Narrow Results

This paper considers the valuation problem of basket CDSs. Based on the construction of total hazard rates, the paper develops the work of Zheng and Jiang Zheng and Jiang (2009) from the homogenous case to the primary-subsidiary heterogenous case in the interacting intensity framework, and obtains the corresponding joint density of the default time. Moreover, the paper derives the valuation formulae for the basket CDSs with and without counterparty risk. Numerical results robustly show that, under certain conditions, using the analytical pricing formulae derived in this paper is more efficient than the Monte Carlo method for the basket CDS valuation.

We consider counterparty risk for Credit Default Swaps (CDS) in presence of correlation between default of the counterparty and default of the CDS reference credit. Our approach is innovative in that, besides default correlation, which was taken into account in earlier approaches, we also model credit spread volatility. Stochastic intensity models are adopted for the default events, and defaults are connected through a copula function. We find that both default correlation and credit spread volatility have a relevant impact on the positive counterparty-risk credit valuation adjustment to be subtracted from the counterparty-risk free price. We analyze the pattern of such impacts as correlation and volatility change through some fundamental numerical examples, analyzing wrong-way risk in particular. Given the theoretical equivalence of the credit valuation adjustment with a contingent CDS, we are also proposing a methodology for valuation of contingent CDS on CDS.

The purpose of this paper is introducing rigorous methods and formulas for bilateral counterparty risk credit valuation adjustment (CVA) on interest-rate portfolios. In doing so, we summarize the general arbitrage-free valuation framework for counterparty risk adjustments in presence of bilateral default risk, including the default of the investor. We illustrate the symmetry in the valuation and show that the adjustment involves a long position in a put option plus a short position in a call option, both with zero strike and written on the residual net present value of the contract at the relevant default times. We allow for correlation between the default times of the investor and counterparty, and for correlation of each with the underlying risk factor, namely interest rates. We also analyze the often neglected impact of credit spread volatility. We include close-out netting rules in our examples, although other agreements, such as periodic margining or collateral posting, are left for future work.

In this paper, the counterparty risk is considered in pricing a Credit Default Swap (abbr. CDS). We adopt an intensity-based reduced form model, in which the default intensity processes of the counterpart and the reference credit are modulated by the credit states of the firms. Two Markov chains are used to describe the credit state processes. We set up a model where the default correlation between the counterpart and the reference is described through the Markov chains. A semi-explicit formula for the pricing of CDS with counterparty risk is obtained. We analyze the impacts of default correlations and the state changes on the CDS price through some numerical experiments.

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.

This article is concerned with the arbitrage-free valuation of bilateral counterparty risk through stochastic dynamical models when collateral is included, with possible rehypothecation. The payout of claims is modified to account for collateral margining in agreement with International Swap and Derivatives Association (ISDA) documentation. The analysis is specialized to interest-rate and credit derivatives. In particular, credit default swaps are considered to show that a perfect collateralization cannot be achieved under default correlation. Interest rate and credit spread volatilities are fully accounted for, as is the impact of re-hypothecation, collateral margining frequency, and dependencies.

In order to dynamize the static Gaussian copula model of portfolio credit risk, we introduce a model filtration made of a reference Brownian filtration progressively enlarged by the default times. This yields a multidimensional density model of default times, where, as opposed to the classical situation of the Cox model, the reference filtration is not immersed into the enlarged filtration. In mathematical terms this lack of immersion means that martingales in the reference filtration are not martingales in the enlarged filtration. From the point of view of financial interpretation this means default contagion, a good feature in the perspective of modeling counterparty wrong-way risk on credit derivatives. Computational tractability is ensured by invariance of multivariate Gaussian distributions through conditioning by some components, the ones corresponding to past defaults. Moreover the model is Markov in an augmented state-space including past default times. After a discussion of different notions of deltas, the model is applied to the valuation of counterparty risk on credit derivatives.

In this paper we discuss the issue of computation of the bilateral credit valuation adjustment (CVA) under rating triggers, and in presence of ratings-linked margin agreements. Specifically, we consider collateralized OTC contracts, that are subject to rating triggers, between two parties — an investor and a counterparty. Moreover, we model the margin process as a functional of the credit ratings of the counterparty and the investor. We employ a Markovian approach for modeling of the rating transitions of the two parties to the contract. In this framework, we derive the representation for bilateral CVA. We also introduce a new component in the decomposition of the counterparty risky price: namely the rating valuation adjustment (RVA) that accounts for the rating triggers. We give two examples of dynamic collateralization schemes where the margin thresholds are linked to the credit ratings of the parties. Our results are illustrated via computation of various counterparty risk adjustments for a CDS contract and for an IRS contract.

We introduce an innovative theoretical framework for the valuation and replication of derivative transactions between defaultable entities based on the principle of arbitrage freedom. Our framework extends the traditional formulations based on credit and debit valuation adjustments (CVA and DVA). Depending on how the default contingency is accounted for, we list a total of ten different structuring styles. These include bi-partite structures between a bank and a counterparty, tri-partite structures with one margin lender in addition, quadri-partite structures with two margin lenders and, most importantly, configurations where all derivative transactions are cleared through a central counterparty clearing house (CCP). We compare the various structuring styles under a number of criteria including consistency from an accounting standpoint, counterparty risk hedgeability, numerical complexity, transaction portability upon default, induced behavior and macro-economic impact of the implied wealth allocation.

We propose a fairly general framework which allows one to perform Credit Value Adjustment (CVA) computations for a contract with bilateral counterparty risk in the presence of (a) systemic risk and (b) wrong-way or right-way risks. Our methodology focuses on the role of alternative settlement clauses, but it also aims to cover various features of margin agreements. We present a comparative analysis of numerical results that supports our initial conjecture that alternative specifications of settlement values have a nonnegligible impact on CVA computations for contracts with bilateral counterparty risk. Our conclusions emphasize the practical importance of more sophisticated models that are capable of fully reflecting the actual features of financial contracts, as well as the influence of the market environment.

We analyze the general risk-neutral valuation for counterparty risk embedded in a Credit Default Swap (CDS) contract by adapting the recent findings of Brigo and Capponi (2009) to allow for simultaneous defaults among the two parties and the underlying reference credit, while the counterparty risk is considered bilaterally. For the default intensities, we employ a Markov copula model allowing for the possibility of a simultaneous default. The dependence between defaults of three names in a CDS contract and the wrong-way risk will thus be represented by the possibility of simultaneous defaults.

We investigate numerically the effect of considering simultaneous defaults on the counterparty risk valuation of a CDS contract. Finally, we study a CDS contract between Royal Dutch Shell and British Airways based on Lehman Brothers applying this methodology, illustrating the bilateral adjustments with the possibility of simultaneous defaults in concrete crisis situations.

We propose a Credit Value Adjustment (CVA) model capturing the Wrong Way Risk (WWR) that is not product-specific and is suitable for large-scale computations. The model is based on a doubly stochastic default process with the default intensities proxied by credit spreads. For different exposure structures, we show how credit–market correlation affects the CVA level, its sensitivities to credit and market factors, its volatility and the quality of hedging. The WWR is most significant for exposures highly sensitive to the market volatility in a situation when credit spreads are at moderate levels but both the market factors and credit spreads are volatile. In such conditions, ignoring credit–market correlations results in important CVA mispricing. While the benefits from hedging are always magnified in the situation of the WWR, the right way exposure case is more delicate: only a well-designed mix of credit and market hedges can bring volatility down. Our results raise doubts on the Basel III policy of recognizing credit but not market hedges for computing the CVA volatility capital charge.

We study a credit risk model of a financial market in which the dynamics of intensity rates of two default times are described by linear combinations of three independent geometric Brownian motions. The dynamics of two default-free risky asset prices are modeled by two geometric Brownian motions which are dependent of the ones describing the default intensity rates. We obtain closed form expressions for the no-arbitrage prices of both risk-free and risky credit default swaps given the reference filtration initially and progressively enlarged by the two default times. The accessible default-free reference filtration is generated by the standard Brownian motions driving the model.

In this paper, we offer a network model that derives the expected counterparty risk of an arbitrary market after netting in a closed-form expression. Graph theory is used to represent market participants and their relationship among each other. We apply the powerful theory of characteristic functions (c.f.) and Hilbert transforms to determine the expected counterparty risk. The latter concept is used to express the c.f. of the random variable (r.v.) $max(Y,0)$ in terms of the c.f. of the r.v. $Y$. This paper applies this concept for the first time in mathematical finance in order to generalize results of Duffie & Zhu (2011), in several ways. The introduced network model is applied to study the features of an over-the-counter and a centrally cleared market. We also give a more general answer to the question of whether it is more advantageous for the overall counterparty risk to clear via a central counterparty or classically bilateral between the two involved counterparties.

In this study, we develop an equilibrium pricing model for an option contract with a counterparty risk, a collateral agreement, a counterparty risk constraint, and a threshold. Since we consider the option market to be an example of the derivatives market, we suppose that the buyer of an option has only countertparty risk of a seller defaulting. In addition, we consider a model where the buyer is allowed to enter into an option contract within an allocated amount of risk capital for counterparty risk, and requires (cash) collateral to the seller if the exposure exceeds the threshold. The counterparty risk is measured as a credit valuation adjustment. We provide an equilibrium pricing rule and an equilibrium volume formula by solving participants’ static utility-maximization problems. Based on numerical simulations, we verify the mechanisms through which collateralization, risk capital, and the threshold affect the size of the over-the-counter (OTC) option market. Finally, we analyze the influence of the buyer’s risk-aversion on the market, without collateralization. The results imply that the risk constraint might be a proxy for an investor’s attitude towards risk.

We examine credit value adjustment (CVA) estimation under wrong-way risk (WWR) by computing the expected positive exposure (EPE) under an equivalent measure as suggested in [1], adjusting the drift of the underlying for default risk. We apply this technique to European put and call options and derive the analytic formulas for EPE under WWR obtained with various approximations of the drift adjustment. We give the results of numerical experiments based on 4 parameter sets, and supply figures of the CVA based on both of the suggested proxys, comparing with CVA based on a 2D-Monte Carlo scheme and Gaussian Copula resampling. We also show the CVA obtained by the formulas from Basel III. We observe that the Basel III formula does not account for the credit-market correlation, while the Gaussian Copula resampling method estimates a too large impact of this correlation. The two proxies account for the credit-market correlation, and give results that are mostly similar to the 2D-Monte Carlo results.

Foreign exchange (FX) is the largest of the financial markets. Currency futures, the first of the financial futures contracts listed on an organized exchange, while not constituting a particularly large portion of global FX volume (around 5%), have seen their market share growing — due to their transparency, their counterparty risk remediation, and being the beneficiaries of the clearing that exchanges provide. Currency futures pricing is based on the same driving factors as over-the-counter (OTC) FX forward pricing: the spot price, the maturity (or time horizon), and the relevant interest rates for the two currencies involved. Many FX futures are physically settled, but some (including all those on non-deliverable currencies) are cash settled. There are also listed FX options (which primarily settle into FX futures). The year-on-year growth in some of these exchange-traded currency derivatives has been rather spectacular due to the ever expanding global FX trading volume, the leverage which futures provide, the popularity of this product area for high frequency and algorithmic trading firms — as well as global macro hedge funds, and the recognition (on the part of the investment community generally — and retail clients in particular) of the benefits of FX as an asset class.