KVA: Capital Valuation Adjustment

Green, Andrew David; Kenyon, Chris

doi:10.2139/ssrn.2400324

Cited by 64 publications

(85 citation statements)

References 8 publications

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“…These future exposures are used to determine the valuation adjustments like CVA, DVA (Gregory 2010) and more recently KVA (Green et al 2014). It requires simulating the future states of the risk factors and then valuing the derivative in all these future states.…”

Section: Introductionmentioning

confidence: 99%

Smile and default: the role of stochastic volatility and interest rates in counterparty credit risk

Simaitis

Graaf

Hari

et al. 2016

Quantitative Finance

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In this research, we investigate the impact of stochastic volatility and interest rates on counterparty credit risk (CCR) for FX derivatives. To achieve this we analyse two real-life cases in which the market conditions are different, namely during the 2008 credit crisis where risks are high and a period after the crisis in 2014, where volatility levels are low. The Heston model is extended by adding two Hull-White components which are calibrated to fit the EURUSD volatility surfaces. We then present future exposure profiles and credit value adjustments (CVAs) for plain vanilla cross-currency swaps (CCYS), barrier and American options and compare the different results when Heston-Hull-WhiteHull-White or Black-Scholes dynamics are assumed. It is observed that the stochastic volatility has a significant impact on all the derivatives. For CCYS, some of the impact can be reduced by allowing for time-dependent variance. We further confirmed that Barrier options exposure and CVA is highly sensitive to volatility dynamics and that American options' risk dynamics are significantly affected by the uncertainty in the interest rates.

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Section: Introductionmentioning

confidence: 99%

Smile and default: the role of stochastic volatility and interest rates in counterparty credit risk

Simaitis

Graaf

Hari

et al. 2016

Quantitative Finance

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“…KVA is a topic that is currently being discussed in the industry, but most papers devoted to KVA follow the guidelines of [3,6,7,8,9]. The approach establishes a hedging equation for the full price (including capital costs) of the derivative.…”

Section: Introductionmentioning

confidence: 99%

“…Under this approach, it is assumed that the regulatory capital supporting the deal (plus the possibly self-imposed cushion) is remunerated at the hurdle rate, no matter whether it has been provided by the shareholder or charged to the client. It is also suggested in [7] that this capital could reduce external funds obtained from debt holders.…”

Section: Introductionmentioning

confidence: 99%

A Retained Earnings‐Consistent KVA Approach and the Impact of Taxes

Muñoz¹,

Burdeus²,

Contreras³

2019

Wilmott

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KVA represents the extra cost being charged by banks to non-collateralized counterparties in order to remunerate banks' shareholders for the mandatory regulatory capital provided by them throughout the life of the deal. Therefore, KVA represents earnings charged to clients that must be retained in the bank's balance sheet and not be immediately paid out as dividends. Since retained earnings are part of core Tier I capital, future KVAs imply a deduction in today's KVA calculation.Another key component of KVA is the fact that shareholders' returns (dividends and capital gains) are generated after taxes are paid. Therefore, taxes should be reflected in the KVA formula. By treating KVA as retained earnings, we derive a pricing formula that is consistent with full replication of market, counterparty, and funding risks, and that takes the effect of taxes into account. We provide a numerical example where the KVA obtained under this new formula is compared with other approaches yielding significantly lower adjustments. This numerical example also helps us to assess the relevance of taxes.

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“…On the numerical side, starting with Green et al (2014), which extended the hedging framework of Burgard and Kjaer (2011) to price capital requirements of derivatives, another notable work is Elouerkhaoui (2016).…”

Section: Introductionmentioning

confidence: 99%