A forward intensity model for the prediction of corporate defaults over different future periods
is proposed. Maximum pseudo-likelihood analysis is then conducted on a large sample of …

This study proposes a novel framework which combines marginal probabilities of default
estimated from a structural credit risk model with the consistent information multivariate …

We conduct a thorough analysis on the role played by the unobserved systematic risk factor
in default prediction. We find that this latent factor outweighs the observed systematic risk …

This article presents a general discrete-time affine framework aimed at jointly modeling yield
curves associated with different debtors. The underlying fixed-income securities may differ in …

Abstract This chapter introduces Markov Chain Monte Carlo (MCMC) methods for empirical
corporate finance. These methods are very useful for researchers interested in capital …

This study provides the first available estimates of systemic risk in the financial sector
comprising the banking and investment fund industries during 2009Q4-2015Q4. Systemic …

Corporate Default Prediction: Models, Drivers and Measurements Page 1 1 Corporate Default
Prediction: Models, Drivers and Measurements Submitted by Yangzhengxuan Wang to the …

Purpose: This study designs to examine the determinants (size, liquidity ratio, leverage ratio,
deposit ratio, asset growth, net interest income ratio and return on asset ratio) of bank's …

In this paper, we propose a regime-switching intensity model to predict the occurrences of
default events. Individual firm's default intensity function, ie, the instantaneous default …

The writing of this thesis coincided with the financial crisis of 2007–09. The causes of the
crisis were complex and varied, and have by now been analyzed and documented, see eg …