Counterparty Credit Research Articles

Implementing Contract-for-Difference Arrangements for Hedging Electricity Price Risks of Renewable Generators on a Blockchain Marketplace

The dynamic nature of competitive electricity markets means that participants often resort to some form of derivative financial instrument. One such instrument is a contract-for-difference (CFD), usually available to renewable generators in certain electricity markets to enable them to hedge their price risk. Embracing CFD presents new risks such as counterparty credit, margining, third-party, legal, and process risks. Derivative instruments existing on blockchains have recently demonstrated potential as suitable hedging tools for minimizing the risks of renewable generators. This article applies this concept for the first time to hedge the price risk of renewable generators by implementing a novel decentralized finance instrument, an Ethereum blockchain marketplace governed by a smart contract to mediate between stakeholders mutually enrolled in bilateral CFD arrangements. The employed structure mitigates the underlying risks of traditional arrangements, underpinned by a suite of autonomous mechanisms.

The cost of hedging XVA

We generalise the results of Burnett (2021) to obtain a formula for the Hedging Valuation Adjustment (HVA) that is consistent with other XVAs. This HVA incorporates friction effects due to hedging both the underlying asset and counterparty credit, along with a contribution from hedge unwinds upon counterparty default. We show numerical results for a simple case, which demonstrate that in a realistic situation the HVA can be on the same scale as more classical XVAs.

CoMap: Mapping Contagion in the Euro Area Banking Sector

This paper presents a novel approach to investigate and model the network of euro area banks’ large exposures within the global banking system. Drawing on a unique dataset, the paper, for the first time, documents the degree of interconnectedness and systemic risk of the euro area banking system based on bilateral linkages. We then develop a Contagion Mapping (CoMap) methodology to study contagion potential of an exogenous default shock via counterparty credit and funding risks. We construct contagion and vulnerability indices measuring respectively the systemic importance of banks and their degree of fragility. Decomposing the results into the respective contributions of credit and funding shocks provides insights to the nature of contagion which can be used to calibrate bank-specific capital and liquidity requirements and large exposures limits. We empirically confirm that tipping points shifting the euro area banking system from a less vulnerable state to a highly vulnerable state are a non-linear function of the combination of network structures and bank-specific characteristics.

The Valuation of Financial Derivatives Subject to Counterparty Risk and Credit Value Adjustment

The Valuation of Financial Derivatives Subject to Counterparty Risk and Credit Value Adjustment

BASEL III Counterparty Risk and Credit Value Adjustment: Impact of the Wrong-way Risk

During the financial crisis of 2007 through 2010, BIS estimated that two-thirds of the loss of the financial institutions was due to credit valuation adjustment (CVA). The CVA is the marked-to-market price of credit products which involve counterparties. Hence, in BASEL III, banks are required to capture and allocate CVA capital and wrong-way risk (WWR). In this paper, we show that BASEL III CVA capital charge increases as the credit rating of the counterparty deteriorates. We also show that the extreme WWR can increase the BASEL III CVA charge by 30 times in Davis and Pistorius model.

Optimal Monetary Policy, the Zero Bound and the Term Structure of Interest Rates

Optimal Monetary Policy, the Zero Bound and the Term Structure of Interest Rates

Algorithmic Exposure and CVA for Exotic Derivatives

We develop the algorithmic approach for Counterparty exposure calculation and automate its application to arbitrary complicated instruments. Assuming that the portfolio is priced by the backward (American) Monte-Carlo method, our approach allows calculating the credit exposure as a pricing by-product, essentially without modifications in the usual pricing procedure.In particular, for the exposure calculation of callable instruments we manage to avoid a cumbersome aggregation of exercise indicators, applying them sequentially in parallel with the main pricing.We explain how the obtained exposure can be integrated into the Credit Valuation Adjustment (CVA), based on the extension of the pricing model with a Counterparty credit component.The presented approach to the exposure computation is formulated in an arbitrary probability measure. To perform the measure change we use the cross-currency model semantics and calibrate the model to the real-world measure using indexes projections.

A Structural Model with Counterparty Risks

The recent financial crisis has triggered more studies on counterparty risks. The theoretical research on credit risk with counterparty risks has been built based upon the reduced-form model. In contrast, this paper suggests a structural model where firm value can be reduced due to counterparty risks. After deriving a price formula for corporate bonds, we analyze the credit spreads of the corporate bonds. The effects of the counterparty risk on credit spreads are as follows: First, regardless of the level of the counterparty's credit rating, the credit spreads of a firm increase because of counterparty risks. Second, the lower the counterparty's credit rating, the stronger the impact of either the correlation between the two firms on credit spreads, or the coefficient of reduction in firm value due to counterparty risks on credit spreads. Third, compared with existing structural models, there are some cases in which the structural model with counterparty risks is more consistent with actual credit spreads. These cases depend upon the counterparty's credit rating.

Pricing Counterparty Risk at the Trade Level and CVA Allocations

We address the problem of allocating the counterparty-level credit valuation adjustment (CVA) to the individual trades composing the portfolio. We show that this problem can be reduced to calculating contributions of the trades to the counterparty-level expected exposure (EE) conditional on the counterparty's default. We propose a methodology for calculating conditional EE contributions for both collateralized and non-collateralized counterparties. Calculation of EE contributions can be easily incorporated into exposure simulation processes that already exist in a financial institution. We also derive closed-form expressions for EE contributions under the assumption that trade values are normally distributed. Analytical results are obtained for the case when the trade values and the counterparty's credit quality are independent as well as when there is a dependence between them (wrong-way risk).

The effect of systematic risk factors on counterparty default and credit risk of interest rate swaps

This research investigates the effect of specific systematic risk factors on credit risk pricing and capital allocation of interest rate swaps. Because of the stochastic nature of uncertain future cash flows and interest rates, practitioners typically employ the Black-Scholes option pricing model in combination with a simulation analysis to establish capital requirements and estimate the shadow price of an interest rate swap. However, this practice of pricing swap risk excludes systematic risk factors that affect the risk shadow price, thereby underestimating the capital allocation required for financial institutions. This research demonstrates the effect of risk mispricing when simulation models ignore systematic risk factors such as model risk, convexity risk, and parameter risk on the pricing of interest rate swaps.