A Framework for Digital Asset Risks with Insurance Applications

Zhengming Li, Jianxi Su, Maochao Xu, Jimmy Yuen
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Abstract

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.
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具有保险应用价值的数字资产风险框架
从 2009 年比特币的诞生到 2024 年 1 万亿的市场规模,数字资产的显著增长凸显了颠覆性技术背后的动力和全球对数字资产的需求。本文制定了一个框架,以加强精算师对与发展中的数字资产生态系统相关的网络风险的理解,以及在数字资产保险中对其进行衡量的方法。通过整合精算视角,我们旨在加强对微观和系统层面网络风险的理解和建模。定性研究揭示了区块链技术及其相关风险,而我们的定量框架则为数字资产保险组合中的网络风险建模提供了严格的方法。这种多层面的方法有三个主要目标:i) 提供清晰易懂的教育,介绍不断发展的数字资产生态系统及其带来的各种网络风险;ii) 制定科学严谨的框架,量化数字资产生态系统中的网络风险;iii) 提供实际应用,包括定价策略和尾端风险管理。特别是,我们开发了基于真实损失数据的频率-严重性模型,用于对数字资产中的网络风险进行定价,并利用蒙特卡罗模拟来估算尾部风险,为风险管理策略提供了实用见解。随着数字资产不断重塑金融业,我们的工作将成为保障这一快速发展领域的完整性和稳定性的基础性步骤。
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