nep-ifn New Economics Papers
on International Finance
Issue of 2016‒02‒17
two papers chosen by
Vimal Balasubramaniam
University of Oxford

  1. Uncertainty and International Banking By Tonzer, Lena; Buch, Claudia M.; Buchholz, Manuel
  2. Quantitative Easing and Tapering Uncertainty: Evidence from Twitter By Tillmann, Peter; Meinusch, Annette

  1. By: Tonzer, Lena; Buch, Claudia M.; Buchholz, Manuel
    Abstract: We develop a new measure of uncertainty derived from bank-level data. We apply the measure of firm-level uncertainty developed by Bloom and others (2012) to banking. Uncertainty is measured as the cross-sectional dispersion of shocks to banking-sector specific variables. We then analyze how uncertainty in banking affects lending by domestic and foreign-owned banks. We find that, first, higher uncertainty in banking has negative effects on bank lending. Second, the effect is heterogeneous across banks: Lending by banks which are better capitalized and have higher liquidity buffers tends to be affected less. Third, foreign-owned banks do not react differently to uncertainty in the host country compared to domestically-owned banks.
    JEL: G01 F34 G21
    Date: 2015
    URL: http://d.repec.org/n?u=RePEc:zbw:vfsc15:113072&r=ifn
  2. By: Tillmann, Peter; Meinusch, Annette
    Abstract: In this paper we analyze the effects of changes in peoples' beliefs about the timing of the exit from Quantitative Easing ("tapering") on asset prices. To quantify beliefs of market participants, we use data from Twitter, the social media application, covering the entire Twitter volume on Federal Reserve tapering in 2013. Based on the time series of beliefs about an early or late tapering, we estimate a VAR model with appropriate sign restrictions on the impulse responses to identify a belief shock. The results show that shocks to tapering beliefs have strong and robust effects on interest rates, exchange rates and asset prices. We also derive measures of monetary policy uncertainty and disagreement of beliefs, respectively, and estimate their impact. The paper is the first to use social media data for
    JEL: E52 E43 F31
    Date: 2015
    URL: http://d.repec.org/n?u=RePEc:zbw:vfsc15:112906&r=ifn

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