We update and extend our framework for the integration of ESG in the Strategic Asset Allocation. Our 2022 analysis reflects the swiftly developing environment of ESG and presents a more granular set of optimisation scenarios that now include Paris alignment, EU Principle Adverse Impact (PAI), or an EU Taxonomy focus. Moreover, we also extend our Framework to Liquid Alternatives and the DWS ESG Long View Estimates. Our 2022 analysis reiterates several previous findings and adds new perspectives. The results of our 2022 analysis can be summarised as follows:

_ Our combined optimisation approach that optimises the asset allocation of ESG index instruments beats at anytime a 1:1 ESG index replacement and leads to tangible ESG improvements while minimizing the expected tracking error. For example, an EU PAB level carbon reduction (-50%) of the portfolio is achievable with an estimated asset allocation TE of slightly below 0.25%. NZAM (Net Zero Asset Manager Alliance) path modelling including different ESG facets while incorporating SBTi (Science Based Targets Initiative) targets appear plausible to us. Additional ESG facets such as the Waste and Water intensity or the UNGC signatoryship that have been added for this year as part of our PAI focus could also be, however for higher TE’s, positively affected.
_ We also can confirm that an integration of Alternatives is possible without diluting either the ESG profile or the risk-adjusted returns vs. the traditional SAA. In the majority of cases, the ESG profile of our SAA including Alternatives improves slightly.
_ The optimisation potential among traditional regional and sector indices remains limited. Enhanced ESG portfolio features are only achievable via the integration of ESG indices. On an ex-post basis, the 1:1 switch to passive ESG indices now averages at an implementation tracking error of roughly 0.9% during our back test, slightly above our previous year’s analysis. Still, the historic information ratio (IR) since 2014 is calculated at 0.56.
_ Last but not least, based on the weighted relative ESG improvements of our different optimisation approaches for the defined TEs, we find a massive improvement already at ex-ante asset allocation TEs of 0.25%, which however grows with higher TEs at much lower slopes. The optimal trade-off between ESG and TE depends on the investors' utility function in terms of ESG impact and risk budget to determine the specific optimum.

Like for the previous analysis and to enable wide applicability, our work is based on liquid global asset classes and readily available passive funds for implementation. We seek to understand the potential impact of integrating ESG factors on risk adjusted returns, and to identify the potentially best approach to optimise ESG performance, while minimising tracking error (TE). Our ESG approach is a mixed, multi-faceted approach of not only minimising typical exclusion criteria (negative screening), but also in parallel maximising ESG impacts (positive screening). This is all performed on index level rather than on single security basis to allow implementation scalability. We study the optimisation potential of traditional indices, ESG replacements, and combined optimisations.

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