Ni na voljo v slovenščini.
Manfred Kremer
Research
- Division
Financial Research
- Current Position
-
Adviser
- Fields of interest
-
Financial Economics,Macroeconomics and Monetary Economics
- Education
- 2017
PhD in Economics (Dr. rer. oec.), University of Wuppertal, Germany
- 1989
MA in Economics (Diplom-Volkswirt), University of Duisburg, Germany
- Professional experience
- 2020-
Seconded to the Strategy Review Project Office, European Central Bank
- 2020-
Head of Financial Markets Research Section, Directorate General Research, European Central Bank
- 2009-2020
Deputy Head of Division - Financial Research Division, Directorate General Research, European Central Bank
- 2003-2009
Deputy Head of Division - Capital Markets and Financial Structure Division, Directorate General Economics, European Central Bank
- 2000-2003
Economist and Principal Economist - Capital Markets and Financial Structure Division, Directorate General Economics, European Central Bank
- 1995-2000
Economist - Economics Department, Capital Markets Section, Deutsche Bundesbank, Frankfurt, Germany
- 1989-1995
Full-time research and teaching fellow - Economics Department, Money and Credit, University of Duisburg, Germany
- 29 January 2024
- RESEARCH BULLETIN - No. 115Details
- Abstract
- When inflationary pressures started intensifying in 2022, the world’s major central banks faced a dilemma. They could rapidly tighten monetary policy at the risk of fuelling financial distress after years of ultra-low interest rates and balance sheet expansion. Or they could take a more gradual approach to fighting inflation that would protect the financial system, but risk high inflation becoming entrenched. While severe financial instability may be an unlikely event (or “tail risk”), it can have devastating macroeconomic consequences. Quantifying financial stability trade-offs therefore requires a way to gauge the three-way interaction between monetary policy, financial stability conditions and tail risks to the economy.
- 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
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
G01 : Financial Economics→General→Financial Crises
- 24 August 2023
- WORKING PAPER SERIES - No. 2842Details
- Abstract
- This paper proposes a general statistical framework for systemic financial stress indices which measure the severity of financial crises on a continuous scale. Several index designs from the financial stress and systemic risk literature can be represented as special cases. We introduce an enhanced daily variant of the CISS (composite indicator of systemic stress) for the euro area and the US. The CISS aggregates a representative set of stress indicators using their time-varying cross-correlations as systemic risk weights, computationally similar to how portfolio risk is computed from the risk characteristics of individual assets. A boot-strap algorithm provides test statistics. Single-equation and system quantile growth-at-risk regressions show that the CISS has stronger effects in the lower tails of the growth distribu-tion. Simulations based on a quantile VAR suggest that systemic stress is a major driver of the Great Recession, while its contribution to the COVID-19 crisis appears to be small.
- JEL Code
- C14 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Semiparametric and Nonparametric Methods: General
C31 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions, Social Interaction Models
C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G01 : Financial Economics→General→Financial Crises
- 31 July 2023
- WORKING PAPER SERIES - No. 2833Details
- Abstract
- We propose a novel empirical approach to inform monetary policymakers about the potential effects of policy action when facing trade-offs between financial and macroeconomic stability. We estimate a quantile vector autoregression (QVAR) for the euro area covering the real economy, monetary policy and measures of ex ante and ex post systemic risk representing financial stability. Policy implications are derived from scenario analyses where the associated costs and benefits are functions of the projected paths of the potentially asymmetric distributions of inflation and economic growth, allowing us to take a risk management perspective. One exercise considers the intertemporal financial stability trade-off in the context of the global financial crisis, where we find ex post evidence in favour of monetary policy leaning against the financial cycle. Another exercise considers the short-term financial stability trade-off when deciding the appropriate speed of monetary policy tightening to combat inflationary pressures in a fragile financial environment.
- 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
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
G01 : Financial Economics→General→Financial Crises
- 10 August 2022
- THE ECB BLOGDetails
- JEL Code
- 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
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
Related- 24 August 2022
- THE ECB BLOG
- 22 September 2021
- RESEARCH BULLETIN - No. 87.1Details
- Abstract
- When considering the use of macroprudential instruments to manage financial imbalances, macroprudential policymakers face an intertemporal trade-off between facilitating short-term expected growth and containing medium-term downside risks to the economy. To assist policymakers in assessing this trade-off, in this article we propose a risk management framework which extends the well-known notion of growth-at-risk to consider the entire predictive real GDP growth distribution, with a view to quantifying the macroprudential policy stance. A novel empirical model fitted to euro area data allows us to study direct and indirect interactions between financial vulnerabilities, financial stress and real GDP growth, incorporating non-linear amplification effects among all variables. Our framework can support policymakers by facilitating model-based macro-financial stress tests and model-based assessments of when to adjust macroprudential instruments.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models - Network
- Research Task Force (RTF)
- 2 June 2021
- WORKING PAPER SERIES - No. 2565Details
- Abstract
- Macro-prudential authorities need to assess medium-term downside risks to the real economy, caused by severe financial shocks. Before activating policy measures, they also need to consider their short-term negative impact. This gives rise to a risk management problem, an inter-temporal trade-off between expected growth and downside risk. Predictive distributions are estimated with structural quantile vector autoregressive models that relate economic growth to measures of financial stress and the financial cycle. An empirical study with euro area and U.S. data shows how to construct indicators of macro-prudential policy stance and to assess when interventions may be beneficial.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models - Network
- Research Task Force (RTF)
- 20 May 2021
- WORKING PAPER SERIES - No. 2556Details
- Abstract
- Macroprudential policymakers assess medium-term downside risks to the real economy arising from financial imbalances and implement policies aimed at managing those risks. In doing so, they face an inherent intertemporal trade-off between the expected growth and downside risks. This paper reviews the literature on Growth-at-Risk, embeds it in the wider literature on macroprudential policy, and proposes an empirical risk management framework that combines insights from the two literatures, by forecasting the entire real GDP growth distribution with a structural quantile vector autoregressive model. It accounts for direct and indirect interactions between financial vulnerabilities, financial stress and real GDP growth and allows for potential non-linear amplification effects. The framework provides policymakers with a macro-financial stress test to monitor downside risks to the economy and a macroprudential stance metric to quantify when interventions may be beneficial.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models - Network
- Discussion papers
- 20 May 2021
- DISCUSSION PAPER SERIES - No. 14Details
- Abstract
- Macroprudential policymakers assess medium-term downside risks to the real economy arising from financial imbalances and implement policies aimed at managing those risks. In doing so, they face an inherent intertemporal trade-off between the expected growth and downside risks. This paper reviews the literature on Growth-at-Risk, embeds it in the wider literature on macroprudential policy, and proposes an empirical risk management framework that combines insights from the two literatures, by forecasting the entire real GDP growth distribution with a structural quantile vector autoregressive model. It accounts for direct and indirect interactions between financial vulnerabilities, financial stress and real GDP growth and allows for potential non-linear amplification effects. The framework provides policymakers with a macro-financial stress test to monitor downside risks to the economy and a macroprudential stance metric to quantify when interventions may be beneficial.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
- 30 September 2019
- WORKING PAPER SERIES - No. 2319Details
- Abstract
- This paper develops composite indicators of financial integration within the euro area for both price-based and quantity-based indicators covering money, bond, equity and banking markets. Prior to aggregation, individual integration indicators are harmonised by applying the probability integral transform. We find that financial integration in Europe increased steadily between 1995 and 2007. The subprime mortgage crisis marked a turning point, bringing about a marked drop in both composite indicators. This fragmentation trend reversed when the European banking union and the ECB's Outright Monetary Transactions Programme were announced in 2012, with financial integration recovering more strongly when measured by price-based indicators. In a growth regression framework, we find that higher financial integration tends to be associated with an increase in per capita real GDP growth in euro area countries. This correlation is found to be stronger the higher a country's growth opportunities.
- JEL Code
- F36 : International Economics→International Finance→Financial Aspects of Economic Integration
F43 : International Economics→Macroeconomic Aspects of International Trade and Finance→Economic Growth of Open Economies
F45 : International Economics→Macroeconomic Aspects of International Trade and Finance
G01 : Financial Economics→General→Financial Crises
G15 : Financial Economics→General Financial Markets→International Financial Markets
- 15 October 2018
- WORKING PAPER SERIES - No. 2185Details
- Abstract
- In this paper we propose a composite indicator that measures multidimensional sovereign bond market stress in the euro area as a whole and in individual euro area member states. It integrates measures of credit risk, volatility and liquidity at short-term and long-term bond maturities into a broad measure of sovereign market stress. The statistical framework builds on that of the ECB’s Composite Indicator of Systemic Stress (CISS) developed by Hollo, Kremer and Lo Duca (2012), so that we call our metric the Composite Indicator of Systemic Sovereign Stress or “SovCISS”. We implement the SovCISS for eleven euro area member states and also present four options of a SovCISS for the entire monetary union. In addition, we suggest a linear decomposition of the SovCISS, singling out contributions of the different components and of the time-varying correlations across these components. Comparing develoments in the SovCISS and the CISS over the crisis period clearly illustrates the usefulness of the latter for the real-time monitoring of systemic instabilities in the financial system as a whole. Finally, an application of the country-specific SovCISS indicators to the VAR-based spillover literature suggests that stress mainly originates from a few euro area countries, and that spillover patterns vary over time.
- JEL Code
- C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
F45 : International Economics→Macroeconomic Aspects of International Trade and Finance
G01 : Financial Economics→General→Financial Crises
H63 : Public Economics→National Budget, Deficit, and Debt→Debt, Debt Management, Sovereign Debt
- 24 May 2018
- FINANCIAL STABILITY REVIEW - BOXFinancial Stability Review Issue 1, 2018Details
- Abstract
- After a period of about two years with fairly steady price increases and persistently low volatility, global stock markets experienced a notable price and volatility correction in early February 2018. Before this correction, policy authorities had become concerned about the benign volatility conditions, since low volatility may lead market participants to take on excessive risk and thereby create risks to financial stability (the “volatility paradox”). Against this background, a return to conditions of higher volatility could, on the one hand, be regarded as a welcome normalisation. On the other hand, a “disorderly” stock market correction with sharp price declines and large price fluctuations might itself pose risks to financial and economic stability.
- 12 March 2012
- WORKING PAPER SERIES - No. 1426Details
- Abstract
- This paper introduces a new indicator of contemporaneous stress in the financial system named Composite Indicator of Systemic Stress (CISS). Its specific statistical design is shaped according to standard definitions of systemic risk. The main methodological innovation of the CISS is the application of basic portfolio theory to the aggregation of five market-specific subindices created from a total of 15 individual financial stress measures. The aggregation accordingly takes into account the time-varying cross-correlations between the subindices. As a result, the CISS puts relatively more weight on situations in which stress prevails in several market segments at the same time, capturing the idea that financial stress is more systemic and thus more dangerous for the economy as a whole if financial instability spreads more widely across the whole financial system. Applied to euro area data, we determine within a threshold VAR model a systemic crisis-level of the CISS at which financial stress tends to depress real economic activity. Weekly updates of the CISS dataset at: sdw.ecb.europa.eu/browseSelection.do?node=9551138
- JEL Code
- G01 : Financial Economics→General→Financial Crises
G10 : Financial Economics→General Financial Markets→General
G20 : Financial Economics→Financial Institutions and Services→General
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy - Network
- Macroprudential Research Network
Annexes- 4 March 2013
- ANNEX
- 20 July 2006
- WORKING PAPER SERIES - No. 656Details
- Abstract
- The debate on the sustainability of public finances is closely related to the analysis of the financial and macroeconomic consequences of government debt accumulation. Focusing on the USA, Germany and Italy over the 1983
- JEL Code
- E6 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
H63 : Public Economics→National Budget, Deficit, and Debt→Debt, Debt Management, Sovereign Debt
- 2020
- Economics Letters
- 2017
- Economics Letters
- 2016
- International Economics and Economic Policy
- 2016
- Spanish Review of Financial Economics
- 2000
- Risikomanagement an internationalen FinanzmärktenPolitikoptionen zur Begrenzung von Hedge-Fonds-Risiken
- 2000
- Bank for International Settlements, Conference PapersThe dynamics of international asset price linkages and their effects on German stock and bond markets
- 1999
- Transmissionsmechanismen der GeldpolitikWhat is behind equity price movements in Germany?
- 1998
- Bank for International Settlements, Conference PapersWhat do asset price movements in Germany tell monetary policy makers?
- 1992
- Transforming economic systems: The case of Poland