XVA modelling: validation, performance and model risk management

Lorenzo Silotto1, Marco Scaringi2, Marco Bianchetti2,3
1Deloitte Consulting, Milan, Italy
2Financial and Market Risk Management, Intesa Sanpaolo, Milan, Italy
3Department of Statistical Sciences “Paolo Fortunati”, University of Bologna, Bologna, Italy

Tóm tắt

Valuation adjustments, collectively named XVA, play an important role in modern derivatives pricing to take into account additional price components such as counterparty and funding risk premia. They are an exotic price component carrying a significant model risk and computational effort even for vanilla trades. We adopt an industry-standard realistic and complete XVA modelling framework, typically used by XVA trading desks, based on multi-curve time-dependent volatility G2++ stochastic dynamics calibrated on real market data, and a multi-step Monte Carlo simulation including both variation and initial margins. We apply this framework to the most common linear and non-linear interest rates derivatives, also comparing the MC results with XVA analytical formulas. Within this framework, we identify the most relevant model risk sources affecting the precision of XVA figures and we measure the corresponding computational effort. In particular, we show how to build a parsimonious and efficient MC time simulation grid able to capture the spikes arising in collateralized exposure during the margin period of risk. As a consequence, we also show how to tune accuracy versus performance, leading to sufficiently robust XVA figures in a reasonable time, a very important feature for practical applications. Furthermore, we provide a quantification of the XVA model risk stemming from the existence of a range of different parameterizations according to the EU prudent valuation regulation. Finally, this work also serves as an handbook containing step-by-step instructions for the implementation of a complete, realistic and robust modelling framework of collateralized exposure and XVA.

Tài liệu tham khảo

Ametrano, F. M., & Bianchetti, M. (2009). Smooth yield curves bootstrapping for forward libor rate estimation and pricing interest rate derivatives. In: Modelling interest rates: Latest advances for derivatives pricing risk books. Ametrano, F. M., & Bianchetti, M. (2013). Everything you always wanted to know about multiple interest rate curve bootstrapping but were afraid to ask. Available at SSRN 2219548. Andersen, L. B., Pykhtin, M., & Sokol, A. (2016). Credit exposure in the presence of initial margin. Available at SSRN 2806156. Andersen, L. B., Pykhtin, M., & Sokol, A. (2017). Rethinking the margin period of risk. Journal of Credit Risk 13(1). Anfuso, F., Aziz, D., & Giltinan, P., et al. (2017). A sound modelling and backtesting framework for forecasting initial margin requirements. Available at SSRN 2716279. Atanassov, E., & Kucherenko, S. (2020). Implementation of Owen’s scrambling with additional permutations for Sobol’ sequences. Basel Committee on Banking Supervision and Board of the International Organization of Securities Commissions. (2013). Second consultative document, margin requirements for non-centrally cleared derivatives. Bank for International Settlements. Basel Committee on Banking Supervision and Board of the International Organization of Securities Commissions. (2015). Margin requirements for non-centrally cleared derivatives. Bank for International Settlements. Bianchetti, M. (2010). Two curves, one price. Risk, 23(8), 66. Bielecki, T., & Rutkowski, M. (2004). Credit risk: Modeling, valuation and hedging. Berlin: Springer. Bormetti, G., Brigo, D., Francischello, M., & Pallavicini, A. (2018). Impact of multiple curve dynamics in credit valuation adjustments under collateralization. Quantitative Finance, 18(1), 31–44. Brigo, D., Buescu, C., & Francischello, M., et al. (2018). Risk-neutral valuation under differential funding costs, defaults and collateralization. Defaults and Collateralization (February 28, 2018). Brigo, D., Capponi, A., & Pallavicini, A. (2014). Arbitrage-free bilateral counterparty risk valuation under collateralization and application to credit default swaps. Mathematical Finance: An International Journal of Mathematics, Statistics and Financial Economics, 24(1), 125–146. Brigo, D., Francischello, M., & Pallavicini, A. (2019). Nonlinear valuation under credit, funding, and margins: Existence, uniqueness, invariance, and disentanglement. European Journal of Operational Research, 274(2), 788–805. Brigo, D., & Masetti, M. (2005). Risk neutral pricing of counterparty risk. Brigo, D., & Mercurio, F. (2007). Interest rate models-theory and practice: With smile, inflation and credit. Berlin: Springer. Brigo, D., Morini, M., & Pallavicini, A. (2013). Counterparty credit risk, collateral and funding: With pricing cases for all asset classes (Vol. 478). Hoboken: Wiley. Brigo, D., Pallavicini, A., & Papatheodorou, V. (2011). Arbitrage-free valuation of bilateral counterparty risk for interest-rate products: Impact of volatilities and correlations. International Journal of Theoretical and Applied Finance, 14(06), 773–802. Burgard, C., & Kjaer, M. (2011). In the balance. In C Burgard, M Kjaer In the balance, Risk, November (pp. 72–75). Capriotti, L., & Giles, M. (2012). Adjoint Greeks made easy. Risk, 25(9), 92. Caspers, P., Giltinan, P., Lichters, R., et al. (2017). Forecasting initial margin requirements: A model evaluation. Journal of Risk Management in Financial Institutions, 10(4), 365–394. Crépey, S., & Dixon, M. (2020). Gaussian process regression for derivative portfolio modeling and application to CVA computations. Journal of Computational Finance, 24, 47–81. European Commission. (2016). Commission Delegated Regulation (EU) 2016/101 of 26 October 2015 supplementing Regulation (EU) No 575/2013 of the European Parliament and of the Council with regard to regulatory technical standards for prudent valuation under Article 105(14). Official Journal of the European Union. European Parliament and Council of the European Union. (2013). Regulation (EU) No 575/2013 of the European Parliament and of the Council of 26 June 2013 on prudential requirements for credit institutions and investment firms and amending Regulation (EU) No 648/2012. Official Journal of the European Union. Glasserman, P., & Xu, X. (2014). Robust risk measurement and model risk. Quantitative Finance, 14(1), 29–58. Green, A. (2015). XVA: Credit, funding and capital valuation adjustments. Hoboken: Wiley. Green, A., & Kenyon, C. (2015). MVA: Initial margin valuation adjustment by replication and regression. Available at SSRN 2432281. Green, A., Kenyon, C., & Dennis, C. (2014). KVA: Capital valuation adjustment. Risk. Gregory, J. (2016). The impact of initial margin. Available at SSRN 2790227. Gregory, J. (2020). The XVA challenge: Counterparty risk, funding, collateral, capital and initial margin. Hoboken: Wiley. Hagan, P. S., Kumar, D., Woodward, A. S., & Lesniewski, D. E. (2002). Managing smile risk. The Best of Wilmott, 1, 249–296. Hagan, P. S., Kumar, D., Woodward, A. S., & Lesniewski, D. E. (2016). Universal smiles. Wilmott, 84, 40–55. Henrard, M. (2007). The irony in the derivatives discounting. Wilmott 92–98. Henrard, M. (2009). The irony in the derivatives discounting part II: The crisis. Wilmott, 2(6), 301–316. Huge, B., & Savine, A. (2020). Differential machine learning: The shape of things to come. Risk (10). International Accounting Standards Board. (2011). International financial reporting standard 13—Fair value measurement. International Swaps and Derivatives Association. (2013). Standard initial margin model for non-cleared derivatives. International Swaps and Derivatives Association. (2016). ISDA SIMM: From Principles to Model Specification. International Swaps and Derivatives Association. (2018). ISDA SIMM. Methodology, version 2.1. Kenyon, C. (2010). Short-rate pricing after the liquidity and credit shocks: including the basis. Risk, November. Kjaer, M. (2018). KVA Unmasked. Available at SSRN 3143875. Konikov, M., & McClelland, A. (2019). Multi-curve Cheyette-style models with lower bounds on tenor basis spreads. Available at SSRN 3524703. Maran, A., Pallavicini, A., & Scoleri, S. (2022). Chebyshev Greeks: Smoothing gamma without bias. Risk, November. Mercurio, F. (2009). Post credit crunch interest rates: Formulas and market models. Bloomberg portfolio research paper 2010-01. Morini, M. (2009). Solving the puzzle in the interest rate market. Available at SSRN 1506046. Morini, M., & Prampolini, A. (2011). Risky funding with counterparty and liquidity charges. Risk, 24(3), 70. Obloj, D. (2007). Fine-tune your smile: Correction to Hagan et al. Arxiv. Pallavicini, A., Perini, D., & Brigo, D. (2012). Funding, collateral and hedging: Uncovering the mechanics and the subtleties of funding valuation adjustments. arXiv:1210.3811. Piterbarg, V. V. (2012). Cooking with collateral. Risk, 25(8), 46. Scaringi, M., & Bianchetti, M. (2020). No fear of discounting-how to manage the transition from EONIA to €STR. Available at SSRN 3674249. Scoleri, S., Bianchetti, M., & Kucherenko, S. (2021). Application of quasi Monte Carlo and global sensitivity analysis to option pricing and Greeks: Finite differences vs. AAD. Wilmott, 2021, 66–83. Zeron, M., & Ruiz, I. (2018). Dynamic initial margin via Chebyshev spectral decomposition. Working paper (24 August).