A Framework for Digital Asset Risks with Insurance Applications
<|reference_start|>A Framework for Digital Asset Risks with Insurance Applications: The remarkable growth of digital assets, starting from the inception of Bitcoin in 2009 into a 1 trillion market in 2024, underscores the momentum behind disruptive technologies and the global appetite for digital assets. This paper develops a framework to enhance actuaries' understanding of the cyber risks associated with the developing digital asset ecosystem, as well as their measurement methods in the context of digital asset insurance. By integrating actuarial perspectives, we aim to enhance understanding and modeling of cyber risks at both the micro and systemic levels. The qualitative examination sheds light on blockchain technology and its associated risks, while our quantitative framework offers a rigorous approach to modeling cyber risks in digital asset insurance portfolios. This multifaceted approach serves three primary objectives: i) offer a clear and accessible education on the evolving digital asset ecosystem and the diverse spectrum of cyber risks it entails; ii) develop a scientifically rigorous framework for quantifying cyber risks in the digital asset ecosystem; iii) provide practical applications, including pricing strategies and tail risk management. Particularly, we develop frequency-severity models based on real loss data for pricing cyber risks in digit assets and utilize Monte Carlo simulation to estimate the tail risks, offering practical insights for risk management strategies. As digital assets continue to reshape finance, our work serves as a foundational step towards safeguarding the integrity and stability of this rapidly evolving landscape.<|reference_end|>
@article{li2024a,
    title={A Framework for Digital Asset Risks with Insurance Applications},
    author={Zhengming Li, Jianxi Su, Maochao Xu, and Jimmy Yuen},
    journal={arXiv preprint arXiv:2408.17227},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17227},
    primaryClass={stat.AP cs.CE}
}