The 24 reference contexts in paper Maria Sole Pagliari, Swarnali Ahmed Hannan (2017) “The Volatility of Capital Flows in Emerging Markets: Measures and Determinants” / RePEc:rut:rutres:201710

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    This paper was prepared while Swarnali Ahmed Hannan was an economist and Maria Sole Pagliari was a summer intern in the Strategy, Policy and Review Department at the IMF. All remaining errors are ours. 4 In the last decade, however, EMDEs have started to increasingly invest abroad as well (see
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    Obstfeld (2012b)).
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    5 For instance, Broner and Ventura (2016) point out that financial globalization has sometimes led to capital flows that are volatile and procyclical. flows and economic growth (see Easterly, Islam, and Stiglitz 2001 and Ramey and Ramey 1995), maintaining a steady stream of capital flows is a policy priority in most EMDEs.
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    This paper was prepared while Swarnali Ahmed Hannan was an economist and Maria Sole Pagliari was a summer intern in the Strategy, Policy and Review Department at the IMF. All remaining errors are ours. 4 In the last decade, however, EMDEs have started to increasingly invest abroad as well (see Obstfeld (2012b)). 5 For instance,
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    Broner and Ventura (2016)
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    point out that financial globalization has sometimes led to capital flows that are volatile and procyclical. flows and economic growth (see Easterly, Islam, and Stiglitz 2001 and Ramey and Ramey 1995), maintaining a steady stream of capital flows is a policy priority in most EMDEs.
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    All remaining errors are ours. 4 In the last decade, however, EMDEs have started to increasingly invest abroad as well (see Obstfeld (2012b)). 5 For instance, Broner and Ventura (2016) point out that financial globalization has sometimes led to capital flows that are volatile and procyclical. flows and economic growth (see Easterly, Islam, and Stiglitz 2001 and
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    Ramey and Ramey 1995),
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    maintaining a steady stream of capital flows is a policy priority in most EMDEs. In this regard, the composition of the financial account is another crucial aspect to consider6. While FDI represent the majority of capital inflows for EMDEs, the surge and the subsequent collapse in inflows during the GFC was mainly driven by shorter-term instruments like other investment flows, an
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    The existing empirical literature on the volatility of capital flows can be classified into two strands.7 The first strand focuses on analyzing the difference in volatility between the capital flows to emerging and advanced economies. For instance,
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    Rigobon and Broner (2005)
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    show that the higher volatility in EMDEs is primarily due to these economies’ propensity to build up imbalances, which generates more persistent shocks and a higher likelihood of international contagion.
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    Neumann et al. (2009) show that financial integration tends to increase the volatility of FDI in emerging economies, while reducing that of other debt flows in mature economies.
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    Bekaert and Harvey (1997), Lagoarde-Segot (2009) and
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    Umutlu et al. (2010), by focusing on prices rather than quantities, conclude that financial liberalization reduces the volatility of stock market returns in emerging economies.8 Broto et al. (2011) analyze the determinants of the volatility of various types of net capital inflows to EMDEs, by using annual data over the period 1980-2006.
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    Their main finding is that, since 2000, global factors have become increasingly significant relative to countryspecific drivers. However, they also identify some domestic macroeconomic and financial factors that appear to reduce the volatility of certain instruments without increasing that of the others. In another study,
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    Byrne and Fiess (2016),
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    using data until 2008Q4, show how the volatility of capital inflows to emerging markets is mainly driven by the US long-term interest rates and the real commodity prices. Despite being a focus of policymakers, capital flow volatility is usually quantified in policy and academic discussions using a crude measure of standard deviation of flows
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    Definitions are taken from the sixth edition of the Balance of Payments and International Investment Position Manual (BPM6). 7 Previous theoretical contributions include Bacchetta and Van Wincoop (1998), Aghion et al. (2004) and
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    Martin and Rey (2006).
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    8 See Chuang et al. (2009) and Jinjarak et al. (2011) for an analysis of the relationship between stock returns and volumes. over a specified period. Given the importance of this issue, a better understanding of volatility in capital flows is warranted.
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    In particular, the volatility spiked up during the taper tantrum episode. These bouts of increase in volatility thus remain a policy challenge for EMDEs, particularly in the current context of capital flow slowdown
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    (IMF 2016a,b),
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    where even small swings in flows can lead to substantial net outflows or sudden stops14. 12 In this section, the aggregates represent total capital flows divided by total GDP.
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    Nonetheless, as already highlighted in the previous section, there exist some fundamental divergences in the dynamics of individual components (or instruments) of the financial account. It is therefore necessary to extend the analysis to single instruments as well. 15 Refer to
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    Geweke (2005).
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    We prefer this test as it allows us to control for autocorrelation of the volatility estimates. 16 With the notable exception of GARCH estimates for gross outflows. RW GARCH ARIMA GIs GOs NIs GIs GOs NIs GIs GOs NIs EMDEs Statistic 0.27 0.62 0.01 0.64 0.01 1.46 0.27 5.85 0.09 P-value 0.60 0.43 0.92 0.42 0.94 0.23 0.60 0.02 0.77 Table 1: statistics and p-values of the Ge
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    than before19. 17 The differences in the behavior of net flows between AEs and EMDEs is also due to the fact that, while in AEs capital flows are used for both risk-sharing and portfolio diversification, in EMDEs, by contrast, capital flows are not only instrumental for risk-sharing, but also to have access to greater external financing (see
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    Canuto and Ghosh (2013),
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    Ch. 3). 18 Measures for gross outflows and net inflows, however, are available upon request. Broadly speaking, the volatility measures for gross inflows and net inflows are similar, but those of gross outflows are different. 19 Even if, in certain cases, volatility seems to have declined over time, the results can be different when estimates are adjusted by the levels (the idea is very similar
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    The cases of Latin America and Europe, however, stand out, as the volatility of debt inflows decreased in 2007-2009 and bounced back to unprecedented levels in the first quarters of 2010. 23 See for instance
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    Lane (2013), Lane and Milesi-Ferretti (2017).
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    Figure 5: ARIMA volatility estimates for gross capital inflows in EMDEs by regions and instruments. Notes: AS=Asia, EU=Europe, LATAM=Latin America, MEAF=Middle East and Africa; measures are computed as average across countries and expressed as % share of GDP.
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    of equation (1), one gets: !"#.=AT.UVT#+T/%+2ATAEX)N(VT#,VE#)EYT+T/% (2) where Z is the number of elements in vector J# and VT# represents the qth element of the same vector. Using a first-order Taylor expansion, equation (2) can be approximated as: !"#.=USOA′J#≈ A′\(J#)|^U(J#)A (3) 24 See, for instance,
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    Ahmed and Zlate (2014).
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    25 Notably, in equation (28), Devereux and Sutherland (2009) show how home-country holdings of foreign equities and bonds depend on a set of domestic and foreign indicators. As we mainly focus on gross inflows (so changes in foreign holdings of domestic assets), we can flip the interpretation of this relationship so to derive a direct link between foreign-country holdings of domestic equities a
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    Therefore, given the N=33 EMDEs in our sample, the baseline dynamic panel regression model is: !"#.=d%!"#0%.+eTf"T#+T/%+5"#, g=1,...,_, where f"T# are growth rates of the regressors and 5"#=i"+j#+k"#, so that we control for the presence of both country and time fixed-effects. 26 See, for instance, Broto et al. (2011) and
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    Rey (2013,2016).
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    27In our specification, we don’t include proxies for convariances among the regressors. This does not generate endogeneity issues, as explained below. 28 Since in equation (3) the variance is linearized around the steady state, we demean the regressors, using the trend estimated via HP filtering as a proxy for the steady state.
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    However, there is a strand of the literature arguing that long-run interest rates may be at least as important for capital flows as short-run rates, if investors prefer to diversify assets with short maturities and assets of different maturity are imperfect substitutes (see
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    Bernanke et al. (2011)).
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    As a robustness check, we ran regressions using long-term interest rates, but the key results were substantially unchanged. 30 In this case, we follow Ghosh et al. (2014) and Ghosh, Ostry and Qureshi (2016) and we use changes in the S&P 500 realized returns volatility as an index for global risk aversion.
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    For a discussion about the relationship between a country’s Net International Investment Position (NIIP) and its current account balance, as well as the risk associated to the build-up of persistent current account imbalances, refer to
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    Obstfeld (2012b) and Lane (2013).
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    Following Broto et al. (2011), we correct the covariance matrix as suggested by Driscoll and Kraay (1998), to account for both serial and spatial (cross-sectional) correlations.32 In addition, we run two sets of diagnostic checks to detect whether this framework is capable of addressing the issues of endogeneity in regressors and cross-sectional dependence in the residuals 33.
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    For a discussion about the relationship between a country’s Net International Investment Position (NIIP) and its current account balance, as well as the risk associated to the build-up of persistent current account imbalances, refer to Obstfeld (2012b) and Lane (2013). Following Broto et al. (2011), we correct the covariance matrix as suggested by
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    Driscoll and Kraay (1998), to
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    account for both serial and spatial (cross-sectional) correlations.32 In addition, we run two sets of diagnostic checks to detect whether this framework is capable of addressing the issues of endogeneity in regressors and cross-sectional dependence in the residuals 33.
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    Indeed, the results from the next section suggest that the baseline regressions may be missing some dynamics (both across countries and over time) that need to be further explored. 32 To do so, we used the ----- command in Stata (see
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    Hoechle (2007)).
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    33 In the former case, we use the Durbin-Wu-Hausman test, while in the latter we perform the test for weak crosssectional dependence proposed by Pesaran (2015). Indicators of the US economic stance (US real GDP and US CPI) generally have a negative effect over the volatility of EMDEs capital inflows, except for FDI and portfolio equity inflows.
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    the baseline regressions may be missing some dynamics (both across countries and over time) that need to be further explored. 32 To do so, we used the ----- command in Stata (see Hoechle (2007)). 33 In the former case, we use the Durbin-Wu-Hausman test, while in the latter we perform the test for weak crosssectional dependence proposed by
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    Pesaran (2015).
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    Indicators of the US economic stance (US real GDP and US CPI) generally have a negative effect over the volatility of EMDEs capital inflows, except for FDI and portfolio equity inflows. All in all, a 1% increase either in the US real GDP or in the US CPI decreases the volatility of gross capital inflows by 6.31% and 2.17% of GDP respectively.
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    Domestic Factors Tables 3 and 4 also show that domestic factors are not relevant drivers of capital flows volatility in EMDEs, thus implying a predominance of global factors influence. While this finding is in line with evidence provided by some of the existing literature (see, for instance, Broto et al (2011) and
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    Byrne and Fiess (2016)),
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    yet it has also been pointed out how these effects over capital flow levels are highly heterogenous across countries, due to differences in the quality of domestic institutions, country risk and the strength of domestic macroeconomic fundamentals (Fratzscher (2012), IMF (2016a)).
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    finding is in line with evidence provided by some of the existing literature (see, for instance, Broto et al (2011) and Byrne and Fiess (2016)), yet it has also been pointed out how these effects over capital flow levels are highly heterogenous across countries, due to differences in the quality of domestic institutions, country risk and the strength of domestic macroeconomic fundamentals
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    (Fratzscher (2012), IMF (2016a)).
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    Moreover, it has also been shown that these effects are characterized by a certain degree of variation over time, since the GFC has actually strengthen the role played by global factors in driving the level of capital flows.
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    . *** p<0.01, ** p<0.05, * p<0.1.Some countries have been excluded from the pool due to lack of an adequate number of observations Extended regression: the role of commodity prices In this session, we expand the baseline regression to account for commodity prices, as they are deemed influential drivers for capital flows towards both exporting and importing economies
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    (Byrne and Fiess (2016)).
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    Notably, a positive shock to commodity prices increases bank investments to commodity exporting countries, whose financial institutions, in turn, reinvest their increased revenues into the international financial markets, to diversify their portfolios.35 Nonetheless, changes in commodity prices are also positively correlated with capital inflows to commodity importers.
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    In particular, and more in line with expectations, an increase in S&P 500 realized returns volatility exerts a positive effect on the volatility of almost all the instruments, with estimates ranging from 0.48% for total private investments to 5.9% for FDI (with an overall effect of about 5% of GDP). 35 See Arezki, Mazarei, and Prasad (2015) and
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    Arezki, Obstfeld and Milesi-Ferretti (2016).
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    36 See Ahmed, Curcuru and Warnock (2015). (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) VARIABLES Total FDI Portfolio Portfolio Debt Portfolio Equity Other Banks Official Non-banks Total private σ2(t-1) 0.825*** 0.658*** 0.306*** 0.317*** 0.657*** 0.241*** 0.762*** 0.202*** 0.735*** 0.741*** (0.0499) (0.0675) (0.0197) (0.0178) (0.0705) (0.0258) (0.0545) (0.0216) (0.0411) (0.0625) US shadow ra
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    The latter one, on the other hand, can be identified with the taper tantrum episode, but it does not coincide with any significant change in the effects of US M2. Finally, commodity price index seems to exert a significant effect on capital inflow volatility only in the pre-GFC period. This is a new finding compared to existing literature
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    (Byrne and Fiess (2016)),
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    as it shows how, in the last 10 years, the influence of commodity prices has been greatly reduced compared to that of other global factors. -----------------------<HDU 86-VKDGRZ-UDWH -----------------------<HDU 5HDOLVHG-UHWXUQV-YRODWLOLW\ -----------------------<HDU 86-5HDO-*'3 <HDU 86-&3, -----------------------<HDU 86-0-----------------------<HDU 86-&XUUHQW-$FFRXQW -----------------------&R
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    —the push factors are more important than pull factors—is yet another reminder that the determinants of the level of capital flows can be very different from the determinants of the volatility of capital flows. However, we also show how certain domestic (pull) factors have started to play a relevant role after the GFC. This is even more interesting against the backdrop of recent studies
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    (IMF 2016)
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    that show how the recent capital flow slowdown can be predominantly attributed to the broader emerging markets economic growth slowdown. While this study is a reminder that the findings from the push vs pull factors literature do not always hold when it comes to the study of capital flow volatility, it is still possible to detect some commonalities in the effects of push and pull factors over
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