Dynamic models for credit rating transitions are important ingredients for dynamic credit risk analyses. We compare the properties of two such models that have recently been put forward. The models mainly differ in their treatment of systematic risk, which can be modeled either using discrete states (e.g., expansion versus recession) or continuous states. It turns out that the implied asset correlations and default rate volatilities for discrete state switching models are implausibly low compared to empirical estimates from the literature. We conclude that care has to be taken when discrete state regime switching models are employed for dynamic credit risk management. As a side result of our analysis, we obtain indirect evidence that asset correlations may change over the business cycle.

We investigate rating and default risk dynamics over the covid-19 crisis from a credit risk modeling perspective. We find that growth dynamics remain a stable and sufficient predictor of credit risk incidence over the pandemic period, despite its large, short-lived swings due to government intervention and lockdown. Unobserved component models as used in the recent credit risk literature appear mainly helpful for explaining the high-default wave in the early 2000s, but less so for default prediction above and beyond growth dynamics during the 2008 financial crisis or the early 2020 covid default peak. Government support variables do not reduce the impact of either growth proxies or unobserved components. Correlations between government support and credit risk are different, however, during the financial and the covid crisis. Using the empirical models in this paper as credit risk management tools, we show that growth factors also suffice to predict credit risk quantiles out-of-sample during covid times.

We investigate whether long-term and short-term components of typical conditioning variables in asset pricing studies, such as the dividend yield or yield spread, have different implications for optimal asset allocation. We argue that short-term components relate mostly to momentum, and long-term components relate mostly to mean-reversion effects, respectively. Therefore, they may have a different information content for investors with different horizons. We obtain improvements in terms of out-of-sample Sharpe ratios and expected utilities for decomposed state variables that directly reflect information related to the stock market, such as the dividend yield and stock market trend.

We test whether asymmetric preferences for losses versus gains affect the prices of cash flow versus discount rate risk. We construct a return decomposition distinguishing cash flow and discount rate betas in up and down markets. Using U.S. data, we find that downside cash flow and discount rate betas carry the largest premia. Downside cash flow risk is priced consistently across different samples, periods, and return decomposition methods. It is the only component of beta with significant out-of-sample predictive ability. Downside cash flow premia mainly occur for small stocks, while large stocks are compensated for symmetric cash-flow-related risk.

Multiple blockholder structures are a widespread phenomenon in the U.S. The theoretical literature, however, provides conflicting predictions on whether a single large blockholder or a set of dispersed smaller blockholders is better for firm value. Using U.S. data, we find a negative correlation between Tobin's Q and blockholder dispersion. The findings are robust to a wide variety of model specifications and controls and differ from results for other geographic regions such as Europe and Asia.

We model 1927–1997 US business failure rates using an unobserved components time series model. Clear evidence is found of cyclical behavior in default rates. We also detect significant longer term movements in default rates and default correlations. In a multi-year backtest experiment we show that accommodation of default rate dynamics has important consequences for credit risk capitalization requirements. Static or myopic variants of credit portfolio models miss significant periods of credit risk accumulation. Empirically congruent dynamic models by contrast provide more timely warning signals of credit risk build-up. In this way they may mitigate some of the pro-cyclicality concerns.

We verify whether parameter-driven and observation-driven classes of dynamic models can outperform each other in predicting time-varying parameters. We consider existing and new dynamic models for counts and durations, but also for volatility, intensity, and dependence parameters. In an extended Monte Carlo study, we present evidence that observation-driven models based on the score of the predictive likelihood function have similar predictive accuracy compared to their correctly specified parameter-driven counterparts. Dynamic observation-driven models based on predictive score updating outperform models based on conditional moments updating. Our main findings are supported by the results from an extensive empirical study in volatility forecasting.

We investigate the information content of stock correlation based network measures for systemic risk rankings, such as SIFIRank (based on Google’s PageRank). Using European banking data, we show that SIFIRank is empirically equivalent to a ranking based on average pairwise stock correlations as developed in this paper. The correlation based network measures complement currently available alternative systemic risk ranking methods based on book or market values. A further analytical investigation shows that the value-added appears to be mainly attributable to pairwise cross-sectional heterogeneity rather than to more subtle network relations and feedback loops.

We derive an analytic approximation to the credit loss distribution of large portfolios by letting the number of exposures tend to infinity. Defaults and rating migrations for individual exposures are driven by a factor model in order to capture co-movements in changing credit quality. The limiting credit loss distribution obeys the empirical stylized facts of skewness and heavy tails. We show how portfolio features like the degree of systematic risk, credit quality and term to maturity affect the distributional shape of portfolio credit losses. Using empirical data, it appears that the Basle 8% rule corresponds to quantiles with confidence levels exceeding 98%. The limit law's relevance for credit risk management is investigated further by checking its applicability to portfolios with a finite number of exposures. Relatively homogeneous portfolios of 300 exposures can be well approximated by the limit law. A minimum of 800 exposures is required if portfolios are relatively heterogeneous. Realistic loan portfolios often contain thousands of exposures implying that our analytic approach can be a fast and accurate alternative to the standard Monte-Carlo simulation techniques adopted in much of the literature.

We propose an observation-driven dynamic factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common dynamics and cross-sectional dependence due to shared dynamic latent factors. A feature of our model is that the likelihood function is known in closed form. This enables parameter estimation using standard maximum likelihood methods. We adopt the new framework for signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moody's-rated firms from January 1982 to March 2010.