Quantum algorithm for calculating risk contributions in a credit portfolio
Quantum algorithm for calculating risk contributions in a credit portfolio
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DOI：
10.1140/epjqt/s40507-022-00153-y
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发表时间：
2022-01
影响因子：
5.3
通讯作者：
Koichi Miyamoto
中科院分区：
文献类型：
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作者：
Koichi Miyamoto
金融是量子计算产业应用前景广阔的领域之一。特别是，提出了用于计算风险度量的量子算法，如信用组合的风险值和条件风险值。本文重点研究了信用风险管理中的另一个问题，即风险贡献的计算，该问题量化了投资组合中风险在子组上的集中程度。在量子同时估计多个期望值算法的基础上，提出了一种计算信用风险贡献的方法。我们还评估了所提出方法的查询复杂性，并看到它与具有复杂性的经典方法相比，随着子群数和精度的变化而变化。这意味着，在计算精细划分的子组的风险贡献时，与计算整个投资组合的风险度量相比，量子方法的优势被降低了。然而，量子方法在高精度计算方面具有优势，并且在一些实际可行的情况下，实际上比经典方法产生的复杂性更小。
Finance is one of the promising field for industrial application of quantum computing. In particular, quantum algorithms for calculation of risk measures such as the value at risk and the conditional value at risk of a credit portfolio have been proposed. In this paper, we focus on another problem in credit risk management, calculation of risk contributions, which quantify the concentration of the risk on subgroups in the portfolio. Based on the recent quantum algorithm for simultaneous estimation of multiple expected values, we propose the method for credit risk contribution calculation. We also evaluate the query complexity of the proposed method and see that it scales ason the subgroup numberand the accuracy ϵ, in contrast with the classical method withcomplexity. This means that, for calculation of risk contributions of finely divided subgroups, the advantage of the quantum method is reduced compared with risk measure calculation for the entire portfolio. Nevertheless, the quantum method can be advantageous in high-accuracy calculation, and in fact yield less complexity than the classical method in some practically plausible setting.