Greenhouse Gas Footprint Minimization of Credit Default Swap Baskets

Abstract: Global bond market capitalization amounts to approximately $100 trillion, compared to $60 trillion in the equity markets. Despite debt financing being a large part of the global financial market, the measurements and greenhouse gas reduction investment strategies to date are not nearly as thorough as for equity financing. More recently, the problem has been brought into light by the World Bank, expressing concerns about the crucial role of debt financing activities in the current and upcoming threats caused by climate change. A commonly used credit derivative in debt financing is credit default swaps (CDS), which is an agreement between two parties to exchange the credit risk of a reference entity. The buyer of the contract makes fixed periodic payments to the seller of the contract, who collects the premiums in exchange for making the protection buyer whole in the case of a defaulting reference entity. This thesis aims to minimize the greenhouse gas emission exposure for two CDS indices, iTraxx Main and CDX.IG, each consisting of 125 equally weighted constituents, or companies. The CDS indices are widely used high liquid fixed income instruments. In 2017, iTraxx Main had a monthly trading volume of $330-440 billion notional, and CDX.IG a corresponding volume of $200-275 billion. In order to rate the greenhouse gas emissions of the constituents, the ECOBAR model was used. The model utilizes a discrete ranking score system, where the aim is to obtain as low score as possible. To minimize the ECOBAR score for the baskets, Markowitz Modern Portfolio Theory was used, implemented by using a quadratic programming algorithm. By optimizing the portfolios while retaining a low tracking error and high correlation toward the CDS indices, underlying investment properties were retained. We show that one can construct replicated portfolios of the CDS indices that have significantly lower ECOBAR scores than the indices themselves, whilst still maintaining a low tracking error and high correlation with the actual indices. When constructing baskets of fewer constituents, one can replicate the indices with merely 10-30 constituents, without worsening the tracking error or correlation substantially, and obtain an even lower ECOBAR score for the respective portfolios.