Calculating the Funding Valuation Adjustment (FVA) of Value-at-Risk (VAR) Based Initial Margin

Green, Andrew David; Kenyon, Chris

doi:10.2139/ssrn.2432281

Cited by 7 publications

(8 citation statements)

References 5 publications

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“…So for example, a historical simulation full re-valuation model would require a historical simulation and full revaluation at each point inside the outer Monte Carlo that captures the capital exposure. This paper will not address the use of IMM for market risk, rather the reader is referred to Green and Kenyon (2014) for details of a suitable computational technique to accelerate this calculation. 10 9 Under the current proposals contained in the Fundamental Review of the Trading Book, expected shortfall (CVAR) will replace VAR and the price shocks will be in most cases taken over periods longer than 10 days (BCBS-265 2013).…”

Section: Immmentioning

confidence: 99%

KVA: Capital Valuation Adjustment

Green

Kenyon²

2014

SSRN Journal

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Credit (CVA), Debit (DVA) and Funding Valuation Adjustments (FVA) are now familiar valuation adjustments made to the value of a portfolio of derivatives to account for credit risks and funding costs. However, recent changes in the regulatory regime and the increases in regulatory capital requirements has led many banks to include the cost of capital in derivative pricing. This paper formalises the addition of cost of capital by extending the Burgard-Kjaer (2013) semi-replication approach to CVA and FVA to include an addition capital term, Capital Valuation Adjustment (KVA). 1

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Section: Immmentioning

confidence: 99%

KVA: Capital Valuation Adjustment

Green

Kenyon²

2014

SSRN Journal

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“…In particular, it is assumed that no initial margin needs to be posted for these collateralized derivatives. Green and Kenyon (Green and Kenyon 2014) discuss the inclusion of initial margin in the context of funding costs. The funding term for initial margin is discussed in (Nauta 2015c).…”

Section: Estimating the Optimal Liquidity Buffermentioning

confidence: 99%

A Model for the Valuation of Assets with Liquidity Risk

Nauta

2015

SSRN Journal

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This paper describes a model for the valuation of assets on a bank balance sheet with liquidity risk. The new feature of this model is that it explicitly incorporates the funding term of an asset. The inclusion of the funding term is important since it determines the expected liquidation loss. By minimizing the sum of the expected liquidation loss and funding costs the optimal funding term and value of the asset can be determined. This paper applies the model to single cash flows, loans, bonds, and derivatives. Also, the calibration to LIBOR basis spreads is discussed.

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“…That is, we regress the value of each trade across an expanded state space against the basis trade set, at every time point of interest (including t = 0), which we term stopping dates. For trades that are Bermudan-callable this is done using Early-Start Longstaff-Schwartz (Longstaff and Schwartz 2001;Wang and Caflish 2009), for those that are not Bermudan-callable we can apply the simpler Augmented State Space approach (Green and Kenyon 2014a). We are interested in many time points for XVA calculations because these involve integrals over time (Burgard and Kjaer 2013;Green, Kenyon, and Dennis 2014).…”

Section: Trade-level Regressionmentioning

confidence: 99%

“…Valuation adjustments are collectively known as XVA. XVA includes the effects of credit (CVA, DVA) (Gregory 2009;Kenyon and Kenyon 2013), funding (FVA, MVA) (Burgard and Kjaer 2013;Green and Kenyon 2014a), capital (KVA) (Green, Kenyon, and Dennis 2014), and Tax (TVA) (Kenyon and Green 2014a).…”

Section: Introductionmentioning

confidence: 99%

“…Our combination of these elements enables orders of magnitude improvements on computation times, and orders of magnitude reductions in system implementation times and costs. Technically this paper generalizes (Green and Kenyon 2014a) from MVA to XVA and adds sensitivities and allocation, it also makes explicit elements implicit in (Green and Kenyon 2014b).…”

Section: Introductionmentioning

confidence: 99%

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