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arXiv 1909.12904q-fin.RMq-fin.PM
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基于预期损失的动态资产配置量子退火算法

Quantum Annealing Algorithm for Expected Shortfall based Dynamic Asset Allocation

Samudra Dasgupta, Arnab Banerjee

摘要:

2008年的抵押贷款危机是一个极端事件的例子。极值理论试图估计这种尾部风险。现代金融从业者更喜欢基于预期损失的风险度量(捕捉尾部风险),而不是波动率甚至风险价值等传统方法。针对具有预期损失约束的动态资产配置问题,提出了一种QUBO形式的量子退火算法。它的动机是需要完善目前的量子算法的Markowitz类型的问题,这是学术上有趣的,但没有实用的从业者。该算法是动态的,风险目标从市场波动中自然涌现。此外,它避免了复杂的统计,如广义帕累托分布。它将问题转化为适合于D-Wave等量子退火器实现的量子比特形式。这样的QUBO算法预计使用量子退火系统比使用经典计算机的任何经典算法更快地解决(但尚未在规模上证明)。

英文摘要:

The 2008 mortgage crisis is an example of an extreme event. Extreme value theory tries to estimate such tail risks. Modern finance practitioners prefer Expected Shortfall based risk metrics (which capture tail risk) over traditional approaches like volatility or even Value-at-Risk. This paper provides a quantum annealing algorithm in QUBO form for a dynamic asset allocation problem using expected shortfall constraint. It was motivated by the need to refine the current quantum algorithms for Markowitz type problems which are academically interesting but not useful for practitioners. The algorithm is dynamic and the risk target emerges naturally from the market volatility. Moreover, it avoids complicated statistics like generalized pareto distribution. It translates the problem into qubit form suitable for implementation by a quantum annealer like D-Wave. Such QUBO algorithms are expected to be solved faster using quantum annealing systems than any classical algorithm using classical computer (but yet to be demonstrated at scale).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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