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Alexander Al-Haschimi

24 January 2023
WORKING PAPER SERIES - No. 2767
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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 - ARTICLE
Economic Bulletin Issue 2, 2022
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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
21 September 2021
OCCASIONAL PAPER SERIES - No. 269
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Abstract
The ECB’s price stability mandate has been defined by the Treaty. But the Treaty has not spelled out what price stability precisely means. To make the mandate operational, the Governing Council has provided a quantitative definition in 1998 and a clarification in 2003. The landscape has changed notably compared to the time the strategy review was originally designed. At the time, the main concern of the Governing Council was to anchor inflation at low levels in face of the inflationary history of the previous decades. Over the last decade economic conditions have changed dramatically: the persistent low-inflation environment has created the concrete risk of de-anchoring of longer-term inflation expectations. Addressing low inflation is different from addressing high inflation. The ability of the ECB (and central banks globally) to provide the necessary accommodation to maintain price stability has been tested by the lower bound on nominal interest rates in the context of the secular decline in the equilibrium real interest rate. Against this backdrop, this report analyses: the ECB’s performance as measured against its formulation of price stability; whether it is possible to identify a preferred level of steady-state inflation on the basis of optimality considerations; advantages and disadvantages of formulating the objective in terms of a focal point or a range, or having both; whether the medium-term orientation of the ECB’s policy can serve as a mechanism to cater for other considerations; how to strengthen, in the presence of the lower bound, the ECB’s leverage on private-sector expectations for inflation and the ECB’s future policy actions so that expectations can act as ‘automatic stabilisers’ and work alongside the central bank.
JEL Code
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
21 September 2021
OCCASIONAL PAPER SERIES - No. 264
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Abstract
This paper summarises the findings of the Eurosystem’s Expert Group on Inflation Expectations (EGIE), which was one of the 13 work streams conducting analysis that fed into the ECB’s monetary policy strategy review. The EGIE was tasked with (i) reviewing the nature and behaviour of inflation expectations, with a focus on the degree of anchoring, and (ii) exploring the role that measures of expectations can play in forecasting inflation. While it is households’ and firms’ inflation expectations that ultimately matter in the expectations channel, data limitations have meant that in practice the focus of analysis has been on surveys of professional forecasters and on market-based indicators. Regarding the anchoring of inflation expectations, this paper considers a number of metrics: the level of inflation expectations, the responsiveness of longer-term inflation expectations to shorter-term developments, and the degree of uncertainty. Different metrics can provide conflicting signals about the scale and timing of potential unanchoring, which underscores the importance of considering all of them. Overall, however, these metrics suggest that in the period since the global financial and European debt crises, longer-term inflation expectations in the euro area have become less well anchored. Regarding the role measures of inflation expectations can play in forecasting inflation, this paper finds that they are indicative for future inflationary developments. When it comes to their predictive power, both market-based and survey-based measures are found to be more accurate than statistical benchmarks, but do not systematically outperform each other. Beyond their role as standalone forecasts, inflation expectations bring forecast gains when included in forecasting models and can also inform scenario and risk analysis in projection exercises performed using structural models. ...
JEL Code
D84 : Microeconomics→Information, Knowledge, and Uncertainty→Expectations, Speculations
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
21 September 2020
ECONOMIC BULLETIN - ARTICLE
Economic Bulletin Issue 6, 2020
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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
30 April 2019
OCCASIONAL PAPER SERIES - No. 221
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Abstract
The studies summarised in this paper focus on the economic implications of euro area firms’ participation in global value chains (GVCs). They show how, and to what extent, a large set of economic variables and inter-linkages have been affected by international production sharing. The core conclusion is that GVC participation has major implications for the euro area economy. Consequently, there is a case for making adjustments to standard macroeconomic analysis and forecasting for the euro area, taking due account of data availability and constraints.
JEL Code
F6 : International Economics→Economic Impacts of Globalization
F10 : International Economics→Trade→General
F14 : International Economics→Trade→Empirical Studies of Trade
F16 : International Economics→Trade→Trade and Labor Market Interactions
E3 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles
15 September 2016
OCCASIONAL PAPER SERIES - No. 178
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Abstract
Global trade has been exceptionally weak over the past four years. While global trade grew at approximately twice the rate of GDP prior to the Great Recession, the ratio of global trade to GDP growth has declined to about unity since 2012. This paper assesses to what extent the change in the relationship between global trade and global economic activity is a temporary phenomenon or constitutes a lasting change. It finds that global trade growth has been primarily dampened by two factors. First, compositional factors, including geographical shifts in economic activity and changes in the composition of aggregate demand, have weighed on the sensitivity of trade to economic activity. Second, structural developments, such as waning growth in global value chains, a rise in non-tariff protectionist measures and a declining marginal impact of financial deepening, are dampening the support from factors that boosted global trade in the past. Notwithstanding the particularly pronounced weakness in 2015 that is assessed to be mostly a temporary phenomenon owing to a number of country-specific adverse shocks, the upside potential for trade over the medium term appears to be limited. The
JEL Code
F10 : International Economics→Trade→General
F13 : International Economics→Trade→Trade Policy, International Trade Organizations
F14 : International Economics→Trade→Empirical Studies of Trade
F15 : International Economics→Trade→Economic Integration
16 December 2014
WORKING PAPER SERIES - No. 1749
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Abstract
This paper links granular data of financial institutions to global macroeconomic variables using an infinite-dimensional vector autoregressive (IVAR) model framework. The approach taken allows for an assessment of the two-way links between the financial system and the macroeconomy, while accounting for heterogeneity among financial institutions and the role of international linkages in the transmission of shocks. The model is estimated using macroeconomic data for 21 countries and default probability estimates for 35 euro area financial institutions. This framework is used to assess the impact of foreign macroeconomic shocks on default risks of euro area financial firms. In addition, spillover effects of firm-specific shocks are investigated. The model captures the important role of international linkages, showing that economic shocks in the US can generate a rise in the default probabilities of euro area firms that are of a significant magnitude compared to recent historical episodes such as the financial crisis. Moreover, the potential heterogeneity across financial firms
JEL Code
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
G33 : Financial Economics→Corporate Finance and Governance→Bankruptcy, Liquidation