Research

Recent Submissions

  • Juselius, Mikael; Tarashev, Nikola (2020)
    Bank of Finland Research Discussion Papers 18/2020
    Extending a standard credit-risk model illustrates that a single factor can drive both expected losses and the extent to which they may be exceeded in extreme scenarios, ie “unexpected losses.” This leads us to develop a framework for forecasting these losses jointly. In an application to quarterly US data on loan charge-offs from 1985 to 2019, we find that financial-cycle indicators – notably, the debt service ratio and credit-to-GDP gap – deliver reliable real-time forecasts, signalling turning points up to three years in advance. Provisions and capital that reflect such forecasts would help reduce the procyclicality of banks’ loss-absorbing resources.
  • He, Qing; Li, Xiaoyang (2020)
    BOFIT Discussion Papers 27/2020
    We investigate the influence of financial and political factors on peer-to-peer (P2P) platform failures in China’s online lending market. Using a competing risk model for platform survival, we show that large platforms, platforms with listed firms as large shareholders, and platforms with better information disclosure were less likely to go bankrupt or run off (platform owners abscond with investor funds). More importantly, failing platforms were much less likely to run off in advance of major political events, but more likely to declare bankruptcy or run off after such events. These effects are more pronounced for politically connected platforms, platforms operating in provinces where local officials have close ties with central government, and in provinces with better local financial conditions. Our study highlights the role of political incentives on government regulatory intervention in platform failures.
  • Gregg, Amanda; Nafziger, Steven (2020)
    BOFIT Discussion Papers 26/2020
    Enterprise creation, destruction, and evolution support the transition to modern economic growth, yet these processes are poorly understood in industrializing contexts. We investigate Imperial Russia’s industrial development at the firm-level by examining entry, exit, and persistence of corporations. Relying on newly developed balance sheet panel data from every active Russian corporation (N > 2500) between 1899 and 1914, we examine the characteristics of entering and exiting corporations, how new entrants evolved, and the impact of founder identity on subsequent outcomes. Russian corporations operated flexibly and competitively, conditional on overcoming distortionary institutional barriers to entry that slowed the emergence of these leading firms in the Imperial economy.
  • Kauko, Karlo; Tölö, Eero (2020)
    Finnish Economic Papers 2
    Published also as BoF DP 6/2019 http://urn.fi/URN:NBN:fi:bof-201902251071
    The credit-to-GDP gap is a widely used early warning indicator of banking crises. It has become standard to calculate this trend deviation with a one-sided Hodrick-Prescott filter that uses a much larger value for the smoothing parameter λ than commonly applied in most business-cycle studies. We recalibrate the smoothing parameter with panel data covering almost one-and-a-half centuries of data. As a result, the 2008 crisis does not dominate the results and sample length helps contain filter initialization problems, i.e. most observations are preceded by decades of data. The optimal λ is found to be much lower than previously suggested.
  • Honkapohja, Seppo; Mitra, Kaushik (2020)
    Journal of Monetary Economics December
    Published also as BoF DP 5/2018 http://urn.fi/URN:NBN:fi:bof-201802231239
    Global learning dynamics for price-level targeting (PLT) monetary policy are analyzed and compared to inflation targeting in a nonlinear New Keynesian model. Domain of attraction of target steady state is a new robustness criterion for policy regimes. Robustness of PLT depends on whether a known target path is incorporated into learning. Credibility is measured by accuracy of this forecasting method relative to simple statistical forecasts evolving through reinforcement learning. Initial credibility and target price are key factors influencing performance. Model results are in line with the Swedish experience of price stabilization in1930’s.