Nėra lietuvių kalba
Martino Ricci
International & European Relations
- Division
International Policy Analysis
- Current Position
-
Senior Economist
- Fields of interest
-
International Economics,Macroeconomics and Monetary Economics
- Education
- 2012-2016
PhD in Economics, University of Milan
- 2010-2011
MA in Economics and International Relations, University of East Anglia
- 2006-2008
MSc in Economics, University of Roma Tre
- Professional experience
- 2023-
Senior Economist, International Policy Analysis Division, Directorate General International & European Relations, European Central Bank
- 2021-2023
Senior Economist, External Developments Division, Directorate General International & European Relations, European Central Bank
- 2018-2021
Economist, External Developments Division, Directorate General International & European Relations, European Central Bank
- 2016-2018
Research Analyst, External Developments Division, Directorate General International & European Relations, European Central Bank
- 7 August 2024
- WORKING PAPER SERIES - No. 2972Details
- Abstract
- In this paper, we investigate the presence of non-linearities in the transmission of geopolitical risk (GPR) shocks. Our methodology involves incorporating a non-linear function of the identified shock into a VARX model and examining its impulse response functions and historical decomposition. We find that the primary transmission channel of such shocks is associated with heightened uncertainty,which significantly escalates only with substantially large GPR shocks (i.e., above 4 standard deviations). This increase in uncertainty prompts precautionary saving behaviors, exerting a strong impact on consumption and reducing activity. The response of inflation is more subdued, reflecting both diminished demand and heightened uncertainty, which influence prices in opposing directions.
- JEL Code
- C30 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→General
D80 : Microeconomics→Information, Knowledge, and Uncertainty→General
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
F44 : International Economics→Macroeconomic Aspects of International Trade and Finance→International Business Cycles
H56 : Public Economics→National Government Expenditures and Related Policies→National Security and War
- 24 January 2023
- WORKING PAPER SERIES - No. 2767Details
- Abstract
- We develop a measure of overall financial risk in China by applying machine learning techniques to textual data. A pre-defined set of relevant newspaper articles is first selected using a specific constellation of risk-related keywords. Then, we employ topical modelling based on an unsupervised machine learning algorithm to decompose financial risk into its thematic drivers. The resulting aggregated indicator can identify major episodes of overall heightened financial risks in China, which cannot be consistently captured using financial data. Finally, a structural VAR framework is employed to show that shocks to the financial risk measure have a significant impact on macroeconomic and financial variables in China and abroad.
- JEL Code
- C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C65 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Miscellaneous Mathematical Tools
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
F44 : International Economics→Macroeconomic Aspects of International Trade and Finance→International Business Cycles
G15 : Financial Economics→General Financial Markets→International Financial Markets
- 24 March 2022
- ECONOMIC BULLETIN - ARTICLEEconomic Bulletin Issue 2, 2022Details
- Abstract
- Recent tensions in China’s real estate market have highlighted the risks inherent in the country’s highly leveraged corporate sector. These risks have been building up for some time, as high investment rates have coincided with high levels of debt accumulation. Moreover, the source of debt has moved beyond the traditional banking sector, with non-bank financial institutions providing financing which is less stable and more susceptible to sudden changes in investor sentiment. In addition, tensions in large corporate sectors could be transmitted to the rest of the economy through a number of channels. These channels include households, which are themselves increasingly leveraged and whose wealth is significantly exposed to the real estate market. A wider Chinese growth slowdown could, in turn, have global repercussions, given the size of the Chinese economy, its important global trade linkages and the central role it plays in international commodity markets. Against this backdrop, this article will review the rise in financial risks in China’s economy stemming from increasing private sector leverage, the interconnectedness between the financial and non-bank financial sectors, and households’ rising debt exposures.
- JEL Code
- E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
E6 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
G2 : Financial Economics→Financial Institutions and Services
G5 : Financial Economics
- 26 April 2021
- WORKING PAPER SERIES - No. 2541Details
- Abstract
- This paper sheds light on the impact of global macroeconomic uncertainty on the euro area economy. We build on the methodology proposed by Jurado et al. (2015) and estimate global as well as country-specific measures of economic uncertainty for fifteen key euro area trade partners and the euro area. Our measures display a clear counter-cyclical pattern and line up well to a wide range of historical events generally associated with heightened uncertainty. In addition, following Piffer and Podstawski (2018), we estimate a Proxy SVAR where we instrument uncertainty shocks with changes in the price of gold around specific past events. We find that, historically, global uncertainty shocks have been important drivers of fluctuations in euro area economic activity, with one standard deviation increase in the identified uncertainty shock subtracting around 0.15 percentage points from euro area industrial production on impact.
- JEL Code
- D81 : Microeconomics→Information, Knowledge, and Uncertainty→Criteria for Decision-Making under Risk and Uncertainty
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
F41 : International Economics→Macroeconomic Aspects of International Trade and Finance→Open Economy Macroeconomics
F62 : International Economics→Economic Impacts of Globalization→Macroeconomic Impacts
- 3 March 2021
- WORKING PAPER SERIES - No. 2530Details
- Abstract
- In a highly interlinked global economy a key question is how foreign shocks transmit to the domestic economy, how domestic shocks affect the rest of the world, and how policy actions mitigate or amplify spillovers. For policy analysis in such a context global multi-country macroeconomic models that allow a structural interpretation are needed. In this paper we present a revised version of ECB-Global, the European Central Bank's global macroeconomic model. ECB-Global 2.0 is a semi-structural, global multi-country model with rich channels of international shock propagation through trade, oil prices and global financial markets for the euro area, the US, Japan, the UK, China, oil-exporting economies, Emerging Asia, and a rest-of-the-world block. Relative to the original version of model, ECB-Global 2.0 features dominant-currency pricing, tariffs and trade diversion. We illustrate the usefulness of ECB-Global exploring scenarios motivated by recent trade tensions between China and the US.
- JEL Code
- C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
E30 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→General
E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
- 21 September 2020
- ECONOMIC BULLETIN - ARTICLEEconomic Bulletin Issue 6, 2020Details
- Abstract
- This article will trace the decline and subsequent recovery of China’s economy following the outbreak of the coronavirus (COVID-19). It employs high-frequency data to assess the speed at which activity in different sectors of the economy is normalising after businesses were allowed to resume operations. One particular focus will be on differentiating between the industrial and services sectors, which are subject to different health and safety measures. The article finds that China’s economic activity rose from a trough of around 20% of normal levels in February 2020 to 90% in the span of just three months. While production capacity recovered swiftly, activity normalised more gradually in the services sector, where COVID-19 containment measures had continued to weigh heavily. The recovery was driven primarily by private domestic demand and the authorities’ policy response, as the normalisation in China coincided with the implementation of lockdown measures by many of its trading partners and hence also with a fall in external demand. Looking ahead, uncertainty and risks surrounding the recovery path remain exceptionally high, owing in large part to the uncertainty regarding how the COVID-19 pandemic will develop and if and when a medical solution to the virus can be found.
- JEL Code
- E2 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy
E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
E6 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
F1 : International Economics→Trade
G1 : Financial Economics→General Financial Markets
- 6 February 2020
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 1, 2020Details
- Abstract
- Over the past year the global economy has transitioned from a robust and synchronised expansion to a widespread slowdown. Global growth has weakened on the back of soft investment, which was also a key driver of global trade’s sharp fall into contractionary territory in the first half of 2019 (see Chart A). The slowdown in global investment and trade has occurred in an environment of rising trade tensions between the United States and China, slowing Chinese demand, (geo-)political tensions, Brexit and idiosyncratic stresses in several emerging economies, with rising uncertainty magnifying the negative impact. Against this backdrop, this box assesses the role of uncertainty in the recent slowdown of global investment and trade.
- JEL Code
- F21 : International Economics→International Factor Movements and International Business→International Investment, Long-Term Capital Movements
F44 : International Economics→Macroeconomic Aspects of International Trade and Finance→International Business Cycles
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
- 25 September 2018
- WORKING PAPER SERIES - No. 2179Details
- Abstract
- In this paper, we analyse the effects of a shock to global financial uncertainty and risk aversion on real economic activity. To this end, we extract a global factor, which explains approximately 40% of the variance of about 1000 risky asset returns from around the world. We then study how shocks to the factor affect economic activity in 36 advanced and emerging small open economies by estimating local projections in a panel regression framework. We find the output responses to be quite heterogeneous across countries but, in general, negative and persistent. Furthermore, the effects of shocks to the global factor are stronger in countries with a higher degree of trade and/or financial openness, as well as in countries with higher levels of external debt, less developed financial sectors, and higher risk rating.
- JEL Code
- C30 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→General
F41 : International Economics→Macroeconomic Aspects of International Trade and Finance→Open Economy Macroeconomics
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
F65 : International Economics→Economic Impacts of Globalization→Finance
- 18 April 2017
- WORKING PAPER SERIES - No. 2045Details
- Abstract
- In a highly interlinked global economy a key question for policy makers is how foreign shocks and policies transmit to the domestic economy. We develop a semi-structural multi-country model with rich real and financial channels of international shock propagation for the euro area, the US, Japan, the UK, China, oil-exporting economies and the rest of the world: ECB-Global. We illustrate the usefulness of ECB-Global for policy analysis by presenting its predictions regarding the global spillovers from a US monetary policy tightening, a drop in oil prices and a growth slowdown in China. The impulse responses implied by ECB-Global are well in line with those generated by other global models, with international spillovers in ECB-Global generally on the high side given its rich real and financial spillover structure.
- JEL Code
- C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
E30 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→General
E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
- 2023
- IMF Economic Review
- 2020
- Journal of International Money and Finance
- 2018
- Economic Modelling