Credit risk measurement and modelling
This thesis aims to make a contribution to the understanding of the key economic and company specific components of credit spreads in the investment and non-investment grade US bond market for different maturing bond indices. It calls for the full integration of different market andfirm specific variables into a unique framework, in order to predict credit spread changes. Key determinants of default risk are employed to determine credit migration risk. Particularly, this thesis provides evidence as to the relation between different macroeconomic factors and credit spread changes in all different maturities and rating categories, it supports the use of the consumer confidence index, as the most important variable explaining changes in credit spreads in investment and high yield companies, but most importantly it provides support for the strong informational content of high yield spreads as predictors of output growth, based on Option Adjusted Spreads. It favours the inclusion of implied volatilities in explaining credit spread changes, while it criticises the incorporation of historical ones. Throughout the thesis, it becomes evident that BBB-rated bonds exhibit highly volatile patterns and are very difficult to model. Financial ratios adjusted to reflect depreciation and amortisation expenses, which are usually very high for non-investment grade companies, prove to be very important in explaining changes of high yield spreads. However, firm specific risk, accounts only for a small fraction of the variation in the investment grade category. Ultimately, it is shown that by using solely market (equity and macro variables) and firm specific variables, i. e. some of the key determinants of default risk and the price of credit risky debt in most Merton-type models, we can accurately forecast credit spread changes at least one year ahead, particularly based on results provided from the investment grade sample. Moreover, credit spread forecasts, based on our set of OAS, tend to be overestimated rather than underestimated, as opposed to results provided by previous studies. This makes forecasts more conservative and therefore more appealing for risk management purposes. In particular, this thesis is focused on the main drivers of credit spread movements in the US corporate bond market. There are four issues mainly considered. The first part of the thesis examines a question that is a point of central focus in the fixed income literature, i. e. the relation between credit spread changes and the macroeconomic cycle. This chapter is inspired by the relatively little work that has been done on the empirical relationship between credit spread changes and the macroeconomy, since most of the literature on this issue focuses on macroeconomic variables and the modelling of default risk. We investigate how this relation evolves, not only with respect to short, medium and long term maturities but also for investment and non-investment rated companies, by testing the direction of causation among economic variables and credit spreads and by employing different sets of data and estimation techniques to explore the relation. We find that irrespective of the statistical method used or the time period tested that the most important variable in explaining the variation of credit spread changes is the US Consumer Confidence Index. We affirm the negative relation between the consumer confidence index, money supply and changes in credit spreads but not for the variables of GDP and industrial production. The negative relation between the term structure and credit spreads is also asserted for investment grade bonds of all maturities, consistent with the structural model's theory, while we find this relation to be positive for non-investment grade companies. Results from the OLS regressions suggest that macroeconomic variables alone, can explain at best a 17% of the variation in medium and long term maturing indices, and a 20.5% in short term indices. Findings from cross sectional regressions suggest that macroeconomic factors alone can explain 27.9% of the variation in credit spreads for investment grade bonds and a 44.4% for high yield ones. When testing the direction of causation, wefind thatfor long and medium term maturity investment grade indices we reject the null hypothesis that macroeconomic variables don't granger cause changes in credit spreads, but not for short term maturities and the high yield sector. Indeed, results provided on that respect from the high yield category, provide evidence that non-investment grade spreads may be a good proxy for predicting estimating overall financial conditions. Secondly, the relation between credit spreads and equities together with their implied and historical volatilities is examined. This chapter constitutes an effort to fill the gap in the existing literature, which has focused mainly on bond returns or yield changes, while very limited work has been done in modelling credit spread changes. 12 Empirical evidence points out to the fact that debt markets not only in the US but also in Europe and elsewhere seem to be greatly affected by the movements in the equity markets. If that is the case we should expect changes in equity prices to affect changes in credit spreads. This assumption is tested on a cross sectional and time series basis, for quarterly and monthly frequencies and by using company specific equity prices against the respective credit spreads, but also by including equity and volatility indices. We find that there is a negative relation between credit spread and equity changes, irrespective of maturity or rating category. Results provided by univariate regressions, based on changes in equity prices alone, explain haýr of the variation of B-rated corporate spreads. Results affirm the positive relation between implied volatilities and their high explanatory power on credit spread changes while findings derived from historical volatilities although statistically significant don't even marginally support the hypothesis of explaining the variation in credit spreads. In particular, results from pooled regressions suggest that when implied volatilities are substitutedfor the historical ones, adjusted R2 sfell to 6% and 28%for the investment and non-investment grade samples respectively (from 25% and 50.3% for investment and non-investment grade companies, when implied volatilities are considered). Resultsfrom OLS regressions, suggest that equity variables explain at best a 44% for short term maturing indices, and 35% and 37% for medium and long term maturing indices 2 as reflected by the adjusted R S. We also strongly reject the null hypothesis that implied volatilities don't granger cause changes in credit spreads but only with regards to short and medium term maturities. The next chapter of the thesis focuses on how changes in a company's financials, as those are presented by ratios, actually infiuence changes in credit spreads. The reason for including this chapter is due to the fact that although traditional ratio analysis has been widely investigated, it has mainly been tested within the context of default risk, while very limited literature exists on the use of traditional credit risk analysis in determining credit spread changes. Cross sectional analysis is employed in this chapter to test the hypothesis that credit spread changes are influenced by changes in accounting factors, both in investment and high yield categories. On a multivariate basis, we find that 63.5% of the variation in high yield credit spreads is explained by the changes in financial ratios, as reflected by the adjusted R2, compared to an adjusted R2 of 19.2% for investment grade companies. Consistently, 13 in the randomly selected group of companies, we find that traditional ratios can explain one third of the variation in credit spreads in the high yield sector, although less than 10% in the investment grade sample. A reason for the higher explanatory power in the high yield sector entails the use of ratios adjusted, to reflect depreciation and amortisation expenses, which hasn't been considered before. The most statistically and economically significant coefficient was obtained from the current market capitalisation, which was used as a proxy for the firm's size. The last part of the thesis, constitutes an effort to combine all the above factors (macroeconomic, equity and financials), in order to forecast credit spread changes one and two years ahead. We show that on a multiple regression context, results provided are consistent with previous chapters and indeed highly significant in explaining credit spread variation, irrespective of the time period tested. For the total sample we get an adjusted R2 of 95% or 52% as part of the weighted and unweighted statistics respectively. A robust model is identified for forecasting credit spread changes one year ahead, with the employment of the dynamic solution method. The accuracy of the model doesn't fall below 85% within the first year, while we choose as the most vigorous method for estimating coefficients the GLS method adjustedfor heteroscedasticity, since it consistently provides more conservative forecasts.