IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v62y2022ics106294082200122x.html
   My bibliography  Save this article

Sensitivity-based Conditional Value at Risk (SCVaR): An efficient measurement of credit exposure for options

Author

Listed:
  • Shi, Ruoshi
  • Zhao, Yanlong
  • Bao, Ying
  • Peng, Cheng

Abstract

Counterparty Credit Risk (CCR) has received extensive attention in the Over-The-Counter (OTC) derivative markets. This paper proposes a credit risk exposure measurement for European options: Sensitivity-based Conditional Value at Risk (SCVaR), which can cover the future credit risk by a stable sensitivity weight, and improve the accuracy of risk tracking in most cases. Compared with VaR and CVaR, SCVaR has superiority in extensibility, computational efficiency and stability. We further derive the tendency and upper bound of sensitivity weights, consequently obtaining a practical value of price weight for long-term stability. The simulation and empirical analysis in the Chinese options market also show good applicability of SCVaR. The risk exposures are efficiently covered during periods of fluctuation, which alleviates the procyclicality to some extent. These results provide a useful guidance for the development of financial risk management.

Suggested Citation

  • Shi, Ruoshi & Zhao, Yanlong & Bao, Ying & Peng, Cheng, 2022. "Sensitivity-based Conditional Value at Risk (SCVaR): An efficient measurement of credit exposure for options," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:ecofin:v:62:y:2022:i:c:s106294082200122x
    DOI: 10.1016/j.najef.2022.101781
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S106294082200122X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.najef.2022.101781?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. James B. Bullard & Christopher J. Neely & David C. Wheelock, 2009. "Systemic risk and the financial crisis: a primer," Review, Federal Reserve Bank of St. Louis, vol. 91(Sep), pages 403-418.
    2. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    3. Li, Shuang & Peng, Cheng & Bao, Ying & Zhao, Yanlong, 2020. "Explicit expressions to counterparty credit exposures for Forward and European Option," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    4. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    5. Peng, Cheng & Li, Shuang & Zhao, Yanlong & Bao, Ying, 2021. "Sample average approximation of CVaR-based hedging problem with a deep-learning solution," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    6. Patrik Karlsson & Shashi Jain & Cornelis W. Oosterlee, 2016. "Counterparty Credit Exposures for Interest Rate Derivatives using the Stochastic Grid Bundling Method," Applied Mathematical Finance, Taylor & Francis Journals, vol. 23(3), pages 175-196, May.
    7. Santa-Clara, Pedro & Saretto, Alessio, 2009. "Option strategies: Good deals and margin calls," Journal of Financial Markets, Elsevier, vol. 12(3), pages 391-417, August.
    8. Mr. Manmohan Singh, 2010. "Collateral, Netting and Systemic Risk in the OTC Derivatives Market," IMF Working Papers 2010/099, International Monetary Fund.
    9. Ingves, S., 2013. "Regulatory reforms for OTC derivatives: past, present and future," Financial Stability Review, Banque de France, issue 17, pages 19-28, April.
    10. Murphy, David & Vasios, Michalis & Vause, Nick, 2014. "Financial Stability Paper No 29: An investigation into the procyclicality of risk-based initial margin models," Bank of England Financial Stability Papers 29, Bank of England.
    11. Alexander, S. & Coleman, T.F. & Li, Y., 2006. "Minimizing CVaR and VaR for a portfolio of derivatives," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 583-605, February.
    12. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    13. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    14. Carole Bernard & Ludger Rüschendorf & Steven Vanduffel & Jing Yao, 2017. "How robust is the value-at-risk of credit risk portfolios?," The European Journal of Finance, Taylor & Francis Journals, vol. 23(6), pages 507-534, May.
    15. Jain, Shashi & Oosterlee, Cornelis W., 2015. "The Stochastic Grid Bundling Method: Efficient pricing of Bermudan options and their Greeks," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 412-431.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhu, Shushang & Zhu, Wei & Pei, Xi & Cui, Xueting, 2020. "Hedging crash risk in optimal portfolio selection," Journal of Banking & Finance, Elsevier, vol. 119(C).
    2. Sofiane Aboura, 2014. "When the U.S. Stock Market Becomes Extreme?," Risks, MDPI, vol. 2(2), pages 1-15, May.
    3. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    4. Jay Cao & Jacky Chen & John Hull & Zissis Poulos, 2021. "Deep Hedging of Derivatives Using Reinforcement Learning," Papers 2103.16409, arXiv.org.
    5. Kathrin Glau & Ricardo Pachon & Christian Potz, 2019. "Speed-up credit exposure calculations for pricing and risk management," Papers 1912.01280, arXiv.org.
    6. Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
    7. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    8. H. Kaibuchi & Y. Kawasaki & G. Stupfler, 2022. "GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 22(7), pages 1277-1294, July.
    9. Heller, Yuval & Schreiber, Amnon, 2020. "Short-term investments and indices of risk," Theoretical Economics, Econometric Society, vol. 15(3), July.
    10. Ra l De Jes s Guti rrez & Lidia E. Carvajal Guti rrez & Oswaldo Garcia Salgado, 2023. "Value at Risk and Expected Shortfall Estimation for Mexico s Isthmus Crude Oil Using Long-Memory GARCH-EVT Combined Approaches," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 467-480, July.
    11. Somayeh Moazeni & Thomas F. Coleman & Yuying Li, 2016. "Smoothing and parametric rules for stochastic mean-CVaR optimal execution strategy," Annals of Operations Research, Springer, vol. 237(1), pages 99-120, February.
    12. Anindya Goswami & Nimit Rana, 2024. "A market resilient data-driven approach to option pricing," Papers 2409.08205, arXiv.org.
    13. Borowska, Agnieszka & Hoogerheide, Lennart & Koopman, Siem Jan & van Dijk, Herman K., 2020. "Partially censored posterior for robust and efficient risk evaluation," Journal of Econometrics, Elsevier, vol. 217(2), pages 335-355.
    14. Dingshi Tian & Zongwu Cai & Ying Fang, 2018. "Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201807, University of Kansas, Department of Economics, revised Oct 2018.
    15. Lazar, Emese & Zhang, Ning, 2019. "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 74-93.
    16. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    17. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    18. Patrice Gaillardetz & Saeb Hachem, 2019. "Risk-Control Strategies," Papers 1908.02228, arXiv.org.
    19. Jinglun Yao & Sabine Laurent & Brice B'enaben, 2017. "Managing Volatility Risk: An Application of Karhunen-Lo\`eve Decomposition and Filtered Historical Simulation," Papers 1710.00859, arXiv.org.
    20. Boris Ter-Avanesov & Homayoon Beigi, 2024. "MLP, XGBoost, KAN, TDNN, and LSTM-GRU Hybrid RNN with Attention for SPX and NDX European Call Option Pricing," Papers 2409.06724, arXiv.org, revised Oct 2024.

    More about this item

    Keywords

    Counterparty credit exposure; VaR; CVaR; Sensitivity; Greeks;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G01 - Financial Economics - - General - - - Financial Crises
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecofin:v:62:y:2022:i:c:s106294082200122x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620163 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.