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Matthias Sydow

9 June 2005
OCCASIONAL PAPER SERIES - No. 30
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Abstract
Chapter 1 provides an overview and assessment of the price competitiveness and export performance of the euro area and the larger euro area countries, as well as an evaluation of how standard equations have been able to explain actual export developments. Chapter 2 carries out a constant market share analysis for the euro area and thereby sheds light on the reasons for movements in aggregate export market shares by looking at the sectoral and geographical composition of euro area exports. Chapter 3 looks at the evolution of the technological competitiveness of the euro area and major competitors
JEL Code
E3 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles
29 September 2006
WORKING PAPER SERIES - No. 677
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Abstract
We combine the dynamic dividend-discount model with an accounting-based vector autoregression framework that allows for a decomposition of EU banks' stock returns to cash-flow and expected return news components. The main findings are that while the bulk of the variability of EU banks' stock returns is due to cash flow shocks, the expected return shocks are relatively more important for larger than for smaller banks. Moroever, variables used in the literature as cash-flow proxies explain a higher share of the cash-flow component of the total excess returns for smaller than for larger EU banks. This suggests that large banks could be more prone to market wide news and events - that in the literature are associated with the expected return news component - as opposed to the bank-specific news, typically assumed to be incorporated in the cash-flow component.
JEL Code
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
28 December 2006
WORKING PAPER SERIES - No. 706
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Abstract
We apply the Campbell-Shiller return decomposition to exchange rate returns and fundamentals in a stationary panel vector autoregression framework. The return decomposition is then used to analyse how different investor segments react to news as captured by the different return components. The results suggest that intrinsic value news are dominating for equity investors and speculative money market investors while investors in currency option markets react strongly to expected return news. The equity and speculative money market investors seem able to distinguish between transitory and permanent FX movements while options investors mainly focus on transitory movements. We also find evidence that offsetting impact on the various return components can blur the effect of macroeconomic data releases on aggregate FX excess returns.
JEL Code
C23 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Panel Data Models, Spatio-temporal Models
F31 : International Economics→International Finance→Foreign Exchange
F32 : International Economics→International Finance→Current Account Adjustment, Short-Term Capital Movements
G15 : Financial Economics→General Financial Markets→International Financial Markets
11 February 2009
WORKING PAPER SERIES - No. 1002
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Abstract
In terms of regulatory and economic capital, credit risk is the most significant risk faced by banks. We implement a credit risk model - based on publicly available information - with the aim of developing a tool to monitor credit risk in a sample of large and complex banking groups (LCBGs) in the EU. The results indicate varying credit risk profiles across these LCBGs and over time. Furthermore, the results show that large negative shocks to real GDP have the largest impact on the credit risk profiles of banks in the sample. Notwithstanding some caveats, the results demonstrate the potential value of this approach for monitoring financial stability.
JEL Code
C02 : Mathematical and Quantitative Methods→General→Mathematical Methods
C19 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Other
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C61 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Optimization Techniques, Programming Models, Dynamic Analysis
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
3 March 2009
WORKING PAPER SERIES - No. 1019
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Abstract
We apply a dynamic dividend-discount model to analyse unexpected housing returns in a panel of eight euro area countries which together comprise 90% of euro area GDP. The application of this model allows for a de-composition of house price movements into movements in rent (cash-flow) and expected return news components. The empirical application of the model involves the estimation of a panel vector autoregressive model (VAR) for four variables –excess return to housing, rents, the real interest rate and real disposable per capita income– using quarterly data over the period 1985-2007. This empirical investigation yields two main findings. First, the bulk of the variability of house price move-ments in the panel of countries analysed can be attributed to movements in the rental yield. Indeed, perturbations to rents appear to result in a one-to-one analogous movement in house prices over the long term once controlling for changes in expected returns. Second, evidence from the dynamic profile of shocks along with the negative co-movement between changing rental yield expectations and changing expected returns on housing assets would suggest that euro area house prices overreact to news.
JEL Code
R21 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Household Analysis→Housing Demand
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
11 October 2013
OCCASIONAL PAPER SERIES - No. 152
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Abstract
The use of macro stress tests to assess bank solvency has developed rapidly over the past few years. This development was reinforced by the financial crisis, which resulted in substantial losses for banks and created general uncertainty about the banking sector's loss-bearing capacity. Macro stress testing has proved a useful instrument to help identify potential vulnerabilities within the banking sector and to gauge its resilience to adverse developments. To support its contribution to safeguarding financial stability and its financial sector-related work in the context of EU/IMF Financial Assistance Programmes, and looking ahead to the establishment of the Single Supervisory Mechanism (SSM), the ECB has developed a top-down macro stress testing framework that is used regularly for forward-looking bank solvency assessments. This paper comprehensively presents the main features of this framework and illustrates how it can be employed for various policy analysis purposes.
JEL Code
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
D85 : Microeconomics→Information, Knowledge, and Uncertainty→Network Formation and Analysis: Theory
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
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→Forecasting and Simulation: Models and Applications
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G01 : Financial Economics→General→Financial Crises
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
31 October 2019
WORKING PAPER SERIES - No. 2323
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Abstract
This paper presents a model for stress testing investment funds, based on a broad worldwide sample of primary open-end equity and bond funds. First, we employ a Bayesian technique to project the impact of macro-financial scenarios on country-level portfolio flows worldwide that are constructed from fund-level asset holdings. Second, from these projected country-level flows, we model the scenarios’ repercussions on individual funds along a three year horizon. Importantly, we further decompose portfolio flows, disentangling the specific contributions of transactions, valuation and foreign exchange effects. Overall, our results indicate that the impact of a global adverse macro-financial scenario leads to a median depletion in assets under management (AUM) of 24% and 5%, for euro area-domiciled equity and bond funds respectively, largely driven by valuation effects. Scenario and results both present similarities to the global financial crisis. We use historical information on fund liquidations to estimate a threshold for a drop in AUM that signals a high likelihood of a forthcoming liquidation. Based on this, we estimate that 5.8% and 0.5% of euro area-domiciled equity and bond funds respectively could go into liquidation. Such empirical thresholds can be useful for the implementation of prudential policy tools, such as redemption gates.
JEL Code
F21 : International Economics→International Factor Movements and International Business→International Investment, Long-Term Capital Movements
G15 : Financial Economics→General Financial Markets→International Financial Markets
G17 : Financial Economics→General Financial Markets→Financial Forecasting and Simulation
G23 : Financial Economics→Financial Institutions and Services→Non-bank Financial Institutions, Financial Instruments, Institutional Investors
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation