Survey_BLPRNV_2016

Abstract

The two main inputs are a stochastic model of default and a distribution of loss-givendefault rates for each euro area sovereign. Model calibration is guided by the principle of conservatism regarding the potential benefits of ESBies. We use a simple and transparent two-level hierarchical model. The first hierarchical level concerns the aggregate state of the euro area economy. We simulate 2,000 five-year periods, in each of which the aggregate state can take one of three values: State 1: A severe recession occurs, default and loss-given-default rates are very high for all nation-states, and particularly for those with worse credit ratings. In this scenario, the expected default rate over five years is listed in column 4 of Table 1, expected loss-given-default rates, are shown in column 7. State 2: A mild recession occurs, default and loss-given-default rates are elevated in all nation-states. Expected five-year default rates are given in column 5 of Table 1, expected loss-given-default rates are 80% of those in state 1. State 3 : The economy expands, default risk is low for most nation-states (column 6 of Table 1), loss-given-default rates are 50% of those in state 1. The random variable determines that the euro area economy is in the good state 70% of the time and in one of the two recessionary states 30% of the time. This 70:30 split between expansions and recessions accords with NBER data on the US business cycle spanning 1854-2. Of the 30% recessionary states, similarly long time-series data gathered by Reinhart & Rogoff (2009) and Schularick & Taylor (2012) suggest that about one-sixth are severe. We match these historical patterns by assuming that mild recessions occur 25% of the time and severe recessions occur 5% of the time. Robustness checks are provided in a web appendix, available at www.euro-nomics.com. The second hierarchical level concerns the possible default of each euro area sovereign. Within each five-year period, conditional on the aggregate state in that period (drawn in the first hierarchical level of the model), we take 5,000 draws of the sovereigns’ stochastic default process. The random variable that determines whether a sovereign defaults, and which can be interpreted as the “sunspot” in the theoretical model in Section 5, is assumedto have a fat-tailed distribution (Student-t with 4 degrees of freedom), making defaults far more likely than under a normal distribution. In each state of the economy, nation-states’ default probabilities increase with their numerical credit score (higher scores indicate worse ratings). Any two nation-states with the same credit rating are assumed to have the same (or similar) independent probabilities of default in each aggregate state of the world. With 2,000 five-year periods and 5,000 draws within each period, our calibration uses a total of 10 million draws.