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Hedging with physical or cash settlement under transient multiplicative price impact (1807.05917v2)

Published 16 Jul 2018 in q-fin.PR, math.PR, and q-fin.TR

Abstract: We solve the superhedging problem for European options in an illiquid extension of the Black-Scholes model, in which transactions have transient price impact and the costs and the strategies for hedging are affected by physical or cash settlement requirements at maturity. Our analysis is based on a convenient choice of reduced effective coordinates of magnitudes at liquidation for geometric dynamic programming. The price impact is transient over time and multiplicative, ensuring non-negativity of underlying asset prices while maintaining an arbitrage-free model. The basic (log-)linear example is a Black-Scholes model with relative price impact being proportional to the volume of shares traded, where the transience for impact on log-prices is being modelled like in Obizhaeva-Wang \cite{ObizhaevaWang13} for nominal prices. More generally, we allow for non-linear price impact and resilience functions. The viscosity solutions describing the minimal superhedging price are governed by the transient character of the price impact and by the physical or cash settlement specifications. Pricing equations under illiquidity extend no-arbitrage pricing a la Black-Scholes for complete markets in a non-paradoxical way (cf.\ {\c{C}}etin, Soner and Touzi \cite{CetinSonerTouzi10}) even without additional frictions, and can recover it in base cases.

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References (29)
  1. arXiv preprint arXiv:2206.03772 (2022)
  2. SIAM J. Financial Math. 3(1), 511–533 (2012)
  3. Math. Finance 14(1), 1–18 (2004)
  4. Math. Financ. Econ. pp. 1–25 (2016)
  5. Springer Berlin Heidelberg, Berlin, Heidelberg (2013). 10.1007/978-3-642-36433-4_2
  6. arXiv preprint arXiv:1807.05917 (2018)
  7. Appl. Math. Optim. 78(3), 643–676 (2018)
  8. Finance Stoch. 22(1), 39–68 (2018)
  9. Bernoulli 25(2), 1105–1140 (2019)
  10. J. Financial Markets 1(1), 1–50 (1998)
  11. Bilarev, T.: Feedback effects in stochastic control problems with liquidity frictions. Ph.D. thesis, Humboldt-Universität zu Berlin (2018). http://dx.doi.org/10.18452/19592
  12. Finance Stoch. 20(3), 741–771 (2016)
  13. SIAM J. Control Optim. 55(5), 3319–3348 (2017)
  14. Ann. Appl. Prob. 32(3), 1705–1733 (2022)
  15. J. Stat. Mech. Theory Exp. 2012(09), P09010 (2012)
  16. Finance Stoch. 14(3), 317–341 (2010)
  17. Finance Stoch. 19(2), 329–362 (2015)
  18. Bull. Amer. Math. Soc. (N.S.) 27(1), 1–67 (1992)
  19. Frey, R.: Perfect option hedging for a large trader. Finance Stoch. 2(2), 115–141 (1998)
  20. SIAM J. Control Optim. 49(1), 185–204 (2011)
  21. SIAM J. Financial Math. 6(1), 281–306 (2015)
  22. arXiv preprint arXiv:2103.05957 (2021)
  23. Econometrica 72(4), 1247–1275 (2004)
  24. J. Financial Quant. Anal. 27(3), 311–336 (1992)
  25. SSRN preprint 4331027 (January 19, 2023). 10.2139/ssrn.4331027
  26. J. Financial Markets 16, 1–32 (2013)
  27. SIAM J. Financial Math. 2(1), 183–212 (2011)
  28. SIAM J. Appl. Math. 61(1), 232–272 (electronic) (2000)
  29. J. Eur. Math. Soc. 4(3), 201–236 (2002)
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