Computation of option greeks under hybrid stochastic volatility models via Malliavin calculus
Volume 5, Issue 2 (2018), pp. 145–165
Pub. online: 24 April 2018
Type: Research Article
Open Access
Received
2 May 2017
2 May 2017
Revised
26 February 2018
26 February 2018
Accepted
30 March 2018
30 March 2018
Published
24 April 2018
24 April 2018
Abstract
This study introduces computation of option sensitivities (Greeks) using the Malliavin calculus under the assumption that the underlying asset and interest rate both evolve from a stochastic volatility model and a stochastic interest rate model, respectively. Therefore, it integrates the recent developments in the Malliavin calculus for the computation of Greeks: Delta, Vega, and Rho and it extends the method slightly. The main results show that Malliavin calculus allows a running Monte Carlo (MC) algorithm to present numerical implementations and to illustrate its effectiveness. The main advantage of this method is that once the algorithms are constructed, they can be used for numerous types of option, even if their payoff functions are not differentiable.
References
Alòs, E., Ewald, C.-O.: Malliavin differentiability of the heston volatility and applications to option pricing. Adv. in Appl. Probab. 40(1), 144–162 (2008) MR2411818. https://doi.org/10.1239/aap/1208358890
Bavouzet, M.P., Messaoud, M.: Computation of Greeks using Malliavin’s calculus in jump type market models. Electron. J. Probab. 11, 10–276300 (2006) MR2217817
Benhamou, E.: Smart Monte Carlo: various tricks using Malliavin calculus. Quantitative Finance 2(5), 329–336 (2002) MR1937315. https://doi.org/10.1088/1469-7688/2/5/301
Bismut, J.M.: Large Deviations and the Malliavin Calculus. Progress in Mathematics. Birkhäuser (1984) MR0755001
Davis, M.H.A., Johansson, M.P.: Malliavin Monte Carlo Greeks for jump diffusions. Stochastic Processes and their Applications 116(1), 101–129 (2006) MR2186841. https://doi.org/10.1016/j.spa.2005.08.002
El-Khatib, Y., Privault, N.: Computations of Greeks in a market with jumps via the Malliavin calculus. Finance and Stochastics 8(2), 161–179 (2004) MR2048826. https://doi.org/10.1007/s00780-003-0111-6
Elworthy, K., Li, X.-M.: Formulae for the derivatives of heat semigroups. Journal of Functional Analysis 125(1), 252–286 (1994) MR1297021. https://doi.org/10.1006/jfan.1994.1124
Etheridge, A.: A Course in Financial Calculus. Cambridge University Press (2002) MR1930394. https://doi.org/10.1017/CBO9780511810107
Ewald, C.-O., Zhang, A.: A new technique for calibrating stochastic volatility models: the Malliavin gradient method. Quantitative Finance 6(2), 147–158 (2006) MR2221626. https://doi.org/10.1080/14697680500531676
Ewald, C.-O., Xiao, Y., Zou, Y., Siu, T.-K.: Malliavin differentiability of a class of Feller-diffusions with relevance in finance. Advances in statistics, probability and actuarial science (2012) MR2985432. https://doi.org/10.1142/9789814383318_0002
Fournié, E., Lasry, J.-M., Lebuchoux, J., Lions, P.-L., Touzi, N.: Applications of Malliavin calculus to Monte Carlo methods in finance. Finance and Stochastics 3(4), 391–412 (1999) MR1842285. https://doi.org/10.1007/s007800050068
Fournié, E., Lasry, J.-M., Lebuchoux, J., Lions, P.-L.: Applications of Malliavin calculus to Monte-Carlo methods in finance. II. Finance and Stochastics 5(2), 201–236 (2001) MR1841717. https://doi.org/10.1007/PL00013529
Glasserman, P.: Monte Carlo Methods in Financial Engineering. Stochastic Modelling and Applied Probability. Springer (2013) MR1999614
Grzelak, L., Oosterlee, C.W., Van Weeren, S.: Extension of stochastic volatility equity models with the Hull–White interest rate process. Quantitative Finance 12(1), 89–105 (2012) MR2881608. https://doi.org/10.1080/14697680903170809
Nualart, D.: The Malliavin Calculus and Related Topics vol. 1995. Springer (2006) MR1344217. https://doi.org/10.1007/978-1-4757-2437-0
Protter, P.E.: Stochastic Integration and Differential Equations. Springer Finance. Springer (2005) MR2273672. https://doi.org/10.1007/978-3-662-10061-5