Market Valuation and Risk Assessment of Canadian Banks

The authors apply the asset-valuation model developed by Rabinovitch (1989) to six publicly traded Canadian banks over the period 1982–2002. The model is an extension of the Merton (1977a) option-pricing model with the incorporation of stochastic interest rates. The authors introduce the Z-score, a measure of distance-to-default, which can be a useful tool for regulators in assessing the risk of bank failures. The Z-scores, overall, suggest that Canadian banks are far from the point of default. The authors also find that both the market valuation of the bank assets and the Z-score of the Canadian banks demonstrate similar regime shifts in the late 1990s, which may be related to regulatory changes during the 1990s.Financial institutions

Transport model study of nuclear stopping in heavy ion collisions over an energy range from 0.09A GeV to 160A GeV

Nuclear stopping in the heavy ion collisions over a beam energy range from SIS, AGS up to SPS is studied in the framework of the modified UrQMD transport model, in which mean field potentials of both formed and "pre-formed" hadrons (from string fragmentation) and medium modified nucleon-nucleon elastic cross sections are considered. It is found that the nuclear stopping is influenced by both the stiffness of the equation of state and the medium modifications of nucleon-nucleon cross sections at SIS energies. At the high SPS energies, the two-bump structure is shown in the experimental rapidity distribution of free protons, which can be understood with the consideration of the "pre-formed" hadron potentials.Comment: 15 pages, 7 figure

Variance-Reduced Stochastic Learning by Networked Agents under Random Reshuffling

A new amortized variance-reduced gradient (AVRG) algorithm was developed in \cite{ying2017convergence}, which has constant storage requirement in comparison to SAGA and balanced gradient computations in comparison to SVRG. One key advantage of the AVRG strategy is its amenability to decentralized implementations. In this work, we show how AVRG can be extended to the network case where multiple learning agents are assumed to be connected by a graph topology. In this scenario, each agent observes data that is spatially distributed and all agents are only allowed to communicate with direct neighbors. Moreover, the amount of data observed by the individual agents may differ drastically. For such situations, the balanced gradient computation property of AVRG becomes a real advantage in reducing idle time caused by unbalanced local data storage requirements, which is characteristic of other reduced-variance gradient algorithms. The resulting diffusion-AVRG algorithm is shown to have linear convergence to the exact solution, and is much more memory efficient than other alternative algorithms. In addition, we propose a mini-batch strategy to balance the communication and computation efficiency for diffusion-AVRG. When a proper batch size is employed, it is observed in simulations that diffusion-AVRG is more computationally efficient than exact diffusion or EXTRA while maintaining almost the same communication efficiency.Comment: 23 pages, 12 figures, submitted for publicatio