Understanding global liquidity

Eickmeier, Sandra; Gambacorta, Leonardo; Hofmann, Boris

doi:10.1016/j.euroecorev.2014.01.015

Cited by 85 publications

(62 citation statements)

References 59 publications

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“…Finally, our analysis of borrower country characteristics draws on the earlier, broader literature on "push" and "pull" factors, which has attempted to explain cross-border capital flows using global factors such as global interest rates, and countries' growth rates, inflation and institutional characteristics (e.g., Calvo et al, 1996;andmore recently Fratzscher, 2011, andBrandao-Marques et al, 2013). 3 3 Among other relevant papers, Eickmeier et al (2013) use principal components analysis to identify common factors in a broad range of country-specific series of various financial variables. As a few factors can statistically account for much of the variability in the data, global factors are then thought to drive in large part cross-border flows.…”

Section: B What Are the Determinants Of Global Liquidity?mentioning

confidence: 99%

Global Liquidity and Drivers of Cross-Border Bank Flows

Cerutti

Claessens²,

Ratnovski³

2014

IMF Working Papers

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This paper studies the determinants of global liquidity using data on cross-border bank flows, with a longer time series and broader country sample than previous studies. We define global liquidity as non-price determinants of cross-border credit supply, consistent with its meaning as the "ease of financing" in international financial markets. We find that global liquidity is driven primarily by uncertainty (VIX), US monetary policy (term premia), and UK and Euro Area bank conditions (proxied by leverage and TED spreads). This expands on previous studies by highlighting non-US drivers of global liquidity, and is consistent with the dominant role of European banks in cross-border lending. We also show that borrowing countries can limit their exposures to global liquidity fluctuations through adapting their macro frameworks, capital flow management tools, and bank regulation. JEL Classification Numbers:F21, F34, G15, G18, G21, G28

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Section: B What Are the Determinants Of Global Liquidity?mentioning

confidence: 99%

Global Liquidity and Drivers of Cross-Border Bank Flows

Cerutti

Claessens²,

Ratnovski³

2014

IMF Working Papers

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“…In Eickmeier et al (2014), when applying this procedure to global monetary, credit and financial variables, they previously clean or purge such variables from the effect of some macroeconomic indicators (inflation and growth). Consequently, their estimated linear model contains two regressors: the liquidity index that is being identified and the macroeconomic variables used in the purging process.…”

Section: An Index Of Macroeconomic Stability (Ms)mentioning

confidence: 99%

“…In Eickmeier, Gambacorta, and Hofmann (2014), the authors broadly define global liquidity as the availability of funds for purchases of goods or assets from a global perspective. In that paper, they construct an index of global liquidity conditions based on the identification of several common factors from a large number of quantity and price-based liquidity variables.…”

Section: An Index Of Macroeconomic Stability (Ms)mentioning

confidence: 99%

Understanding Financial Fluctuations and Their Relation to Macroeconomic Stability

Guarata¹,

Pagliacci²

2017

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work is licensed under a Creative Commons IGO 3.0 AttributionNonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose, as provided below. No derivative work is allowed.Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license.Following a peer review process, and with previous written consent by the Inter-American Development Bank (IDB), a revised version of this work may also be reproduced in any academic journal, including those indexed by the American Economic Association's EconLit, provided that the IDB is credited and that the author(s) receive no income from the publication. Therefore, the restriction to receive income from such publication shall only extend to the publication's author(s). With regard to such restriction, in case of any inconsistency between the Creative Commons IGO 3.0 Attribution-NonCommercial-NoDerivatives license and these statements, the latter shall prevail.Note that link provided above includes additional terms and conditions of the license.The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent.http://www.iadb.org 2017 1 Abstract * This paper examines how financial fluctuations and macroeconomic stability interact in the case of Venezuela, acknowledging that financial conditions deteriorating the macroeconomic environment can arise with both good and bad macroeconomic performance. An empirical methodology is provided that constructs two indexes, which are fully interpretable and are constructed with a minimum set of assumptions applied to a large number of financial time series. Structural interpretation of indexes is pursued using a structural VAR (SVAR) that associates macroeconomic stability with financial indexes. For Venezuela, a deterioration of procyclical financial conditions relates to financial margin reductions and expansions in banks' balance sheets, which are mostly triggered by unexpected increases in net primary money creation. Such expansions tend to appear in situations of declining macroeconomic stability. Worse countercyclical financial conditions are instead associated with situations of rising bank profitability, deleveraging and increased banking instability. In this case, fragility tends to materialize in periods of ameliorated macroeconomic stability. JEL classifications: E30, G10, E00

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“…We use a broad data set along the lines of Breitung and Eickmeier (2014) and Eickmeier et al (2014) to construct our data-driven measure of the financial cycle.…”

Section: Data Descriptionmentioning

confidence: 99%

Dissecting the financial cycle with dynamic factor models

Menden

Proaño

2017

Quantitative Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe analysis of the financial cycle and its interaction with the macroeconomy has become a central issue for the design of macroprudential policy since the 2007-08 financial crisis. This paper proposes the construction of financial cycle measures for the US based on a large data set of macroeconomic and financial variables. More specifically, we estimate three synthetic financial cycle components that account for the majority of the variation in the data set using a dynamic factor model. We investigate whether these financial cycle components have significant predictive power for economic activity, inflation and short-term interest rates by means of Granger causality tests in a factor-augmented VAR set-up. Further, we analyze if the synthetic financial cycle components have significant forecasting power for the prediction of economic recessions using dynamic probit models. Our main findings indicate that all financial cycle measures improve the quality of recession forecasts significantly. In particular, the factor related to financial market participants' uncertainty and risk aversion -related to Rey's (2013) global financial cycle -seems to serve as an appropriate early warning indicator for policymakers.-------

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Understanding global liquidity

Cited by 85 publications

References 59 publications

Global Liquidity and Drivers of Cross-Border Bank Flows

Global Liquidity and Drivers of Cross-Border Bank Flows

Understanding Financial Fluctuations and Their Relation to Macroeconomic Stability

Dissecting the financial cycle with dynamic factor models

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