Não disponível em português
Stefano Borgioli
- 8 November 2024
- WORKING PAPER SERIES - No. 2999Details
- Abstract
- In 1936, John Maynard Keynes proposed that emotions and instincts are pivotal in decision-making, particularly for investors. Both positive and negative moods can influence judgments and decisions, extending to economic and financial choices. Intuitions, emotional states, and biases significantly shape how people think and act. Measuring mood or sentiment is challenging, but surveys and data collection methods, such as confidence indices and consensus forecasts, offer some solutions. Recently, the availability of web data, including search engine queries and social media activity, has provided high-frequency sentiment measures. For example, the Italian National Statistical Institute’s Social Mood on Economy Index (SMEI) uses Twitter data to assess economic sentiment in Italy. The relationship between SMEI and financial market activity, specifically the FTSE MIB index and its volatility, is examined using a trivariate Vector Autoregressive model, taking into account the impact of the COVID-19 pandemic.
- JEL Code
- C1 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
G4 : Financial Economics
- 16 June 2023
- STATISTICS PAPER SERIES - No. 43Details
- Abstract
- In early 2020, the rapid spread of the coronavirus (COVID-19) quickly developed into a pandemic. This was followed by a sharp global economic downturn that was extraordinary in its speed, reach and scale. Within days of the first reported COVID-19 cases, the ECB daily Composite Indicator of Systemic Stress soared, and stress in several financial market segments began to flare up. These rapidly emerging financial strains could not be captured by a composite indicator of financial integration at the time because such indicators were low-frequency – principally monthly or even quarterly. The first aim of this paper is to present the steps taken in constructing a novel high-frequency price-based indicator of financial integration (HF-PIFI). Throughout the COVID-19 crisis, this novel indicator was responsive to public health data releases, incoming economic and financial data, and policy announcements. In this sense, it acted as a “thermometer”. The second aim of the paper is to use the novel indicator to identify events that were either supportive or damaging with respect to financial integration. This helps to distinguish between the main phases of the pandemic. The third aim of the paper is to review how the novel HF-PIFI indicator performed against the low-frequency indicators of financial integration. Looking back, the signals from the HF-PIFI index were quite accurate: the benefits of daily signals based on market data outweigh those of relying on a more limited set of low-frequency data.
- JEL Code
- C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G10 : Financial Economics→General Financial Markets→General
- 10 November 2020
- ECONOMIC BULLETIN - ARTICLEEconomic Bulletin Issue 7, 2020Details
- Abstract
- This article provides an overview of financial fragmentation during the coronavirus (COVID-19) crisis and the policies enacted to counter its effects. It does so through the lens of a set of high-frequency indicators for monitoring developments in financial integration. The readings from these indicators are then linked to unfolding economic and political events and to the main policy responses in monetary, fiscal and financial stability policy at the national and European levels. After initial sharp fragmentation, euro area financial integration broadly recovered to pre-crisis levels by mid-September, but not for all indicators. However, this recovery is still fragile and relies on an unprecedented amount of fiscal, monetary and prudential policy support.
- JEL Code
- G00 : Financial Economics→General→General
F36 : International Economics→International Finance→Financial Aspects of Economic Integration
- 20 December 2019
- STATISTICS PAPER SERIES - No. 32Details
- Abstract
- This paper describes the Macroprudential Database (MPDB) of the European CentralBank (ECB), which is an important component of the ECB’s Statistical DataWarehouse. After explaining the rationale for creating the MPDB, the paper illustrateshow it supports the macroprudential analysis conducted by the European System ofCentral Banks (ESCB), the European Systemic Risk Board (ESRB) and the nationalauthorities of the Single Supervisory Mechanism (SSM) and the European Union. Thestructure of the database and a broad overview of available indicators are thenpresented, with a description of the relevant confidentiality issues. Examples illustratehow the MPDB is used for monitoring purposes and econometric modelling. Finally,the paper discusses remaining data gaps and expected future enhancements of thedatabase.
- JEL Code
- C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
E60 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→General
- 3 May 2017
- STATISTICS PAPER SERIES - No. 20Details
- Abstract
- The Consolidated Banking Data CBD) are a key component of the ECB/ESCB statistical toolbox for financial stability analysis. This dataset, which contains all the relevant dimensions of systemic risk stemming from and affecting national banking systems, is compiled from firm-level supervisory returns. With the entry into force of the new set of European Banking Authority (EBA) Implementing Technical Standards on Supervisory Reporting in 2014, the whole CBD statistical framework had to be reshaped. In August 2015 the first data for the revised CBD were released. This paper deals with the main issues in the challenging endeavour of transposing firmlevel supervisory returns, often based on different accounting systems, into comprehensive aggregate statistics, while ensuring as far as possible continuity in the time series for indicators and aggregates calculated from different successive data models. At the same time, the new CBD has substantially enlarged the quantity and increased the quality of data, available to the users. This paper provides a description of the database, together with some examples drawn from it.
- JEL Code
- C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
- 15 January 2013
- OCCASIONAL PAPER SERIES - No. 140Details
- Abstract
- This occasional paper explores the Consolidated Banking Data (CBD), a key component of the ECB statistical toolbox for financial stability analysis. We show that non-consolidated, host-country Monetary Financial Institutions (MFI) balance sheet data, which constitutes a key source of input into monetary analysis, are a rather weak proxy for consolidated, home-country data and therefore cannot easily substitute CBD for the purposes of macro-prudential assessment. In addition, it is argued that, notwithstanding the relevance of large banks, medium-sized and small banks must also be taken into account in financial stability analysis, given their relevance in several EU countries and their different business models. A discussion follows on how aggregate data, broken down by bank size, can be used to complement micro data, in particular by signalling where and what to look for, again highlighting the differences between large banks on the one hand and small and medium sized banks on the other.
- JEL Code
- C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
- 30 April 2012
- OCCASIONAL PAPER SERIES - No. 133Details
- Abstract
- Shadow banking, as one of the main sources of financial stability concerns, is the subject of much international debate. In broad terms, shadow banking refers to activities related to credit intermediation and liquidity and maturity transformation that take place outside the regulated banking system. This paper presents a first investigation of the size and the structure of shadow banking within the euro area, using the statistical data sources available to the ECB/Eurosystem. Although overall shadow banking activity in the euro area is smaller than in the United States, it is significant, at least in some euro area countries. This is also broadly true for some of the components of shadow banking, particularly securitisation activity, money market funds and the repo markets. This paper also addresses the interconnection between the regulated and the non-bank-regulated segments of the financial sector. Over the recent past, this interconnection has increased, likely resulting in a higher risk of contagion across sectors and countries. Euro area banks now rely more on funding from the financial sector than in the past, in particular from other financial intermediaries (OFIs), which cover shadow banking entities, including securitisation vehicles. This source of funding is mainly shortterm and therefore more susceptible to runs and to the drying-up of liquidity. This finding confirms that macro-prudential authorities and supervisors should carefully monitor the growing interlinkages between the regulated banking sector and the shadow banking system. However, an in-depth assessment of the activities of shadow banking and of the interconnection with the regulated banking system would require further improvements in the availability of data and other sources of information.
- JEL Code
- G01 : Financial Economics→General→Financial Crises
G15 : Financial Economics→General Financial Markets→International Financial Markets
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
- 28 June 2007
- OCCASIONAL PAPER SERIES - No. 63Details
- Abstract
- This report analyses the financial position of non-financial enterprises in the euro area, in particular the amount of external financing, the choice between debt and equity and the composition and maturity structure of debt. It aims at identifying the main features of the euro area, as well as the peculiarities that depend on the country of origin and the sector of activity. Attention is also devoted to assessing whether a country's institutional features are correlated with different financial structures by firms. In light of the particular interest in the access of small and medium-sized enterprises (SMEs) to financing, the report also analyses how financing patterns differ across large, medium-sized and small enterprises. Finally, the report discusses the recent trends observed in the corporate finance landscape of the euro area over the past few years. Although it is still too early to pass final judgement, vast structural changes are underway that could have already influenced in a positive way in the availability of external funds for firms. All in all, a comprehensive understanding of corporate finance in the euro area is important from a monetary policy perspective, given its impact on the transmission mechanism and for productivity and economic growth. Moreover, such an understanding is also relevant from a financial stability perspective. A first assessment is now possible eight years into the third stage of Economic and Monetary Union (EMU), given that sufficient data have been accumulated during this period. This assessment is particularly important as the introduction of the single currency has had significant structural effects on the working of financial markets, increasing their size and liquidity, and fostering cross-border competition. The data available for this report generally cover the period 1995-2005, and the cut-off date for the statistics included is 10 March 2007.
- JEL Code
- D92 : Microeconomics→Intertemporal Choice→Intertemporal Firm Choice, Investment, Capacity, and Financing
G30 : Financial Economics→Corporate Finance and Governance→General
G10 : Financial Economics→General Financial Markets→General
O16 : Economic Development, Technological Change, and Growth→Economic Development→Financial Markets, Saving and Capital Investment, Corporate Finance and Governance
K40 : Law and Economics→Legal Procedure, the Legal System, and Illegal Behavior→General