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𝗘𝗜𝗢𝗣𝗔'𝘀 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝘁𝗼 𝗦𝘆𝘀𝘁𝗲𝗺𝗶𝗰 𝗖𝘆𝗯𝗲𝗿 𝗧𝗵𝗿𝗲𝗮𝘁𝘀
The strategy employs four interlocking pillars to build a multi-layered defense. It is anchored in enhancing foundational digital operational resilience across the financial market through collaboration with other European Supervisory Authorities and crucial oversight of critical third-party service providers. This internal strengthening is complemented by a public-facing initiative to close the significant cyber protection gap, promoting informed decision-making to encourage mitigation and adaptation actions among businesses and citizens. To sustain these efforts amid rapid digitalization, EIOPA mandates the continuous adaptation of supervisory frameworks, leveraging SupTech and enhanced data sharing to detect vulnerabilities and structural shifts more efficiently. These pillars are unified through fostering collaborative risk management, working with other relevant EU and international authorities to enable a coordinated response.

This study introduces a novel capital allocation mechanism for banks, using game theory to assign capital requirements while enforcing macro-prudential standards. Based on competition for lower requirements, the approach employs insensitive risk measures from Chen et al. (2013) and Kromer et al. (2016), typically yielding a unique Nash allocation rule, while sensitive measures from Feinstein et al. (2017) may need additional conditions for uniqueness. The Eisenberg-Noe (2001) clearing system is analyzed for systemic risk, with numerical Nash allocations demonstrated. The study claims that further investigation into properties like continuity, monotonicity, or convexity is needed, noting that not all can hold simultaneously due to firm interactions.

This paper defines vector-valued risk measures using axioms and shows they ignore dependence structures of input random vectors, unlike set-valued risk measures. Convex vector-valued risk measures are unsuitable for capital allocation in various financial applications, including systemic risk measures. The results also generalize to conditional settings.