Exploring the Dynamics of Global Liquidity

Chen, Sally; Liu, Philip L.‐F.; Maechler, Andréa M.; Marsh, Chris; Saksonovs, Sergejs; Shin, Hyun Song

doi:10.2139/ssrn.2169785

Cited by 7 publications

(3 citation statements)

References 37 publications

(19 reference statements)

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“…If limited credit is a result of reduced investment opportunities, leading to a fall in demand for credit, the attendant growth prospect could well be different from a supply shock, driven by, say, a tightening in bank lending standards. One study finds that in general, demand shocks to credit 12 have longer lasting and stronger effects on real GDP than supply shocks (Chen et al, 2012). A novel approach may be to proxy supply shocks using bank loan write-offs.…”

Section: A Analytical Considerationsmentioning

confidence: 99%

Private Sector Deleveraging and Growth Following Busts

et al. 2015

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Section: A Analytical Considerationsmentioning

confidence: 99%

Private Sector Deleveraging and Growth Following Busts

et al. 2015

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“…Alessi and Detken (2011) used liquidity as a predictive indicator to study asset prices during boom/bust cycles that can have serious economic consequences. Chen et al (2012) recognized the importance of decision-making and explored the significance of liquidity for the global economy and for policymakers through multiple sets of liquidity indicators.…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms applied to the estimation of liquidity: the 10-year United States treasury bond

Luque Raya,

Luque Raya

2023

EJMBE

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PurposeHaving defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.Design/methodology/approachConceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection.FindingsThe predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.Originality/valueBetter understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.

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“…It has sometimes been used to refer to the stance of monetary policy in major currency areas. In this monetary aggregates view, global liquidity is a major determinant of aggregate demand, goods price inflation, and associated macroeconomic spillovers across countries (Baks & Kramer 1999, Rueffer & Stracca 2006, Chen et al 2012. Since the GFC, however, policy makers and academics alike have placed an increasing emphasis on the financial stability implications of global liquidity (for a broad overview, see CGFS 2011).…”

Section: Introductionmentioning

confidence: 99%

Global Liquidity: A Selective Review

et al. 2017

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Global liquidity is a catch-all term that is used to denote the combination of easier financing conditions, capital inflows, and exchange rate appreciation. An approach based on the activities of internationally active financial institutions sheds light on the conceptual underpinnings and the economic mechanisms involved in the transmission of global liquidity. The analysis highlights the role of international funding currencies, especially the US dollar. The analysis also motivates a set of global liquidity indicators based on the size and currency composition of balance sheets.

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Exploring the Dynamics of Global Liquidity

Cited by 7 publications

References 37 publications

Private Sector Deleveraging and Growth Following Busts

Private Sector Deleveraging and Growth Following Busts

Machine learning algorithms applied to the estimation of liquidity: the 10-year United States treasury bond

Global Liquidity: A Selective Review

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