We present a highly parallelizable and flexible computational method to solve high-dimensional stochastic dynamic economic models. Solving such models often requires the use of iterative methods, like time iteration or dynamic programming. By exploiting the generic iterative structure of this broad class of economic problems, we propose a parallelization scheme that favors hybrid massively parallel computer architectures. Within a parallel nonlinear time iteration framework, we interpolate policy functions partially on GPUs using an adaptive sparse grid algorithm with piecewise linear hierarchical basis functions. GPUs accelerate this part of the computation one order of magnitude thus reducing overall computation time by 50%. The developments in this paper include the use of a fully adaptive sparse grid algorithm and the use of a mixed MPI-Intel TBB-CUDA/Thrust implementation to improve the interprocess communication strategy on massively parallel architectures. Numerical experiments on “Piz Daint” (Cray XC30) at the Swiss National Supercomputing Centre show that high-dimensional international real business cycle models can be efficiently solved in parallel. To our knowledge, this performance on a massively parallel petascale architecture for such nonlinear high-dimensional economic models has not been possible prior to present work.
This paper provides evidence on how the new international regulation on Global Systemically Important Banks (G-SIBs) impacts the market value of large banks. We analyze the stock price reactions for the 300 largest banks from 52 countries across 12 relevant regulatory announcement and designation events. We observe that the new regulation negatively affects the value of the newly regulated banks, yet that the official designation of banks as “globally systemically important” itself has a partly offsetting positive impact. A cross-sectional analysis of the valuation effects with respect to, for example, government ownership of banks supports the view that the positive reaction to these designations can be attributed to a Too-Big-to-Fail (TBTF) perception by investors. The fact that these valuation effects emerge from a regulation specifically designed to reduce the costs and risks of Too-Big-to-Fail demonstrates the inherently paradoxical nature of the new regulation. These results further suggest that even though the individual components of the regulation have been effective, revealing the identities of G-SIBs eliminated ambiguity about the presence of government guarantees, and thereby may have run counter to the regulators’ intent to contain the effects of TBTF.
This paper examines the impact of monetary conditions on the risk-taking behaviour of banks in the Czech Republic by analysing the comprehensive credit register of the Czech National Bank. Our duration analysis indicates that expansionary monetary conditions promote risk-taking among banks. At the same time, a lower interest rate during the life of a loan reduces its riskiness. While seeking to assess the association between banks’ appetite for risk and the short-term interest rate we answer a set of questions related to the difference between higher liquidity versus credit risk and the effect of the policy rate conditioned on bank and borrower characteristics
We review existing empirical research on the design and impact of regulation in the banking sector. The impact of each individual piece of regulation may inexorably depend on the set of regulations already in place, the characteristics of the banks involved (from their size or ownership structure to operational idiosyncrasies in terms of capitalization levels or risk taking behavior), and the institutional development of the country where the regulation is introduced. This complexity is challenging for the econometrician, who relies either on single country data to identify challenges for regulation or cross country data to assess the overall effects of regulation. It is also troubling for the policymaker, who has to optimally design regulation to avoid any unintended consequences, especially those that vary over the credit cycle such as the currently developing macroprudential frameworks.
Expected final online publication date for the Annual Review of Financial Economics Volume 7 is November 6, 2015. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
