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What predicts Financial (In)Stability? A Bayesian Approach

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  • Dr. Michael Sigmund

    () (OOesterreichische Nationalbank, Financial Stability and Macroprudential Supervision Division, Otto Wagner Platz 3, 1090 Vienna Austria)

  • Dr. Ingrid Stein

    () (Deutsche Bundesbank, Department of Financial Stability, Wilhelm-Epstein-Strasse 14, 60431 Frankfurt Germany)

Abstract

This paper contributes to the literature on early warning indicators by applying a Bayesian model averaging approach. Our analysis, based on Austrian data, is carried out in two steps: First, we construct a quarterly financial stress index (AFSI) quantifying the level of stress in the Austrian financial system. Second, we examine the predictive power of various indicators, as measured by their ability to forecast the AFSI. Our approach allows us to investigate a large number of indicators. The results show that banks’ share price growth and cross-border lending are among the best early warning indicators.

Suggested Citation

  • Dr. Michael Sigmund & Dr. Ingrid Stein, 2017. "What predicts Financial (In)Stability? A Bayesian Approach," Credit and Capital Markets, Credit and Capital Markets, vol. 50(3), pages 299-336.
  • Handle: RePEc:kuk:journl:v:50:y:2017:i:3:p:299-336
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    Cited by:

    1. Virtanen, Timo & Tölö, Eero & Virén, Matti & Taipalus, Katja, 2016. "Use of unit root methods in early warning of financial crises," Research Discussion Papers 27/2016, Bank of Finland.
    2. repec:wea:econth:v:6:y:2017:i:2:p:14 is not listed on IDEAS
    3. Virtanen, Timo & Tölö, Eero & Virén, Matti & Taipalus, Katja, 2017. "Use of unit root methods in early warning of financial crises," ESRB Working Paper Series 45, European Systemic Risk Board.

    More about this item

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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