Through-the-Cycle Ratings Versus Point-in-Time Ratings and Implications of the Mapping Between Both Rating Types

The two philosophies of ratings, one that includes cyclical effects and the other that doesn't, are mirrored by the two different rating types commonly known as point-in-time (pit) and through-the-cycle (ttc). Point-in-time ratings try to evaluate the current situation of a customer by taking into account both cyclical and permanent effects. In contrast, through the-cycle ratings focus mainly on the permanent component of default risk and are nearly independent from cyclical changes in the creditworthiness of a customer. In this paper we give a review of the characteristics of both rating types and examine whether these properties can actually be observed in practice. In this context we present the results of an analysis of Standard& Poor's rating data, which show that the ratings, though being through-the-cycle, still vary in accordance with the business cycle. Another concern of this paper is the wide spread practice to map 'external' through-the-cycle ratings to 'internal' point-in-time ratings, with the purpose to enrich or validate a financial institution's internal rating database. We show that in doing so financial institutions severely misspecify customers' risk profiles and under- or overestimate costs in connection with credit pricing or capitalization. We confirm our theoretical considerations by calculating pricing quantities when using one or the other rating information.     

Addressing Uncertainty in Efficient Mitigation of Agricultural Greenhouse Gas Emissions

The agricultural sector, as an important source of greenhouse gas (GHG) emissions, is under pressure to reduce its contribution to climate change. Decisions on financing and regulating agricultural GHG mitigation are often informed by cost‐effectiveness analysis of the potential GHG reduction in the sector. A commonly used tool for such analysis is the bottom‐up marginal abatement cost curve (MACC) which assesses mitigation options and calculates their cumulative cost‐effective mitigation potential. MACCs are largely deterministic, typically not reflecting uncertainties in underlying input variables. We analyse the uncertainty of GHG mitigation estimates in a bottom‐up MACC for agriculture, for those uncertainties capable of quantitative assessment. Our analysis identifies the sources and types of uncertainties in the cost‐effectiveness analysis and estimates the statistical uncertainty of the results by propagating uncertainty through the MACC via Monte Carlo analysis. For the case of Scottish agriculture, the uncertainty of the cost‐effective abatement potential from agricultural land, as expressed by the coefficient of variation, was between 9.6% and 107.3% across scenarios. This means that the probability of the actual abatement being less than half of the estimated abatement ranged from <1% (in the scenario with lowest uncertainty) to 32% (in the scenario with highest uncertainty). The main contributors to uncertainty are the adoption rate and abatement rate. While most mitigation options appear to be ‘win–win’ under some scenarios, many have a high probability of switching between being cost‐ineffective and cost‐effective.     

Marginal Abatement Cost Curves for UK Agricultural Greenhouse Gas Emissions

This article addresses the challenge of developing a ‘bottom‐up’ marginal abatement cost curve (MACC) for greenhouse gas (GHG) emissions from UK agriculture. An MACC illustrates the costs of specific crop, soil and livestock abatement measures against a ‘business as usual’ scenario. The results indicate that in 2022 under a specific policy scenario, around 5.38 Mt CO₂ equivalent (e) could be abated at negative or zero cost. A further 17% of agricultural GHG emissions (7.85 Mt CO₂e) could be abated at a lower unit cost than the UK Government’s 2022 shadow price of carbon [£34 (tCO₂e)^?1]. The article discusses a range of methodological hurdles that complicate cost‐effectiveness appraisal of abatement in agriculture relative to other sectors.