Dynamic Initial Margin Estimation Based on Quantiles of Johnson Distributions

McWalter, Thomas A.; Kienitz, Jörg; Nowaczyk, Nikolai; Rudd, Ralph; Acar, Sarp Kaya

doi:10.2139/ssrn.3147811

Cited by 3 publications

(5 citation statements)

References 23 publications

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“…Often, it is only possible to obtain several independent runs of the portfolio scenarios. Thus, methods such as Gaussian Least Square Monte-Carlo (GLSMC) and Johnson Least Square Monte-Carlo (JLSMC) [9,3] are used to extract information about the distribution of the portfolio value change only from such outer simulations. The data usually available from outer simulations is illustrated in Figure 3.…”

Section: Nonnested Monte-carlo: Cross-sectional Moment-matching Methodsmentioning

confidence: 99%

“…• JLSMC [9]: The estimated first four raw moments r 1 , r 2 , r 3 , r 4 can be used to fit an appropriate Johnson's distribution via Moment Matching method [5] with (approximately) these first four moments. The so obtained Johnson's distribution is then used to estimate the conditional quantile.…”

Section: Moment-matching Methods: Glsmc and Jlsmcmentioning

confidence: 99%

“…A detailed procedure and the settings used for JLSMC is presented in [9] and is not repeated here for the sake of brevity. However, it is noted here that the moment matching algorithm [5] originally designed for smaller sets of observations is quite involved.…”

Section: Moment-matching Methods: Glsmc and Jlsmcmentioning

confidence: 99%

“…where xi(j), lambda(j), delta(j), gamma(j) = ξ j , λ j , δ j , γ j are the parameters of the Johnsons distribution, and type(j) represents the type or family of the distribution. The pdf of Johnson's distributions belonging to different families is given by [6,9]: • S B family,…”

Section: Moment-matching Methods: Glsmc and Jlsmcmentioning

confidence: 99%

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Estimating Future VaR from Value Samples and Applications to Future Initial Margin

Ganesan¹,

Hientzsch²

2021

Preprint

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Predicting future values at risk (fVaR) is an important problem in finance. They arise in the modelling of future initial margin requirements for counterparty credit risk and future market risk VaR. One is also interested in derived quantities such as: i) Dynamic Initial Margin (DIM) and Margin Value Adjustment (MVA) in the counterparty risk context; and ii) risk weighted assets (RWA) and Capital Value Adjustment (KVA) for market risk. This paper describes several methods that can be used to predict fVaRs. We begin with the Nested MCempirical quantile method as benchmark, but it is too computationally intensive for routine use. We review several known methods and discuss their novel applications to the problem at hand. The techniques considered include computing percentiles from distributions (Normal and Johnson) that were matched to parametric moments or percentile estimates, quantile regressions methods, and others with more specific assumptions or requirements. We also consider how limited inner simulations can be used to improve the performance of these techniques. The paper also provides illustrations, results, and visualizations of intermediate and final results for the various approaches and methods.

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Section: Nonnested Monte-carlo: Cross-sectional Moment-matching Methodsmentioning

confidence: 99%

Section: Moment-matching Methods: Glsmc and Jlsmcmentioning

confidence: 99%

Section: Moment-matching Methods: Glsmc and Jlsmcmentioning

confidence: 99%

Section: Moment-matching Methods: Glsmc and Jlsmcmentioning

confidence: 99%

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Estimating Future VaR from Value Samples and Applications to Future Initial Margin

Ganesan¹,

Hientzsch²

2021

Preprint

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“…They investigate potential extensions and improvements. McWalter et al (2018) also provide estimation of a dynamic initial margin model with three approaches: Nested Monte Carlo, Gaussian Least-Squares Monte Carlo, and the Johnson Least-Squares Monte Carlo (JLSMC) Algorithm. Caspers et al (2018) describe initial margin forecast methodology for Bermuda swaption.…”

Section: Literature Reviewmentioning

confidence: 99%

Utilization Schemes of the Pre-Settlement Risk Limits

Wybieralski

2024

JoFFL

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The purpose of the article is to investigate the selected method employed to manage the counterparty credit risk, namely the application of various risk limits. The aim is to recognize utilization schemes of the pre-settlement risk limits in the Polish OTC derivatives market in the relationship between a financial institution and a non-financial counterparty. They are used not only to cover the credit exposure but also to support and enhance the entire market risk management process and day-to-day operations in the financial institutions. Methodology. The research method comprises the analysis of recommendations of the Polish Financial Supervision Authority as well as reports, documents and market risk management principles of selected financial institutions (WSE listed banks). Results of the research. The study indicates two utilization schemes of the pre-settlement limit setup applicable both for daily and credit-related transactions. The first one assumes that the risk requirements remain unchanged during the contract lifetime, the second one considers variable risk requirements over time. Practical implications are discussed (in relation to a notional trade size, risk exposure and margining policy).

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