Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. iii Terms of use: Documents in EconStor may AbstractThis paper introduces a new methodology to date systemic financial stress events in a transparent, objective and reproducible way. The financial cycle is captured by a monthly country-specific financial stress index. Based on a Markov-switching model, high financial stress regimes are identified, and a simple algorithm is used to select those episodes of financial stress that are associated with a substantial negative impact on the real economy. By applying this framework to 27 European Union countries, the paper is a first attempt to provide a chronology of systemic financial stress episodes in addition to the expert-detected events that are currently available. Non-Technical SummaryIt is widely agreed that the global financial crisis that started in 2007 was an episode of severe financial market stress, which spilled over to the real economy causing the Great Recession. However, it is much more difficult to identify and classify other periods of, possibly systemic, financial market stress. The expert-based approach to identifying episodes of systemic financial stress prevails so far, but an objective and reproducible method for the detection of periods of low and high financial stress is lacking. A comprehensive analysis of the succession of tranquil and stress periods is a prerequisite to determining the leading indicators of systemic financial stress and evaluating the effectiveness of prudential policies implemented over the course of the financial cycle. This paper provides a new framework for a transparent and objective identification of systemic financial stress episodes, i.e., periods of high financial stress associated with a substantial and prolonged decline in real economic activity. By applying the framework to the countries of the European Union (EU), this is the first paper to build a consistent monthly chronology of EU systemic financial stress episodes beyond the expert-detected crises currently available. In fact, a continuous measure of financial stress is converted into a binary systemic stress dummy, commonly used in early-warning models.The model-based framework consists of three steps. First, we build on the existing financial stress literature and construct a simple country-specific financial stress index for 27 EU countries starting as early as 1964 for core EU countries. The essential feature of the financial stress index is that it captures co-movements in key financial market...

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for valuable comments as well as seminar participants at the Paris School of Economics, the Banque de France, the ADRES conference, the 7th Financial Risk Forum, the 3rd MoFiR workshop and the 2014 SoFiE conference on "systemic risks and financial regulation".

In this paper we develop an index to monitor the intensity of financial stress in the UK over a period of 45 years. By aggregating various market-based indicators of financial stress from six major markets, we allow each indicator to be assessed in terms of its systemic importance. This enables the index to capture the interconnectedness of financial markets. The index successfully captures three episodes of heightened stress in UK financial history. We also attempt to determine how much a financial shock to the UK economy is amplified in a period of stress vis-à-vis a tranquil period. It involves exploring the dynamic relationship of the index with the UK real economy by two specifications of threshold vector auto-regression models. We find empirical evidence for the existence of feedback loops in the shock propagation between the real and the financial sector in the United Kingdom.

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L'auteur crée un nouvel indicateur composite de tensions systémiques du marché financier pour le Can ada. Comparativement aux mesures existantes, cet indicateur rend mieux compte de la correction du marché immobilier en 1990 et reflète avec plus d'exactitude l'absence de possibilités de diversifi cation lors d'événements systémiques. L'indicateur proposé peut servir à des fins de contrôle. Durant la pandémie de maladie à coronavirus de 2019, par exemple, il a atteint un sommet que seule surpasse la crise financière mondiale de 2008. L'indicateur peut également servir à intégrer la dynamique macrofinancière non liné aire dans les modèles macroéconomiques empiriques de l'économie canadienne. Selon les constatations de l'auteur, les conditions macroéconomiques se détériorent sensiblement lorsque l'indicateur canadien de tensions financières excède le 90 e percentile. Mots clés : crise financière, indicateur de tensions fi nancières, marchés financiers, modèle vectoriel autoré gressif à seuil limite I construct a new composite measure of systemic financial market stress for Canada. Compared with ex isting measures, it better captures the 1990 housing market correction and more accurately refl ects the absence of diversification opportunities during systemic events. The index can be used for monitoring. For instance, during the coronavirus disease 2019 pandemic, it reached a peak second only to the 2008 global financial crisis. The index can also be used to introduce non-linear macro-financial dynamics in empirical macroeconomic models of the Canadian economy. Macroeconomic conditions are shown to deteriorate significantly when the Canadian financial stress index is above its 90th percentile.

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This paper predicts phases of the financial cycle by using a continuous financial stress measure in a Markov switching framework. The debt service ratio and property market variables signal a transition to a high financial stress regime, while economic sentiment indicators provide signals for a transition to a tranquil state. Whereas the in-sample analysis suggests that these indicators can provide an early warning signal up to several quarters prior to the respective regime change, the out-of-sample findings indicate that most of this performance is owing to the data gathered during the global financial crisis. Comparing the prediction performance with a standard binary early warning model reveals that the Markov switching model is outperforming the vast majority of model specifications for a horizon up to three quarters prior to the onset of financial stress.