论文标题

图理论模型和投资组合压缩算法

Graph theoretical models and algorithms of portfolio compression

论文作者

Hanics, Mihály Péter

论文摘要

在投资组合压缩中,市场参与者(银行,组织,公司,金融代理商)签署合同,彼此之间产生负债,从而增加了系统性的风险。大型,密集的市场通常可以通过减少义务而不降低每个参与者的净概念来压缩(例如,如果负债使代理之间的周期循环,那么可以减少每个参与者的周期,而没有任何净值的净值更改),而我们的目标是消除尽可能多的过量(在实践中定义为差异和净值之间的差异)。可能会降低压缩有效性的一个限制因素可能是压缩参与者的偏好和优先级,他们可以单独定义压缩条件,在设计清除过程时必须考虑这些条件,否则,参与者可以释放,从而导致设计的清除过程无法执行。这些市场可以通过边缘加权图表得到很好的代表。在本文中,我检查了当参与者代表清算的偏好时,例如,他们将以什么顺序偿还负债(关键因素可能是利率),并且我对这些问题显示了清算算法。最重要的是,由于它是压缩协调授权以最大化压缩量的共同目标,因此我还展示了一种计算网络中最大体积保守压缩的方法。我进一步评估了将这两个模型结合在一起的可能性。还显示了模型的示例和程序代码,还显示了清除算法的A0伪代码。

In portfolio compression, market participants (banks, organizations, companies, financial agents) sign contracts, creating liabilities between each other, which increases the systemic risk. Large, dense markets commonly can be compressed by reducing obligations without lowering the net notional of each participant (an example is if liabilities make a cycle between agents, then it is possible to reduce each of them without any net notional changing), and our target is to eliminate as much excess notional as possible in practice (excess is defined as the difference between gross and net notional). A limiting factor that may reduce the effectiveness of the compression can be the preferences and priorities of compression participants, who may individually define conditions for the compression, which must be considered when designing the clearing process, otherwise, a participant may bail out, resulting in the designed clearing process to be impossible to execute. These markets can be well-represented with edge-weighted graphs. In this paper, I examine cases when preferences of participants on behalf of clearing are given, e.g., in what order would they pay back their liabilities (a key factor can be the rate of interest) and I show a clearing algorithm for these problems. On top of that, since it is a common goal for the compression coordinating authority to maximize the compressed amount, I also show a method to compute the maximum volume conservative compression in a network. I further evaluate the possibility of combining the two models. Examples and program code of the model are also shown, also a0 pseudo-code of the clearing algorithms.

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