I am organizing a workshop on forecasting to be held in Central University of Finance and Economics on Saturday, November 18, 2017. We have invited Professor Rob J Hyndman as keynote speaker. He will give four talks as part of the workshop. Other speakers are Junni Zhang for Peking University, Lei Song for JD.com, Hui Bu and Yanfei from Beihang University and I.
Full program details are available online.
Slides for the keynote speaker are available here.
An R package dng is now on CRAN.
At the moment, this package includes the distribution function and random generating process for the “split-t” density and gradient base on my previous paper “Flexible Modeling of Conditional Distributions using Smooth Mixtures of Asymmetric Student T Densities”, Journal of Statistical Planning and Inference. We plan to add more densities.
Thank you Jiayue for the hard work.
with Zhuojing He from Business School, Central University of Finance and Economics.
Understanding how corporate defaults cluster is particularly important for risk management of portfolios of corporate debt. In this paper, we discuss the dynamic nature of the clustering of credit risk across firms pairwise in the same family corporation in China. We insert the tail-dependence coefficient into the Joe-Clayton copula model directly through a reparameterized methodology to estimate the tail-dependence structure of credit risk. We also use both macroeconomic and firm-specific covariates to study the dynamic nature of the lower tail-dependence coefficient of distance-to-default which measures the credit risk clustering, and to find the driving forces behind credit risk clustering. Empirical results indicate that both macroeconomic and firm-specific covariates play important roles in the time-varying features of credit risk clustering. However, for different pairwise portfolios, these macroeconomic and firm-specific covariates have different effects.
Keywords: Credit risk clustering; Covariate-dependent copulas; tail-dependence; distance-to-default; MCMC.
In our study we apply our method to total of 45 pairwise firms. There are 39 pairs showing significant results, three of which are already explained in the paper. The supplementary material shows the empirical results of credit risk clustering across 36 significant pairwise firms not listed in the paper.