Random thinning model with a truncated credit quality vulnerability factor: Application to top-down-type credit risk assessment

In the top-down approach of intensity-based credit risk modeling, a procedure called “random thinning” is needed to obtain credit event intensities for sub-portfolios. This paper presents a random thinning model incorporating a risk factor called the credit quality vulnerability factor (CQVF) to capture time-series variation in credit event occurrence in a target sub-portfolio. In particular, we propose a type of CQVF that follows truncated normal distributions specified by macroeconomic variables. Using credit event samples of Japanese firms, our empirical analysis aims to clarify the applicability and effectiveness of the proposed model to practical credit risk management. Since macroeconomic variables are included in our model, it is applicable to the macro-stress testing of portfolio credit risk management within a top-down-type framework.

Assimilation of Mode-S EHS Aircraft Observations in COSMO-KENDA

Aircraft observations of wind and temperature collected by airport surveillance radars [Mode-S Enhanced Surveillance (Mode-S EHS)] were assimilated in the Consortium for Small-Scale Modeling Kilometre-scale Ensemble Data Assimilation (COSMO-KENDA), which couples an ensemble Kalman filter to a 40-member ensemble of the convection permitting COSMO-DE model. The number of observing aircrafts in Mode-S EHS was about 15 times larger than in the AMDAR system. In the comparison of both aircraft observation systems, a similar observation error standard deviation was diagnosed for wind. For temperature, a larger error was diagnosed for Mode-S EHS. With the high density of Mode-S EHS observations, a reduction of temperature and wind error in forecasts of 1 and 3 hours was found mainly in the flight level and less near the surface. The amount of Mode-S EHS data was reduced by random thinning to test the effect of a varying observation density. With the current data assimilation setup, a saturation of the forecast error reduction was apparent when more than 50% of the Mode-S EHS data were assimilated. Forecast kinetic energy spectra indicated that the reduction in error is related to analysis updates on all scales resolved by COSMO-DE.

The mean waiting time of a GI/G/1 queue in light traffic via random thinning

In this paper, we derive the MacLaurin series of the mean waiting time in light traffic for a GI/G/1 queue. The light traffic is defined by random thinning of the arrival process. The MacLaurin series is derived with respect to the admission probability, and we prove that it has a positive radius of convergence. In the numerical examples, we use the MacLaurin series to approximate the mean waiting time beyond light traffic by means of Padé approximation.

The mean waiting time of a GI/G/1 queue in light traffic via random thinning

In this paper, we derive the MacLaurin series of the mean waiting time in light traffic for a GI/G/1 queue. The light traffic is defined by random thinning of the arrival process. The MacLaurin series is derived with respect to the admission probability, and we prove that it has a positive radius of convergence. In the numerical examples, we use the MacLaurin series to approximate the mean waiting time beyond light traffic by means of Padé approximation.