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Business and Economics > Information Management

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Cover IMF Working Papers
Global Shocks Unfolding: Lessons from Fragile and Conflict-affected States
Jocelyn Boussard
,
Chiara Castrovillari
,
Tomohide Mineyama
,
Marta Spinella
, and
Maxwell Tuuli
This paper investigates the consequences of global shocks on a sample of low- and lower-middle-income countries with a particular focus on fragile and conflict-affected states (FCS). FCS are a group of countries that display institutional weakness and/or are negatively affected by active conflict, thereby facing challenges in macroeconomic policy management. Examining different global shocks associated with commodity prices, external demand, and financing conditions, this paper establishes that FCS economies are more vulnerable to these shocks compared to non-FCS peers. The higher sensitivity of FCS economies is mainly driven by procyclical fiscal responses, aggravated by the lack of effective spending controls and timely access to financial sources. External financing serves as a source of stability, partially mitigating the adverse impact of global shocks. This paper contributes to a better understanding of how conditions of fragility, which are on the rise in many parts of the world today, can amplify the effects of negative exogenous shocks. Its results highlight the diverse nature of underlying sources of vulnerabilities, spanning from fiscal and external buffers to institutional quality and economic structure, with lessons applicable to a broader set of countries. Efficient and timely external financial support from external partners, including international financial institutions, should help countries’ counter-cyclical responses to mitigate adverse shocks and achieve macroeconomic stability.
Keywords:
Global shocks; external financing; low-income countries; Fragile and conflict-affected states (FCS); FCS economy; Commodity tot shock; FCS list; shock variable; decline shock; Technology transfer; Exports; Commodity prices; Commodity price fluctuations; Global
Cover IMF Working Papers
Derivative Margin Calls: A New Driver of MMF Flows
Maddalena Ghio
,
Linda Rousova
,
Dilyara Salakhova
, and
German Villegas Bauer
During the March 2020 market turmoil, euro area money-market funds (MMFs) experienced significant outflows, reaching almost 8% of assets under management. This paper investigates whether the volatility in MMF flows was driven by investors’ liquidity needs related to derivative margin payments. We combine three highly granular unique data sources (EMIR data for derivatives, SHSS data for investor holdings of MMFs and Refinitiv Lipper data for daily MMF flows) to construct a daily fund-level panel dataset spanning from February to April 2020. We estimate the effects of variation margin paid and received by the largest holders of EURdenominated MMFs on flows of these MMFs. The main findings suggest that variation margin payments faced by some investors holding MMFs were an important driver of the flows of EUR-denominated MMFs domiciled in euro area.
Keywords:
liquidity risk; money market funds; big data; interconnectedness; non-bank financial intermediaries.; EUR-denominated MMFs; derivative margin calls; MMF flow; variation margin payment; VM flow; Stocks; Liquidity; Mutual funds; Nonbank financial institutions; Pension spending; Global; IMF working paper research Department; derivatives reform; margin payment; Derivative markets
Cover IMF Working Papers
World Seaborne Trade in Real Time: A Proof of Concept for Building AIS-based Nowcasts from Scratch
Diego A. Cerdeiro
,
Andras Komaromi
,
Yang Liu
, and
Mamoon Saeed
Maritime data from the Automatic Identification System (AIS) have emerged as a potential source for real time information on trade activity. However, no globally applicable end-to-end solution has been published to transform raw AIS messages into economically meaningful, policy-relevant indicators of international trade. Our paper proposes and tests a set of algorithms to fill this gap. We build indicators of world seaborne trade using raw data from the radio signals that the global vessel fleet emits for navigational safety purposes. We leverage different machine-learning techniques to identify port boundaries, construct port-to-port voyages, and estimate trade volumes at the world, bilateral and within-country levels. Our methodology achieves a good fit with official trade statistics for many countries and for the world in aggregate. We also show the usefulness of our approach for sectoral analyses of crude oil trade, and for event studies such as Hurricane Maria and the effect of measures taken to contain the spread of the novel coronavirus. Going forward, ongoing refinements of our algorithms, additional data on vessel characteristics, and country-specific knowledge should help improve the performance of our general approach for several country cases.
Keywords:
port visit; machine-learning technique; data from the Automatic Identification System; port call; trade activity; Imports; Exports; Oil; Machine learning; Trade balance; Global