Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Markov chain Monte Carlo estimation of default and recovery: dependent
  via the latent systematic factor   It is a well known fact that recovery rates tend to go down when the number
of defaults goes up in economic downturns. We demonstrate how the loss given
default model with the default and recovery dependent via the latent systematic
risk factor can be estimated using Bayesian inference methodology and Markov
chain Monte Carlo method. This approach is very convenient for joint estimation
of all model parameters and latent systematic factors. Moreover, all relevant
uncertainties are easily quantified. Typically available data are annual
averages of defaults and recoveries and thus the datasets are small and
parameter uncertainty is significant. In this case Bayesian approach is
superior to the maximum likelihood method that relies on a large sample limit
Gaussian approximation for the parameter uncertainty. As an example, we
consider a homogeneous portfolio with one latent factor. However, the approach
can be easily extended to deal with non-homogenous portfolios and several
latent factors.

14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
2
The categories of this paper are: math