Journal articles: 'Funding Valuation Adjustment'

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CRÉPEY, STÉPHANE, RÉMI GERBOUD, ZORANA GRBAC, and NATHALIE NGOR. "COUNTERPARTY RISK AND FUNDING: THE FOUR WINGS OF THE TVA." International Journal of Theoretical and Applied Finance 16, no. 02 (March 2013): 1350006. http://dx.doi.org/10.1142/s0219024913500064.

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The credit crisis and the ongoing European sovereign debt crisis have highlighted the native form of credit risk, namely the counterparty risk. The related credit valuation adjustment (CVA), debt valuation adjustment (DVA), liquidity valuation adjustment (LVA) and replacement cost (RC) issues, jointly referred to in this paper as total valuation adjustment (TVA), have been thoroughly investigated in the theoretical papers [8, 9]. The present work provides an executive summary and numerical companion to these papers, through which the TVA pricing problem can be reduced to Markovian pre-default TVA BSDEs. The first step consists in the counterparty clean valuation of a portfolio of contracts, which is the valuation in a hypothetical situation where the two parties would be risk-free and funded at a risk-free rate. In the second step, the TVA is obtained as the value of an option on the counterparty clean value process called contingent credit default swap (CCDS). Numerical results are presented for interest rate swaps in the Vasicek, as well as in the inverse Gaussian Hull-White short rate model, which allows also to assess the related model risk issue.

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Han, Meng, Yeqi He, and Hu Zhang. "A note on discounting and funding value adjustments for derivatives." Journal of Financial Engineering 01, no. 01 (March 2014): 1450008. http://dx.doi.org/10.1142/s2345768614500081.

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In this paper, valuation of a derivative partially collateralized in a specific foreign currency defined in its credit support annex traded between default-free counterparties is studied. Two pricing approaches — by hedging and by expectation — are presented to obtain similar valuation formulae which are equivalent under certain conditions. Our findings show that the current marking-to-market value of such a derivative consists of three components: the price of the perfectly collateralized derivative (a.k.a. the price by collateral rate discounting), the value adjustment due to different funding spreads between the payoff currency and the collateral currency, and the value adjustment due to funding requirements of the uncollateralized exposure. These results generalize previous works on discounting for fully collateralized derivatives and on funding value adjustments for partially collateralized or uncollateralized derivatives.

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NAUTA, BERT-JAN. "LIQUIDITY RISK, INSTEAD OF FUNDING COSTS, LEADS TO A VALUATION ADJUSTMENT FOR DERIVATIVES AND OTHER ASSETS." International Journal of Theoretical and Applied Finance 18, no. 02 (March 2015): 1550014. http://dx.doi.org/10.1142/s0219024915500144.

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Traditionally derivatives have been valued in isolation. The balance sheet of which a derivative position is part, was not included in the valuation. Recently however, aspects of the valuation have been revised to incorporate certain elements of the balance sheet. Examples are the debt valuation adjustment which incorporates default risk of the bank holding the derivative, and the funding valuation adjustment that some authors have proposed to include the cost of funding into the valuation. This paper investigates the valuation of derivatives as part of a balance sheet. In particular, the paper considers funding costs, default risk and liquidity risk. A valuation framework is developed under the elastic funding assumption. This assumption states that funding costs reflect the quality of the assets, and any change in asset composition is immediately reflected in the funding costs. The result is that funding costs should not affect the value of derivatives. Furthermore, a new model for pricing liquidity risk is described. The paper highlights that the liquidity spread, used for discounting cashflows of illiquid assets, should be expressed in terms of the liquidation value (LV) of the asset, and the probability that the institution holding the asset needs to liquidate its assets.

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Singh, Derek, and Shuzhong Zhang. "Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk." Applied Economics and Finance 7, no. 6 (October 27, 2020): 70. http://dx.doi.org/10.11114/aef.v7i6.5060.

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This paper investigates calculations of robust X-Value adjustment (XVA), in particular, credit valuation adjustment (CVA) and funding valuation adjustment (FVA), for over-the-counter derivatives under distributional ambiguity using Wasserstein distance as the ambiguity measure. Wrong way counterparty credit risk and funding risk can be characterized (and indeed quantified) via the robust XVA formulations. The simpler dual formulations are derived using recent Lagrangian duality results. Next, some computational experiments are conducted to measure the additional XVA charges due to distributional ambiguity under a variety of portfolio and market configurations. Finally some suggestions for further work are discussed.

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Wu, Lixin, and Chonhong Li. "FVA and CVA under margining." Studies in Economics and Finance 32, no. 3 (August 3, 2015): 298–321. http://dx.doi.org/10.1108/sef-08-2014-0162.

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Purpose – The purpose of this paper is to provide a framework of replication pricing of derivatives and identify funding valuation adjustment (FVA) and credit valuation adjustments (CVA) as price components. Design/methodology/approach – The authors propose the notion of bilateral replication pricing. In the absence of funding cost, it reduces to unilateral replication pricing. The absence of funding costs, it introduces bid–ask spreads. Findings – The valuation of CVA can be separated from that of FVA, so-called split up. There may be interdependence between FVA and the derivatives value, which then requires a recursive procedure for their numerical solution. Research limitations/implications – The authors have assume deterministic interest rates, constant CDS rates and loss rates for the CDS. The authors have also not dealt with re-hypothecation risks. Practical implications – The results of this paper allow user to identify CVA and FVA, and mark to market their derivatives trades according to the recent market standards. Originality/value – For the first time, a line between the risk-neutral pricing measure and the funding risk premiums is drawn. Also, the notion of bilateral replication pricing extends the unilateral replication pricing.

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Chataigner, Marc, and Stéphane Crépey. "Credit Valuation Adjustment Compression by Genetic Optimization." Risks 7, no. 4 (September 29, 2019): 100. http://dx.doi.org/10.3390/risks7040100.

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Since the 2008–2009 financial crisis, banks have introduced a family of X-valuation adjustments (XVAs) to quantify the cost of counterparty risk and of its capital and funding implications. XVAs represent a switch of paradigm in derivative management, from hedging to balance sheet optimization. They reflect market inefficiencies that should be compressed as much as possible. In this work, we present a genetic algorithm applied to the compression of credit valuation adjustment (CVA), the expected cost of client defaults to a bank. The design of the algorithm is fine-tuned to the hybrid structure, both discrete and continuous parameter, of the corresponding high-dimensional and nonconvex optimization problem. To make intensive trade incremental XVA computations practical in real-time as required for XVA compression purposes, we propose an approach that circumvents portfolio revaluation at the cost of disk memory, storing the portfolio exposure of the night so that the exposure of the portfolio augmented by a new deal can be obtained at the cost of computing the exposure of the new deal only. This is illustrated by a CVA compression case study on real swap portfolios.

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Brigo, Damiano, and Andrea Pallavicini. "Nonlinear consistent valuation of CCP cleared or CSA bilateral trades with initial margins under credit, funding and wrong-way risks." Journal of Financial Engineering 01, no. 01 (March 2014): 1450001. http://dx.doi.org/10.1142/s2345768614500019.

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The introduction of Central Clearing Counterparties (CCPs) in most derivative transactions will dramatically change the landscape of derivatives pricing, hedging and risk management, and, according to the TABB Group, will lead to an overall liquidity impact of about USD 2 trillions. In this paper, we develop for the first time a comprehensive approach for pricing under CCP clearing, including variation and initial margins, gap credit risk and collateralization, showing concrete examples for interest rate swaps. This framework stems from our 2011 framework on credit, collateral and funding costs in Pallavicini et al. (Pallavicini, A., D. Perini and D. Brigo, 2011, Funding Valuation Adjustment: FVA consistent with CVA, DVA, WWR, Collateral, Netting and Re-hypothecation, arxiv.org, ssrn.com). Mathematically, the inclusion of asymmetric borrowing and lending rates in the hedge of a claim, and a replacement closeout at default, lead to nonlinearities showing up in claim dependent pricing measures, aggregation dependent prices, nonlinear Partial Differential Equations (PDEs) and Backward Stochastic Differential Equations (BSDEs). This still holds in presence of CCPs and CSA. We introduce a modeling approach that allows us to enforce rigorous separation of the interconnected nonlinear risks into different valuation adjustments where the key pricing nonlinearities are confined to a funding costs component that is analyzed through numerical schemes for BSDEs. We present a numerical case study for Interest Rate Swaps that highlights the relative size of the different valuation adjustments and the quantitative role of initial and variation margins, of liquidity bases, of credit risk, of the margin period of risk and of wrong-way risk correlations.

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WU, LIXIN. "CVA AND FVA TO DERIVATIVES TRADES COLLATERALIZED BY CASH." International Journal of Theoretical and Applied Finance 18, no. 05 (July 28, 2015): 1550035. http://dx.doi.org/10.1142/s0219024915500351.

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In this paper, we consider replication pricing of derivatives that are partially collateralized by cash. We let issuer replicate the derivatives payout using shares and cash, and let buyer replicate the loss given the counterparty default using credit default swaps. The costs of funding for replication and collateral posting are taken into account in the pricing process. A partial differential equation (PDE) for the derivatives price is established, and its solution is provided in a Feynman–Kac formula, which decomposes the derivatives value into the risk-free value of the derivative plus credit valuation adjustment (CVA) and funding valuation adjustment (FVA). For most derivatives, we show that CVAs can be evaluated analytically or semi-analytically, while FVAs as well as the derivatives values can be solved recursively through numerical procedures due to their interdependence. In numerical demonstrations, continuous and discrete margin revisions are considered, respectively, for an equity call option and a vanilla interest-rate swap.

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Iyer, Subramaniam. "Stochastic Actuarial Modelling of a Defined-Benefit Social Security Pension Scheme: An Analytical Approach." Annals of Actuarial Science 3, no. 1-2 (September 2008): 127–85. http://dx.doi.org/10.1017/s174849950000049x.

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ABSTRACTAmong the systems in place in different countries for the protection of the population against the long-term contingencies of old-age (or retirement), disability and death (or survivorship), defined-benefit social security pension schemes, i.e. social insurance pension schemes, by far predominate, despite the recent trend towards defined-contribution arrangements in social security reforms. Actuarial valuations of these schemes, unlike other branches of insurance, continue to be carried out almost exclusively on traditional, deterministic lines. Stochastic applications in this area, which have been restricted mainly to occasional special studies, have relied on the simulation technique. This paper develops an analytical model for the stochastic actuarial valuation of a social insurance pension scheme. Formulae are developed for the expected values, variances and covariances of and among the benefit expenditure and salary bill projections and their discounted values, allowing for stochastic variation in three key input factors, i.e., mortality, new entrant intake, and interest (net of salary escalation). Each deterministic output of the valuation is thus supplemented with a confidence interval, that is, a range with an attached probability. The treatment covers the premiums under the different possible financial systems for these schemes, which differ from the funding methods of private pensions, as well as the testing of the level of the Fund ratio when the future contributions schedule is pre-determined. Although it is based on a relatively simplified approach and refers only to retirement pensions, with full adjustment in line with salary escalation, the paper brings out the stochastic features of pension scheme projections and illustrates a comprehensive stochastic valuation. It is hoped that the paper will stimulate interest in further research, both of a theoretical and a practical nature, and lead to progressively increasing recourse to stochastic methods in social insurance pension scheme valuations.

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Prorokowski, Lukasz, and Hubert Prorokowski. "FVA – Sailing on the uncharted waters." Journal of Financial Regulation and Compliance 23, no. 1 (February 9, 2015): 31–54. http://dx.doi.org/10.1108/jfrc-01-2014-0005.

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Purpose – This report aims to investigate the approaches taken by financial institution to implement and compute the funding valuation adjustment (FVA). FVA can be defined as the incremental cost attributable to trades with non-collateralised counterparties. This cost emerges related to the need to fund the collateral calls associated with collateralised hedge trades. In this respect, one common challenge faced by banks relates to designing appropriate methodological approaches to compute an FVA. Design/methodology/approach – Recognising the growing importance of an FVA, this report is designed to investigate different approaches to computing FVA and pricing funding costs into the uncollateralised positions. Embarking on semi-structured interviews, the report explores the methodologies and structural solutions utilised by global banks. Findings – This report has indicated several influential trends that shape the nascent FVA landscape, as well as innovative initiatives undertaken by the banks to effectively use this metric. Going forward, this paper has pointed to a number of current and future challenges faced by the participating banks with regard to implementing the FVA. Originality/value – Before regulators make FVA punitive, unprofitable or inadequate by propagating the move to collateralised trading or introducing sanctions on banks recognising FVA in their financial statements, and thus reducing banks’ exposure to arbitrage opportunities, FVA will remain a challenging metric for the banking industry in the near future. Therefore, it is pivotal to understand any potential risks and operational difficulties arising from “sailing on the uncharted waters with FVA”. Moreover, it is necessary to understand market consensus on methodological approaches to computing FVA, as well as practices around constructing the bank’s own cost of funds curves.

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WU, LIXIN, and DAWEI ZHANG. "xVA: DEFINITION, EVALUATION AND RISK MANAGEMENT." International Journal of Theoretical and Applied Finance 23, no. 01 (February 2020): 2050006. http://dx.doi.org/10.1142/s0219024920500065.

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xVA is a collection of valuation adjustments made to the classical risk-neutral valuation of a derivative or derivatives portfolio for pricing or for accounting purposes, and it has been a matter of debate and controversy. This paper is intended to clarify the notion of xVA as well as the usage of the xVA items in pricing, accounting or risk management. Based on bilateral replication pricing using shares and credit default swaps, we attribute the P&L of a derivatives trade into the compensation for counterparty default risks and the costs of funding. The expected present values of the compensation and the funding costs under the risk-neutral measure are defined to be the bilateral CVA and FVA, respectively. The latter further breaks down into FCA, MVA, ColVA and KVA. We show that the market funding liquidity risk, but not any idiosyncratic funding risks, can be bilaterally priced into a derivative trade, without causing price asymmetry between the counterparties. We call for the adoption of VaR or CVaR methodologies for managing funding risks. The pricing of xVA of an interest-rate swap is presented.

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Albanese, Claudio, Yannick Armenti, and Stéphane Crépey. "XVA metrics for CCP optimization." Statistics & Risk Modeling 37, no. 1-2 (March 1, 2020): 25–53. http://dx.doi.org/10.1515/strm-2017-0034.

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AbstractBased on an XVA analysis of centrally cleared derivative portfolios, we consider two capital and funding issues pertaining to the efficiency of the design of central counterparties (CCPs). First, we consider an organization of a clearing framework, whereby a CCP would also play the role of a centralized XVA calculator and management center. The default fund contributions would become pure capital at risk of the clearing members, remunerated as such at some hurdle rate, i.e. return-on-equity. Moreover, we challenge the current default fund Cover 2 EMIR sizing rule with a broader risk based approach, relying on a suitable notion of economic capital of a CCP. Second, we compare the margin valuation adjustments (MVAs) resulting from two different initial margin raising strategies. The first one is unsecured borrowing by the clearing member. As an alternative, the clearing member delegates the posting of its initial margin to a so-called specialist lender, which, in case of default of the clearing member, receives back from the CCP the portion of IM unused to cover losses. The alternative strategy results in a significant MVA compression. A numerical case study shows that the volatility swings of the IM funding expenses can even be the main contributor to an economic capital based default fund of a CCP. This is an illustration of the transfer of counterparty risk into liquidity risk triggered by extensive collateralization.

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ABBAS-TURKI, LOKMAN A., STÉPHANE CRÉPEY, and BABACAR DIALLO. "XVA PRINCIPLES, NESTED MONTE CARLO STRATEGIES, AND GPU OPTIMIZATIONS." International Journal of Theoretical and Applied Finance 21, no. 06 (September 2018): 1850030. http://dx.doi.org/10.1142/s0219024918500309.

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We present a nested Monte Carlo (NMC) approach implemented on graphics processing units (GPUs) to X-valuation adjustments (XVAs), where X ranges over C for credit, F for funding, M for margin, and K for capital. The overall XVA suite involves five compound layers of dependence. Higher layers are launched first, and trigger nested simulations on-the-fly whenever required in order to compute an item from a lower layer. If the user is only interested in some of the XVA components, then only the sub-tree corresponding to the most outer XVA needs be processed computationally. Inner layers only need a square root number of simulation with respect to the most outer layer. Some of the layers exhibit a smaller variance. As a result, with GPUs at least, error-controlled NMC XVA computations are doable. But, although NMC is naively suited to parallelization, a GPU implementation of NMC XVA computations requires various optimizations. This is illustrated on XVA computations involving equities, interest rate, and credit derivatives, for both bilateral and central clearing XVA metrics.

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Cohen, Joshua, Laura Faden, and Kenneth Getz. "Mapping Biopharmaceutical Innovation and Diffusion: How the Second Translational Block (T2) Shapes Drug Diffusion." Open Pharmacology Journal 2, no. 1 (October 10, 2008): 89–106. http://dx.doi.org/10.2174/1874143600802010089.

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In the US, there is a vigorous public debate on the merits of biopharmaceutical innovations and their diffusion. There is virtual unanimity about the importance of maintaining a steady stream of biopharmaceutical innovations, to which patients should have timely access. However, the debate’s participants are cognizant that the effects of innovation and diffusion on health outcomes, health care spending, and incentives for future innovation, must be weighed against one another. First, we performed a Medline literature review to map the innovation diffusion process, combining the search terms “innovation,” “diffusion,” and “pharmaceutical.” Second, we conducted a survey of 190 physicians to examine their valuation of the innovativeness and rate of diffusion of 20 new molecular entities (NMEs). Third, we collected data from the Centers for Medicare and Medicaid Services (CMS) Formulary Finder to assess payers’ valuation of the innovativeness of the 20 NMEs in question. Based on our literature review, we identified the key stakeholders involved in the innovation diffusion process. Furthermore, we highlighted the changing landscape of translational movers and shakers, tracing the emergence of T2 barriers, emanating largely from third party payer formulary management. Our empirical analysis suggests payers are exerting influence on physicians’ prescribing decisions, while the role of patients and pharmaceutical firms has diminished somewhat. Payers directly affect prescribing decisions through the use of formularies, and indirectly by funding evidence-based continuing medical education. On average, across the 20 drugs we sampled, the time from approval to first prescription was 33 months, which indicates a slow diffusion process. Our data analysis shows a gap in perception of innovativeness between physicians and payers, with physicians ranking drugs as more innovative on average than payers. And, our findings suggest the more innovative a drug is perceived by physicians and payers the higher market share it has. Striking an appropriate balance on access to and cost of biopharmaceuticals will require policy adjustments on the part of payers. In cases in which there is a large degree of uncertainty or the fiscal impact is particularly high, coverage could be made subject to a policy of coverage with evidence development (CED). Here, coverage would be conditional on development and capture of outcome data. A CED policy could be combined with a risk-sharing arrangement in which financial risk is shared between payers and the biopharmaceutical industry.

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Naser, N. "The Interaction between Profitability and Macroeconomic Factors for Future Examinations of European Banks Soundness – Theoretical Study." Financial Markets, Institutions and Risks 3, no. 3 (2019): 63–97. http://dx.doi.org/10.21272/fmir.3(3).63-97.2019.

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Any weakness in the financial institution is subject to the contagion mechanism. As result, the whole financial system will experience unpredictable financial risks and possible crisis, such as to a systemically relevant institution (e.g. Lehman Brothers’ default, 2008’s financial crises and Asian financial crises). The contagion mechanism (Quagliariello, M., 2009 [1] (Trapanese, M.) is a crucial element in the assessment of the cross-border dimension. The direct cross-border contagion risks (idiosyncratic risks) are: risks related to cross-border interbank links; money markets and cross-border ownership links; common shocks of foreign economies and global financial markets that can affect banks’ exposures due to changes in credit quality, market valuations and funding costs. Secondly, the indirect cross-border contagion risks (Indirect contagion) are caused by systematics risks that exclusively related to cross-border credit exposures (e.g. lending to non- financial institutions, credit risk transfer exposures as well as international syndicated lending), market risk exposures (by holdings of securities and off-balance sheet positions), common cross-border funding (by financing through market instruments and operational risk). From a theoretical point of view, said institutions are defined as risky banks, have unpredictable impacts on the smoothness of whole financial system. Moreover, these credit, market and liquidity risks represent the main triggers of crises. This paper is the second part of my theoretical study focused on the profitability and the soundness of European banks with an emphasis on the role of the Macro profile. In this paper, I also thoroughly investigated the macroeconomic determinants used to predict the Banking crises. Moreover, this paper analyzed the history and behavior of European banks during financial crises, and the corrective measures taken by authorities, governments and supervisory institutes to bail out the troubled banks, or to support the banking system as a whole. An extensive assessment of collected data resulted in detailed analysis of quantitative methodologies as well as the examination of the effectiveness of selected macroeconomic determinants to avoid the financial instability. This study shows that the macroeconomic adjustments were called upon during the crises. Keywords: bank profitability; soundness of banks, inflation, GDP, interest rates, macroeconomic determinants, Moody’s rating, exchange rates.

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Albanese, Claudio, Simone Caenazzo, and Stephane Crepey. "Capital Valuation Adjustment and Funding Valuation Adjustment." SSRN Electronic Journal, 2016. http://dx.doi.org/10.2139/ssrn.2745909.

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Green, Andrew David, and Chris Kenyon. "Calculating the Funding Valuation Adjustment (FVA) of Value-at-Risk (VAR) Based Initial Margin." SSRN Electronic Journal, 2014. http://dx.doi.org/10.2139/ssrn.2432281.

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Pallavicini, Andrea, Daniele Perini, and Damiano Brigo. "Funding Valuation Adjustment: A Consistent Framework Including CVA, DVA, Collateral, Netting Rules and Re-Hypothecation." SSRN Electronic Journal, 2011. http://dx.doi.org/10.2139/ssrn.1969114.

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Durand, Cyril. "Stabilizing Log-Normal Diffusion in View of Compounding Swaps Funding Risk Credit Valuation Adjustment (FRCVA)." SSRN Electronic Journal, 2015. http://dx.doi.org/10.2139/ssrn.2684392.

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Castagna, Antonio. "Funding Valuation Adjustment (FVA) and Theory of the Firm: A Theoretical Justification of the Inclusion of Funding Costs in the Evaluation of Financial Contracts." SSRN Electronic Journal, 2013. http://dx.doi.org/10.2139/ssrn.2278595.

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Fries, Christian P., and Mark Lichtner. "Collateralization and Funding Valuation Adjustments (FVA) for Total Return Swaps." SSRN Electronic Journal, 2014. http://dx.doi.org/10.2139/ssrn.2444452.

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Albanese, Claudio, Simone Caenazzo, and Stéphane Crépey. "Credit, funding, margin, and capital valuation adjustments for bilateral portfolios." Probability, Uncertainty and Quantitative Risk 2, no. 1 (June 26, 2017). http://dx.doi.org/10.1186/s41546-017-0019-2.

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Pallavicini, Andrea, Daniele Perini, and Damiano Brigo. "Funding, Collateral and Hedging: Uncovering the Mechanics and the Subtleties of Funding Valuation Adjustments." SSRN Electronic Journal, 2012. http://dx.doi.org/10.2139/ssrn.2161528.

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Journal articles on the topic 'Funding Valuation Adjustment'

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