Abstract
Blockchain is a decentralized infrastructure that has attracted more and more attention from financial institutions due to its irreplaceable advantages. We implemented a blockchain solution for interest rate swap based on the Corda platform. Based on Andersen et al. [8], we derive a risk estimation model for blockchain empowered interest rate swap trading. We conjecture that most of problems in today’s derivative markets could potentially be relieved. For example, through our numerical experiment, we find that with blockchain, both the expected risk exposure and dynamic initial margin decrease significantly, which reduces the risk in interest rate swap trading and increases market liquidity. At the same time, we expect the Effective Expected Positive Exposure(EEPE) in the Basel III standard to decrease. Next, we plan to conduct more mathematical and numerical analysis and continue working on improving our blockchain based trading implementation and risk management model.
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References
Nakamoto, S.: Bitcoin: a peer-to peer electronic cash system. https://bitcoin.org/bitcoin.pdf
Brown, R.G., Carlyle, J., Grigg, I., et al.: Corda: An introduction. R3 CEV, August 2016
Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media Inc., USA (2015)
Bicksler, J., Chen, A.H.: An economic analysis of interest rate swaps. J. Financ. 41, 645–655 (1986). https://doi.org/10.1111/j.1540-6261.1986.tb04527.x
Brigo, D., Mercurio, F.: Interest Rate Models-Theory and Practice: with Smile, Inflation and Credit. Springer Finance. Springer Science & Business Media, Heidelberg (2007). https://doi.org/10.1007/978-3-540-34604-3
Wall, L.D.: Interest rate swaps in an agency theoretic model with uncertain interest rates. J. Bank. Financ. 13(2), 261–270 (1989)
BIS statistics Explorer: Table D7. https://stats.bis.org/statx/srs/table/d7. Accessed 01 Oct 2018
Andersen, L.B.G.. Pykhtin, M., Sokol, A.: Rethinking Margin Period of Risk (2016). https://ssrn.com/abstract=2719964
Clack, B., Braine, L.: Smart Contract Templates: foundations, design landscape and research directions (2016). http://arxiv.org/abs/1608.00771
Mosler, W.B., McCauley, W.P., Sherman, J.M.: Method, system, and computer program product for trading interest rate swaps. Patent 6,304,858. 2001-10-16, U.S
Bott, J.: Central bank money and blockchain: a payments perspective. J. Paym. Strategy Syst. 11(2), 145–157 (2017)
Biryukov, A., Khovratovich, D., Tikhomirov, S.: Findel: secure derivative contracts for Ethereum. In: Brenner, M., et al. (eds.) FC 2017. LNCS, vol. 10323, pp. 453–467. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70278-0_28
Pennington, W.: The collateral-linked currency forward (CLCF) contract: blockchain-enabled OTC currency forward market infrastructure. J. Index Invest. 9(2), 27–33 (2018)
Grinblatt, M.: An analytic solution for interest rate swap spreads. Int. Rev. Financ. 2(3), 113–149 (2001)
Duffie, D., Singleton, K.J.: An econometric model of the term structure of interest-rate swap yields. J. Financ. 52(4), 1287–1321 (1997)
Brown, K.C., Van Harlow, W., Smith, D.J.: An empirical analysis of interest rate swap spread. Boston University, School of Management (1991)
Liu, J., Longstaff, F.A., Mandell, R.E.: The market price of credit risk: An empirical analysis of interest rate swap spreads. National Bureau of Economic Research (2002)
Sun, T.S., Sundaresan, S., Wang, C.: Interest rate swaps: An empirical investigation. J. Financ. Econ. 34(1), 77–99 (1993)
Eom, Y.H., Subrahmanyam, M.G., Uno. J.: Credit risk and the yen interest rate swap market (2000)
Minton, B.A.: An empirical examination of basic valuation models for plain vanilla US interest rate swaps. J. Financ. Econ. 44(2), 251 (1997)
Liangyu.: China Focus: China launches first Internet court in e-commerce hub. Xinhua. https://www.xinhuanet.com//english/2017-08-18/c-136537234.htm
Glasserman, P.: Monte Carlo Methods in Financial Engineering. Stochastic Modelling and Applied Probability. Springer-Verlag, New York (2004). https://doi.org/10.1007/978-0-387-21617-1
Gibson, M.: Measuring counterparty credit exposure to a margined counterparty. In: Pykhtin, M. (ed.) Counterparty Credit Risk Modelling. Risk Books, London (2005)
Hull, J., White, A.: Numerical procedures for implementing term structure models i: single factor models. J. Deriv. Fall 2, 7–16 (1994)
Hull, J., White, A.: Using Hull-White interest rate trees. J. Deriv. Winter 3(3), 26–36 (1996)
Hull, J., White, A.: The general Hull-White model and super calibration. Financ. Anal. J. 57, 34–44 (2001)
Ostrovski, V.: Efficient and Exact Simulation of the Hull-White Model (2013). Available at SSRN. https://ssrn.com/abstract=2304848
Black, F., Scholes, M.: The pricing of options and corporate liabilities. J. Polit. Econ. 81(3), 637–654 (1973). https://doi.org/10.1086/260062
ORE User Guide Quaternion Risk Management (2017). https://github.com/OpenSourceRisk/Engine/blob/master/Docs/UserGuide/userguide.tex
Quantlib Homepage. http://www.quantlib.org. Accessed 30 Aug 2018
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Ding, X., Zhu, H. (2020). Blockchain-Based Implementation of Smart Contract and Risk Management for Interest Rate Swap. In: Si, X., et al. Blockchain Technology and Application. CBCC 2019. Communications in Computer and Information Science, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-3278-8_14
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DOI: https://doi.org/10.1007/978-981-15-3278-8_14
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