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Title: 3 essays on credit risk modeling and the macroeconomic environment
Author: Papanastasiou, Dimitrios
ISNI:       0000 0004 6351 4498
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2015
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In the aftermath of the recent financial crisis, the way credit risk is affected by and affects the macroeconomic environment has been the focus of academics, risk practitioners and central bankers alike. In this thesis I approach three distinct questions that aim to provide valuable insight into how corporate defaults, recoveries and credit ratings interact with the conditions in the wider economy. The first question focuses on how well the macroeconomic environment forecasts corporate bond defaults. I approach the question from a macroeconomic perspective and I make full use of the multitude of lengthy macroeconomic time series available. Following the recent literature on data-rich environment modelling, I summarise a large panel of 103 macroeconomic time series into a small set of 6 dynamic factors; the factors capture business cycle, yield curve, credit premia and equity market conditions. Prior studies on dynamic factors use identification schemes based on principal components or recursive short-run restrictions. The main contribution to the body of existing literature is that I provide a novel and more robust identification scheme for the 6 macro-financial stochastic factors, based on a set of over-identifying restrictions. This allows for a more straightforward interpretation of the extracted factors and a more meaningful decomposition of the corporate default dynamics. Furthermore, I use a novel Bayesian estimation scheme based on a Markov chain Monte Carlo algorithm that has not been used before in a credit risk context. I argue that the proposed algorithm provides an effcient and flexible alternative to the simulation based estimation approaches used in the existing literature. The sampling scheme is used to estimate a state-of-the-art dynamic econometric specification that is able to separate macro-economic fluctuations from unobserved default clustering. Finally, I provide evidence that the macroeconomic factors can lead to significant improvements in default probability forecasting performance. The forecasting performance gains become less pronounced the longer the default forecasting horizon. The second question explores the sensitivity of corporate bond defaults and recoveries on monetary policy and macro-financial shocks. To address the question, I follow a more structural approach to extract theory-based economic shocks and quantify the magnitude of the impact on the two main credit risk drivers. This is the first study that approaches the decomposition of the movements in credit risk metrics from a structural perspective. I introduce a VAR model with a novel semi-structural identification scheme to isolate the various shocks at the macro level. The dynamic econometric specification for defaults and recoveries is similar to the one used to address the first question. The specification is flexible enough to allow for the separation of the macroeconomic movements from the credit risk specific unobserved correlation and, therefore, isolate the different shock transmission mechanisms. I report that the corporate default likelihood is strongly affected by balance sheet and real economy shocks for the cyclical industry sectors, while the effects of monetary policy shocks typically take up to one year to materialise. In contrast, recovery rates tend to be more sensitive to asset price shocks, while real economy shocks mainly affect secured debt recovery values. The third question shifts the focus to credit ratings and addresses the Through-the- Cycle dynamics of the serial dependence in rating migrations. The existing literature treats the so-called rating momentum as constant through time. I show that the rating momentum is far from constant, it changes with the business cycle and its magnitude exhibits a non-linear dependence on time spent in a given rating grade. Furthermore, I provide robust evidence that the time-varying rating momentum substantially increases actual and Marked-to-Market losses in periods of stress. The impact on regulatory capital for financial institutions is less clear; nevertheless, capital requirements for high credit quality portfolios can be significantly underestimated during economic downturns.
Supervisor: Crook, Jonathan ; Ansell, Jake Sponsor: Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: credit risk ; corporate defaults ; credit ratings ; macroeconomic environment forecasts ; Markov chain Monte Carlo algorithm ; sampling ; corporate bond defaults