A Sound Modelling and Backtesting Framework for Forecasting Initial Margin Requirements

Anfuso, Fabrizio; Aziz, Daniel; Giltinan, Paul; Loukopoulos, Klearchos

doi:10.2139/ssrn.2716279

Cited by 23 publications

(10 citation statements)

References 6 publications

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“…The most popular of these methods have been the regression methods, since they offer simplicity and reusability of the Bank's legacy Monte Carlo engines, paired with good results. Inspired by early works of Longstaff-Schwartz [16], several banks have implemented some version of the polinomial regression proposed by [1], [8] and [10], or the Kernel regressions proposed by [2], [10] and [11]. Most recently estimation by Neural Networks have been proposed by [12] and [18].…”

Section: Literature Reviewmentioning

confidence: 99%

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Mathematical Foundations of Regression Methods for Approximating the Forward Initial Margin

2020

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Section: Literature Reviewmentioning

confidence: 99%

“…In the context of initial margin estimation, linear regression methods are proposed by [1], [8] and [10]. The problem setting is as described in equation 9 above.…”

Section: Algorithms Based On Linear Mapsmentioning

confidence: 99%

Mathematical Foundations of Regression Methods for Approximating the Forward Initial Margin

2020

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“…Moreover, adopting graph modeling enables visualization, calculation and testing of the robustness of various hypotheses under alternative parameter assumptions. More technical details on the technology stack used in the simulation can be found in Anfuso et al ( 2017 ); O'Halloran et al ( 2017b ).…”

Section: Models In Crisis: a New Approachmentioning

confidence: 99%

An Artificial Intelligence Approach to Regulating Systemic Risk

O’Halloran

Nowaczyk²

2019

Front. Artif. Intell.

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We apply an artificial intelligence approach to simulate the impact of financial market regulations on systemic risk—a topic vigorously discussed since the financial crash of 2007–09. Experts often disagree on the efficacy of these regulations to avert another market collapse, such as the collateralization of interbank (counterparty) derivatives trades to mitigate systemic risk. A limiting factor is the availability of proprietary bank trading data. Even if this hurdle could be overcome, however, analyses would still be hampered by segmented financial markets where banks trade under different regulatory systems. We therefore adapt a simulation technology, combining advances in graph theoretic models and machine learning to randomly generate entire financial systems derived from realistic distributions of bank trading data. We then compute counterparty credit risk under various scenarios to evaluate and predict the impact of financial regulations at all levels—from a single trade to individual banks to systemic risk. We find that under various stress testing scenarios collateralization reduces the costs of resolving a financial system, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations. Moreover, the concentration of credit risk does not necessarily correlate monotonically with systemic risk. While the analysis focuses on counterparty credit risk, the method generalizes to other risks and metrics in a straightforward manner.

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“…In order to properly define Ψ B (Z 1 ) in L B as a random variable, we assume that the function space H is pointwise measurable. 3 We introduce the following object (cf. (3.1)):…”

Section: Convergence Analysis Of the Economic Capital Sa Algorithm 2 mentioning

confidence: 99%

“…• In the insurance case, Solvency capital is determined as the 99.5%-value-at-risk of the one year loss of the firm for Solvency II (see [11]), and as the 99%-expected shortfall for the Swiss Solvency Test (see [25]); • In the banking case, Basel II Pillar II defines economic capital as the 99% value-at-risk of the depletion over a one-year period of core equity tier I capital (CET1) (where the latter corresponds the one year trading loss of the bank as detailed in [2,Section A.2]); But the FRTB required a shift from 99% valueat-risk to 97.5% expected shortfall as the reference risk measure in capital calculations. Moreover, valueat-risk is relevant to banks for the computation of their initial margin (with a time horizon of one or two weeks, as opposed to one year conventionally in the paper) and, in turn, of their dynamic (conditional) initial margin (see [3]) in the context of the computation of their margin valuation adjustment (MVA).…”

Section: Introductionmentioning

confidence: 99%

Stochastic approximation schemes for economic capital and risk margin computations

et al. 2019

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We consider the problem of the numerical computation of its economic capital by an insurance or a bank, in the form of a value-at-risk or expected shortfall of its loss over a given time horizon. This loss includes the appreciation of the mark-to-model of the liabilities of the firm, which we account for by nested Monte Carlo à la Gordy and Juneja [17] or by regression à la Broadie, Du, and Moallemi [10]. Using a stochastic approximation point of view on value-at-risk and expected shortfall, we establish the convergence of the resulting economic capital simulation schemes, under mild assumptions that only bear on the theoretical limiting problem at hand, as opposed to assumptions on the approximating problems in [17] and [10]. Our economic capital estimates can then be made conditional in a Markov framework and integrated in an outer Monte Carlo simulation to yield the risk margin of the firm, corresponding to a market value margin (MVM) in insurance or to a capital valuation adjustment (KVA) in banking parlance. This is illustrated numerically by a KVA case study implemented on GPUs.

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A Sound Modelling and Backtesting Framework for Forecasting Initial Margin Requirements

Cited by 23 publications

References 6 publications

Mathematical Foundations of Regression Methods for Approximating the Forward Initial Margin

Mathematical Foundations of Regression Methods for Approximating the Forward Initial Margin

An Artificial Intelligence Approach to Regulating Systemic Risk

Stochastic approximation schemes for economic capital and risk margin computations

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