Abstract
In this chapter, we simulate and analyze the impact of financial regulations concerning the collateralization of derivative trades on systemic risk—a topic that has been vigorously discussed since the financial crisis in 2007/08. Experts often disagree on the efficacy of these regulations. Compounding this problem, banks regard their trade data required for a full analysis as proprietary. We adapt a simulation technology combining advances in graph theory to randomly generate entire financial systems sampled from realistic distributions with a novel open-source risk engine to compute risks in financial systems under different regulations. This allows us to consistently evaluate, predict, and optimize the impact of financial regulations on all levels—from a single trade to systemic risk—before it is implemented. The resulting data set is accessible to contemporary data science techniques like data mining, anomaly detection, and visualization. We find that collateralization reduces the costs of resolving a financial system in crisis, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations.
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Notes
- 1.
The corresponding outstanding notional amount of all contracts in 2016 was $544,052 billions of the US dollars. See Bank of International Settlements, Global OTC Derivatives Market Semi-Annual Statistics, March 6, 2017. http://stats.bis.org/statx/srs/table/d5.1.
- 2.
In this chapter, we assume all our counterparties to be banks.
- 3.
In an earlier paper, see [18], we examined a case study that analyzed a single financial system under varying regulatory regimes, see also.
- 4.
In reality, these graphs are significantly more complex. According to an analysis carried out in [10] based on central bank data, the Brazilian financial system for example has about 2400 banks heavily interconnected via 20,000 links.
- 5.
See [4] for an overview of graph theoretic modeling of large-scale networks.
- 6.
A way to capture this technically is to give all trades in the financial system a globally unique ID. The trade relation function is then a function
, where 
denotes the set of finite subsets of 
(assuming each trade ID is a natural number). For instance, if the trades in Fig. 3 where enumerated by 0, 1, 2, …, 4 and IRS1 corresponds to 0, then for the trade relation t between bank A and B, we would have τ(t) = {0}. - 7.
Due to the finite number of Monte Carlo paths, it is sometimes even numerically zero in the simulation.
- 8.
It should be highlighted that in our simulation we model the bilateral trading between various banks, where Initial Margin is posted into segregated accounts. Derivatives that are cleared through a central counterparty (CCP) or exchange traded derivatives (ETDs) are not in scope of this simulation.
- 9.
Will be made available on http://fintech.datascience.columbia.edu/.
- 10.
The question to what extent exactly Initial Margin reduces exposure to CCR is subject to debate even when considering only one counterparty. In [2], the authors argue that when taking time lags between trade payments and margin reposting into account, the reduction is much smaller.
, where 
denotes the set of finite subsets of 
(assuming each trade ID is a natural number). For instance, if the trades in Fig. References
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O’Halloran, S., Nowaczyk, N., Gallagher, D., Subramaniam, V. (2019). A Data Science Approach to Predict the Impact of Collateralization on Systemic Risk. In: Karampelas, P., Kawash, J., Özyer, T. (eds) From Security to Community Detection in Social Networking Platforms. ASONAM 2017. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-11286-8_8
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