within the country have also been identified as important in determining sovereign credit ratings
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    (Afonso et al. 2012).
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    Information as to how CRAs assign weights to each variable they consider in assessment of the credit risk is not publically available. The effects of sovereign credit ratings on debt and equity markets have been studied by many researchers.
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    Information as to how CRAs assign weights to each variable they consider in assessment of the credit risk is not publically available. The effects of sovereign credit ratings on debt and equity markets have been studied by many researchers.
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    Brooks et al. (2004)
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    report that rating downgrades negatively affect stock market returns. At the same time the dollar value of the domestic currency decreases. Kräussl (2005) shows that negative ratings significantly increase an index of speculative market pressure which consists of daily nominal exchange rate changes, daily short-term interest rate changes and daily stock market changes.
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    In contrast, rating upgrades and positive outlooks show a weak or even insignificant impact. Kim & Wu (2008) claim that long-term credit ratings support the development of financial markets in emerging economies.
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    Hooper et al. (2008)
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    examine the impact of credit rating events on international financial markets using a database of 42 countries over the period 1995 to 2003. They provide evidence that rating upgrades significantly increased USD denominated stock market returns and decreased volatility.
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    They provide evidence that rating upgrades significantly increased USD denominated stock market returns and decreased volatility. Downgrades show the corresponding contrary effect. However the market responses for both return and volatility are asymmetric and more pronounced for downgrades. A recent paper by
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    Wu & Treepongkaruna (2008)
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    empirically tested the impact of sovereign credit rating news on volatility of stock returns and currency markets during the Asian financial crisis. Both market measures were found to be strongly affected by changes in sovereign credit ratings with currency markets being more responsive to credit rating news, while changes in sovereign outlooks had much stronger 4 impact on stock price volatility
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    to be strongly affected by changes in sovereign credit ratings with currency markets being more responsive to credit rating news, while changes in sovereign outlooks had much stronger 4 impact on stock price volatility than did actual rating announcements. Several research papers also find strong contagion effects of watch and outlook changes on stock, bond and CDS markets of nearby countries.
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    Hamilton & Cantor (2004) and Alsati et al. (2005)
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    find evidence that rating events such as outlooks and watchlist assignements increase the predictive power of sovereign credit rating announcements, as they shed light on which governments are likely to default on their debt or to be downgraded in the foreseen future. 2.1 Credibility of CRA rating announcements The reliability of CRA announcements in times of crisis has often been questioned.
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    Ferri & Stiglitz (1999) report that prior to the Asian financial crisis, credit ratings were higher than economic fundamentals would suggest, while ex post ratings were much lower than the model predicted. This evidence suggests a procyclical rating behavior.
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    Reinhart (2002)
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    confirms that rating agencies lag behind the market. Following the markets with downgrading rather than leading it might accelerate the panic among investors, drive money out of the country and sovereign yield spreads up (Reisen & von Maltzan 1999).
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    Reinhart (2002) confirms that rating agencies lag behind the market. Following the markets with downgrading rather than leading it might accelerate the panic among investors, drive money out of the country and sovereign yield spreads up (Reisen & von Maltzan 1999).
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    Bhatia (2002)
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    claims that failed ratings stem from CRAs’ inclination towards ratings stability rather than accuracy of reported announcements. Mora (2006) reports indeed a considerable stickiness of ratings. Elkhoury (2009) also describes the assessment of the CRA as “tend[ing] to be sticky, lagging markets, and then overreact[ing] when they do change” (p. 1).
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    Following the markets with downgrading rather than leading it might accelerate the panic among investors, drive money out of the country and sovereign yield spreads up (Reisen & von Maltzan 1999). Bhatia (2002) claims that failed ratings stem from CRAs’ inclination towards ratings stability rather than accuracy of reported announcements.
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    Mora (2006)
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    reports indeed a considerable stickiness of ratings. Elkhoury (2009) also describes the assessment of the CRA as “tend[ing] to be sticky, lagging markets, and then overreact[ing] when they do change” (p. 1).
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    Bhatia (2002) claims that failed ratings stem from CRAs’ inclination towards ratings stability rather than accuracy of reported announcements. Mora (2006) reports indeed a considerable stickiness of ratings.
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    Elkhoury (2009)
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    also describes the assessment of the CRA as “tend[ing] to be sticky, lagging markets, and then overreact[ing] when they do change” (p. 1). Moreover, Ferri & Stiglitz (1999) observe that in response to a past major rating failure CRAs tend to become overly conservative.
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    For example, public debt levels in Italy of 116.4% in 2009, 119.3% in 2010 and 120.8%, and in Greece of 129.7% in 2009, 148.3% in 2010 and 170.3% in 2011 far exceeded GDP in the corresponding years (see Figure 1).2In addition to this, credit default swap (CDS) spreads experienced increased volatility during the financial crisis, resulting in a dramatic growth of CDS premia (see Figure 2).
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    Deb et al. (2011)
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    report that from January 2008 to June 2010 the premia increased 850% for France, 614% for Germany, 3364% for Greece and 1394% for Spain. Many European private banks have invested heavily in government bonds issued by the troubled countries, which now makes the return on these investments highly uncertain.
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    French banks’ exposure was 347 billion USD to Italy and 128 billion USD to Spain (Bank of International Settlements 2012, http://www.bis.org/statistics/consstats.htm). 6 Existing research indicates the presence of statistically significant spillover effects of rating news from an event country to markets of non-event countries.
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    Duggar et al. (2009)
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    find evidence that sovereign defaults spread into other areas of corporate finance, leading to widespread corporate defaults. Arezki et al. (2011) report that rating downgrades to near-speculative grade for sovereigns are especially contagious across countries and financial markets.
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    (Bank of International Settlements 2012, http://www.bis.org/statistics/consstats.htm). 6 Existing research indicates the presence of statistically significant spillover effects of rating news from an event country to markets of non-event countries. Duggar et al. (2009) find evidence that sovereign defaults spread into other areas of corporate finance, leading to widespread corporate defaults.
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    Arezki et al. (2011)
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    report that rating downgrades to near-speculative grade for sovereigns are especially contagious across countries and financial markets. For example, a rating downgrade of Greece from “A-” to “BBB+” grade, as announced by Fitch on 8 December 2009, resulted in substantial spillover effects across members of the EMU including a 17 and 5 basis points increase in Greek and Irish CDS spreads respective
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    For example, a rating downgrade of Greece from “A-” to “BBB+” grade, as announced by Fitch on 8 December 2009, resulted in substantial spillover effects across members of the EMU including a 17 and 5 basis points increase in Greek and Irish CDS spreads respectively.
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    Afonso et al. (2012)
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    argue that rating downgrades of lower-rated countries have strong spillover effects on higher-rated sovereigns in the region, which is consistent with prior studies by Gande & Parsley (2005) and Ismailescu & Kazemi (2010).
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    +” grade, as announced by Fitch on 8 December 2009, resulted in substantial spillover effects across members of the EMU including a 17 and 5 basis points increase in Greek and Irish CDS spreads respectively. Afonso et al. (2012) argue that rating downgrades of lower-rated countries have strong spillover effects on higher-rated sovereigns in the region, which is consistent with prior studies by
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    Gande & Parsley (2005) and Ismailescu & Kazemi (2010).
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    They also report statistically significant persistence effects of rating announcements: countries downgraded (upgraded) within the last month tend to have at least 0.5% higher (lower) sovereign bond yields for the next six months until the effect disappears.
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    Market reactions to credit rating changes and contagion effects were particularly strong during the crisis period of 2006–2010 compared to the pre-crisis period (2000–2006). Negative news from all three major agencies were reported to have an impact but Fitch triggered the strongest reactions. In contrast
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    Ehrmann et al. (2013)
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    found no effect of rating news on the Euro from October 1st, 2009 until November 30, 2011. Our research is closely related to Afonso et al. (2012), Alsakka & ap Gwilym (2013) and Ehrmann et al. (2013).
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    Negative news from all three major agencies were reported to have an impact but Fitch triggered the strongest reactions. In contrast Ehrmann et al. (2013) found no effect of rating news on the Euro from October 1st, 2009 until November 30, 2011. Our research is closely related to
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    Afonso et al. (2012),
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    Alsakka & ap Gwilym (2013) and Ehrmann et al. (2013). However, since we are interested in the impact of CRA announcements on the single currency in times of crisis, we depart from these papers in a number of aspects.
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    In contrast Ehrmann et al. (2013) found no effect of rating news on the Euro from October 1st, 2009 until November 30, 2011. Our research is closely related to Afonso et al. (2012), Alsakka & ap Gwilym (2013) and
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    Ehrmann et al. (2013).
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    However, since we are interested in the impact of CRA announcements on the single currency in times of crisis, we depart from these papers in a number of aspects. First, we concentrate on CRA announcements (unlike Ehrmann et al. 2013).
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    However, since we are interested in the impact of CRA announcements on the single currency in times of crisis, we depart from these papers in a number of aspects. First, we concentrate on CRA announcements (unlike
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    Ehrmann et al. 2013).
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    Second, we consider only the Eurozone and 7 exclusively analyze the period of the acute debt crisis (unlike Alsakka & ap Gwilym 2013 and Afonso et al. 2012). Third, Afonso et al. (2012) focus on the impact of CRA announcements on spreads of sovereign bond yields over German bond yields for the EU countries.
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    First, we concentrate on CRA announcements (unlike Ehrmann et al. 2013). Second, we consider only the Eurozone and 7 exclusively analyze the period of the acute debt crisis (unlike Alsakka & ap Gwilym 2013 and
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    Afonso et al. 2012).
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    Third, Afonso et al. (2012) focus on the impact of CRA announcements on spreads of sovereign bond yields over German bond yields for the EU countries. In contrast, we focus on excess bond yield from large Eurozone members since contagion effects from downgrades should be visible either in the exchange rate or in the large countries’ yields. 3 Development of debt ratios, ratings and exchange rate
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    First, we concentrate on CRA announcements (unlike Ehrmann et al. 2013). Second, we consider only the Eurozone and 7 exclusively analyze the period of the acute debt crisis (unlike Alsakka & ap Gwilym 2013 and Afonso et al. 2012). Third,
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    Afonso et al. (2012)
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    focus on the impact of CRA announcements on spreads of sovereign bond yields over German bond yields for the EU countries. In contrast, we focus on excess bond yield from large Eurozone members since contagion effects from downgrades should be visible either in the exchange rate or in the large countries’ yields. 3 Development of debt ratios, ratings and exchange rates The most affected economie
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    Each of the other three regional currencies depreciated more than 30 percent. The rating agencies were considered to have contributed to a large extent to unnecessary withdrawals of funds from the region and the subsequent depreciation of regional currencies. For example,
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    Radelet & Sachs (1998)
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    accused the major ratings agencies to have “belatedly downgraded countries in the region, triggering further withdrawals by creditors” (p. 27). 10 5 Empirical analysis 5.1 Event study methodology with GARCH models In this paper, we investigate the rating agencies’ contribution to the trajectories of the Euro’s value and that of selected government bond excess yields during the Eurozone crisis.
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    The strength and direction of this correlation is captured by the coefficientβbond. The higher isβbond, the higher the market risk of the bond, and the higher should be its expected excess yield. This is different from
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    Afonso et al. (2012)
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    who base their event study on the observed bond yield spreads between country specific bonds and German bonds. Accordingly, the equation is ∆ (yieldt−rft) ︸︷︷︸ bond excess yield =α0+α1eventt+βbond(mktyieldt−rft) ︸︷︷︸ market excess yield +α2∆cdst+α3∆ ln(vixt) +νt, whererfdenotes the risk-free yield, defined as the yield of AAA rated Eurozone sovereign bonds.
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    We interact these variables with the event dummy variable: ∆(yieldt−rft) =α0+α1eventt+βbond(mktyieldt−rft) +γbond(mktyieldt−rft)×eventt +α2∆cdst+α3∆cdst×eventt+α4∆ ln(vixt) +ξt. In each model, we assume that the CRA event may not only affect the mean equation, but also the variance of the dependent variable
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    (Bollerslev 1986).
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    For instance, one could expect that during event times we observe a higher variance of the excess yield compared to non-event times. The simplest generalized autoregressive conditional heteroskedasticity model is GARCH(1,1).
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    The GARCH formulation has also the advantage that variables can be specified that enter the variance specification collectively as multiplicative heteroskedasticity. The GARCH(1,1) conditional heteroscedasticity equation is specified as σ2t= (γ0+γ1σ2t−1+λ1ε2t−1)×(θ1eventt+θ2lnvixt+θ3cdst), see
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    Judge et al. (1985,
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    p. 843). 13 In principle we could have utilized more complicated models such as EGARCH (Nelson 1991) but these models often give rise to numerical estimation problems and non-convergence. Therefore we have decided to use the simplest GARCH model.
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    The GARCH(1,1) conditional heteroscedasticity equation is specified as σ2t= (γ0+γ1σ2t−1+λ1ε2t−1)×(θ1eventt+θ2lnvixt+θ3cdst), see Judge et al. (1985, p. 843). 13 In principle we could have utilized more complicated models such as EGARCH
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    (Nelson 1991)
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    but these models often give rise to numerical estimation problems and non-convergence. Therefore we have decided to use the simplest GARCH model. Some models’ residuals appear to be distributed with fatter tails than those of a normal distribution.
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    Therefore we have decided to use the simplest GARCH model. Some models’ residuals appear to be distributed with fatter tails than those of a normal distribution. To allow for this flexibility, a generalized error distribution (GED) model is specified
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    (Bollerslev et al. 1994).
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    The GED distribution includes the normal distribution as a special case when its estimated shape parameter is 2.0. 6 Data Description and Results 6.1 Data sources Table 4 provides a description of the variables that are employed in the analysis.
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