Empirical credit cycles and capital buffer formation

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.     

Domestic value chains as stepping stones to global value chain integration

Identifying the determinants of global value chain (GVC) integration is essential to understand the past expansion and current slowdown in GVCs. In this paper, we study the role of domestic value chains (DVCs) for GVC integration. In the presence of industry‐specific fixed costs of fragmenting production and of switching across input suppliers, DVCs can either be stepping stones or stumbling blocks for subsequent GVC entry. Focusing on backward linkages, that is, the sourcing of intermediates, we provide robust empirical evidence in favour of the stepping‐stone hypothesis. In our benchmark specification, a one standard deviation increase in DVC integration raises subsequent GVC integration by about 0.4%. To identify the mechanisms at work, we exploit two dimensions of industry‐level heterogeneity: product differentiation (a proxy of fragmentation costs) and relationship specificity (a proxy of the costs of switching between suppliers). We find that DVC integration is less conducive to GVC integration in industries that are characterised by relatively high switching costs and relatively low fragmentation costs.     

Risk attitude and wage growth: replicating Shaw (1996)

We replicate Shaw (J Labor Econ 14(4):626–653, 1996) who found that individual wage growth is higher for individuals with greater preference for risk taking. Expanding her dataset with more American observations and data for Germany, Spain, and Italy, we find evidence that risk attitudes are relevant but support is mixed at best for the original specifications.     

Changing returns to education in Portugal during the 1980s and early 1990s: OLS and quantile regression estimators

This paper examines the evolution of the returns to education in Portugal over the 1980s and early 1990s. The main findings indicate that the returns to education have increased, particularly after joining the European Union in 1986. Since this occurred along with an increase in the level of education within the labour force, the process is most likely demand driven. The results also indicate that modelling on average (i.e. OLS) misses important features of the wage structure. Quantile regression (QR) analysis reveals that the effect of education is not constant across the conditional wage distribution. They are higher for those at higher quantiles in the conditional wage distribution. Wage inequality expanded in Portugal over the 1980s and the returns to education had an important role in this process.     

GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS

We propose a class of observation‐driven time series models referred to as generalized autoregressive score (GAS) models. The mechanism to update the parameters over time is the scaled score of the likelihood function. This new approach provides a unified and consistent framework for introducing time‐varying parameters in a wide class of nonlinear models. The GAS model encompasses other well‐known models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity, and Poisson count models with time‐varying mean. In addition, our approach can lead to new formulations of observation‐driven models. We illustrate our framework by introducing new model specifications for time‐varying copula functions and for multivariate point processes with time‐varying parameters. We study the models in detail and provide simulation and empirical evidence. Copyright © 2012 John Wiley & Sons, Ltd.     

Global Credit Risk: World,Country and Industry Factors

We investigate the dynamic properties of systematic default risk conditions for firms in different countries, industries and rating groups. We use a high‐dimensional nonlinear non‐Gaussian state‐space model to estimate common components in corporate defaults in a 41 country samples between 1980:Q1 and s2014:Q4, covering both the global financial crisis and euro area sovereign debt crisis. We find that macro and default‐specific world factors are a primary source of default clustering across countries. Defaults cluster more than what shared exposures to macro factors imply, indicating that other factors also play a significant role. For all firms, deviations of systematic default risk from macro fundamentals are correlated with net tightening bank lending standards, suggesting that bank credit supply and systematic default risk are inversely related. Copyright © 2016 John Wiley & Sons, Ltd.     

SMOOTH DYNAMIC FACTOR ANALYSIS WITH APPLICATION TO THE US TERM STRUCTURE OF INTEREST RATES

We consider the dynamic factor model and show how smoothness restrictions can be imposed on factor loadings by using cubic spline functions. We develop statistical procedures based on Wald, Lagrange multiplier and likelihood ratio tests for this purpose. The methodology is illustrated by analyzing a newly updated monthly time series panel of US term structure of interest rates. Dynamic factor models with and without smooth loadings are compared with dynamic models based on Nelson–Siegel and cubic spline yield curves. We conclude that smoothness restrictions on factor loadings are supported by the interest rate data and can lead to more accurate forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

Starting Wages Respond to Employer's Risk

Firms hiring new graduates face uncertainty on the future productivity of workers. Theory suggests that starting wages reflect this, with lower pay for greater uncertainty. We use the dispersion of exam grades within a field of education as an indicator of the unobserved heterogeneity that employers face. We find solid evidence that starting wages are lower if the variance of exam grades is higher and higher if the skew is higher: employers shift the cost of productivity risk to new hires, but pay for the opportunity to catch a really good worker. Estimating the extent of risk cost sharing between firm and worker shows that shifting to workers is larger in the market sector than in the public sector and diminishes with experience.     

Nowcasting and forecasting global financial sector stress and credit market dislocation

We introduce a new international model for the systematic distress risk of financial institutions from the US, the European Union, and the Asia-Pacific region. Our proposed dynamic factor model can be represented as a nonlinear, non-Gaussian state space model with parameters that we estimate using Monte Carlo maximum likelihood methods. We construct measures of global financial sector risk and of credit market dislocation, where credit market dislocation is defined as a significant and persistent decoupling of the credit risk cycle from macro-financial fundamentals in one or more regions. We show that, in the past, such decoupling has preceded episodes of systemic financial distress. Our new measure provides a risk-based indicator of credit conditions, and as such, complements earlier quantity-based indicators from the literature. In an extensive comparison with such quantity-based systemic risk indicators, we find that the behaviour of the new indicator is competitive with that of the best quantity-based indicators.