An Empirical Comparison of Default Swap Pricing Models

In this paper we compare market prices of credit default swaps with model prices. We show that a simple reduced form model with a constant recovery rate outperforms the market practice of directly comparing bonds' credit spreads to default swap premiums. We find that the model works well for investment grade credit default swaps, but only if we use swap or repo rates as proxy for default-free interest rates. This indicates that the government curve is no longer seen as the reference default-free curve. We also show that the model is insensitive to the value of the assumed recovery ratecredit default swaps, credit derivatives, credit risk, default risk, risk-neutral valuation, pricing

Pricing default swaps: empirical evidence

In this paper we compare market prices of credit default swaps with model prices. We show that a simple reduced form model with a constant recovery rate outperforms the market practice of directly comparing bonds' credit spreads to default swap premiums. We find that the model works well for investment grade credit default swaps, but only if we use swap or repo rates as proxy for default-free interest rates. This indicates that the government curve is no longer seen as the reference default-free curve. We also show that the model is insensitive to the value of the assumed recovery rat

An Empirical Comparison of Default Swap Pricing Models

Abstract: In this paper we compare market prices of credit default swaps with model prices. We show that a simple reduced form model with a constant recovery rate outperforms the market practice of directly comparing bonds' credit spreads to default swap premiums. We find that the model works well for investment grade credit default swaps, but only if we use swap or repo rates as proxy for default-free interest rates. This indicates that the government curve is no longer seen as the reference default-free curve. We also show that the model is insensitive to the value of the assumed recovery rate. Keywords: credit default swaps, credit derivatives, credit risk, default risk, default-free interest rate

Comparing possible proxies of corporate bond liquidity

We consider eight different proxies (issued amount, coupon, listed, age, missing prices, yield volatility, number of contributors and yield dispersion) to measure corporate bond liquidity and use a five-variable model to control for interest rate risk, credit risk, maturity, rating and currency differences between bonds. The null hypothesis that liquidity risk is not priced in our data set of euro corporate bonds is rejected for seven out of eight liquidity proxies. We find significant liquidity premia, ranging from 9 to 24 basis points. A comparison test between liquidity proxies shows limited differences between the proxies

What Can 14CO Measurements Tell Us about OH?

The possible use of 14CO measurements to constrain hydroxyl radical (OH) concentrations in the atmosphere is investigated. 14CO is mainly produced in the upper atmosphere from cosmic radiation. Measurements of 14CO at the surface show lower concentrations compared to the upper atmospheric source region, which is the result of oxidation by OH. In this paper, the sensitivity of 14CO mixing ratio surface measurements to the 3-D OH distribution is assessed with the TM5 model. Simulated 14CO mixing ratios agree within a few molecules 14COcm-3 (STP) with existing measurements at five locations worldwide. The simulated cosmogenic 14CO distribution appears mainly sensitive to the assumed upper atmospheric 14C source function, and to a lesser extend to model resolution. As a next step, the sensitivity of 14CO measurements to OH is calculated with the adjoint TM5 model. The results indicate that 14CO measurements taken in the tropics are sensitive to OH in a spatially confined region that varies strongly over time due to meteorological variability. Given measurements with an accuracy of 0.5 molecules 14COcm-3 STP, a good characterization of the cosmogenic 14CO fraction, and assuming perfect transport modeling, a single 14CO measurement may constrain OH to 0.2¿0.3×106 moleculesOHcm-3 on time scales of 6 months and spatial scales of 70×70 degrees (latitude×longitude) between the surface and 500 hPa. The sensitivity of 14CO measurements to high latitude OH is about a factor of five higher. This is in contrast with methyl chloroform (MCF) measurements, which show the highest sensitivity to tropical OH, mainly due to the temperature dependent rate constant of the MCF¿OH reaction. A logical next step will be the analysis of existing 14CO measurements in an inverse modeling framework. This paper presents the required mathematical framework for such an analysis.JRC.H.2-Climate chang

Model simulations of atmospheric methane (1997-2016) and their evaluation using NOAA and AGAGE surface and IAGOS-CARIBIC aircraft observations

Methane (CH4) is an important greenhouse gas, and its atmospheric budget is determined by interacting sources and sinks in a dynamic global environment. Methane observations indicate that after almost a decade of stagnation, from 2006, a sudden and continuing global mixing ratio increase took place. We applied a general circulation model to simulate the global atmospheric budget, variability, and trends of methane for the period 1997–2016. Using interannually constant CH4 a priori emissions from 11 biogenic and fossil source categories, the model results are compared with observations from 17 Advanced Global Atmospheric Gases Experiment (AGAGE) and National Oceanic and Atmospheric Administration (NOAA) surface stations and intercontinental Civil Aircraft for the Regular observation of the atmosphere Based on an Instrumented Container (CARIBIC) flights, with > 4800 CH4 samples, gathered on > 320 flights in the upper troposphere and lowermost stratosphere. Based on a simple optimization procedure, methane emission categories have been scaled to reduce discrepancies with the observational data for the period 1997–2006. With this approach, the all-station mean dry air mole fraction of 1780 nmol mol−1 could be improved from an a priori root mean square deviation (RMSD) of 1.31 % to just 0.61 %, associated with a coefficient of determination (R2) of 0.79. The simulated a priori interhemispheric difference of 143.12 nmol mol−1 was improved to 131.28 nmol mol−1, which matched the observations quite well (130.82 nmol mol−1). Analogously, aircraft measurements were reproduced well, with a global RMSD of 1.1 % for the measurements before 2007, with even better results on a regional level (e.g., over India, with an RMSD of 0.98 % and R2=0.65). With regard to emission optimization, this implied a 30.2 Tg CH4 yr−1 reduction in predominantly fossil-fuel-related emissions and a 28.7 Tg CH4 yr−1 increase of biogenic sources. With the same methodology, the CH4 growth that started in 2007 and continued almost linearly through 2013 was investigated, exploring the contributions by four potential causes, namely biogenic emissions from tropical wetlands, from agriculture including ruminant animals, and from rice cultivation, and anthropogenic emissions (fossil fuel sources, e.g., shale gas fracking) in North America. The optimization procedure adopted in this work showed that an increase in emissions from shale gas (7.67 Tg yr−1), rice cultivation (7.15 Tg yr−1), and tropical wetlands (0.58 Tg yr−1) for the period 2006–2013 leads to an optimal agreement (i.e., lowest RMSD) between model results and observations

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2019

Funding Information: We thank Aurélie Paquirissamy, Géraud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: 10.18160/P7E9-EKEA , Integrated Non-CO Observing System, 2018a, and 10.18160/B3Q6-JKA0 , Integrated Non-CO Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH: 10.25925/20221001 , Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55). This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810). Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). Sönke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204).Peer reviewedPublisher PD