An integral representation of elasticity and sensitivity for stochastic volatility models

Zhenyu Cui1, Duy Nguyen2, Hyungbin Park3
1[School of Business, Stevens Institute of Technology, Hoboken, USA]
2Department of Mathematics, Marist College, Poughkeepsie, USA
3Department of Mathematical Sciences, Seoul National University, Seoul, Republic of Korea

Tóm tắt

This paper presents a generic probabilistic approach to study elasticities and sensitivities of financial quantities under stochastic volatility models. We describe the shock elasticity, the quantile sensitivity and the vega value of cash flows with respect to perturbation of the volatility function of the model. The main contribution is to establish explicit formulae for these elasticities and sensitivities based on a novel application of the exponential measure change technique in Palmowski and Rolski (Bernoulli 8(6):767–785 2002). We carry out explicit calculations for the Heston model and the 3/2 stochastic volatility model, and derive explicit expressions in terms of model parameters.

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Tài liệu tham khảo

Alexander, C., Sarabia, J.M.: Quantile uncertainty and value-at-risk model risk. Risk Anal. 32(8), 1293–1308 (2012) Bernard, C., Cui, Z., McLeish, D.: On the martingale property in stochastic volatility models based on time-homogeneous diffusions. Math. Finance 27(1), 194–223 (2017) Borodin, A., Salminen, P.: Handbook of Brownian Motion, 2nd edn. Birkhäuser, Basel (2002) Borovička, J., Hansen, L.P., Scheinkman, J.A.: Shock elasticities and impulse responses. Math. Financ. Econ. 8(4), 333–354 (2014) Carr, P., Sun, J.: A new approach for option pricing under stochastic volatility. Rev. Deriv. Res. 10, 87–150 (2007) Cheridito, P., Filipović, D., Kimmel, R.: Market price of risk specifications for affine models: theory and evidence. J. Financ. Econ. 83, 123–170 (2007) Cui, Z., Nguyen, D.: Density of generalized Verhulst process and Bessel process with constant drift. Lith. Math. J. 56(4), 463–473 (2016) Fournié, E., Lasry, J.-M., Lebuchoux, J., Lions, P.-L., Touzi, N.: Applications of Malliavin calculus to Monte Carlo methods in finance. Finance Stoch. 3(4), 391–412 (1999) Glasserman, P.: Monte Carlo Methods in Financial Engineering, vol. 53. Springer, Berlin (2003) Hansen, L.: Modeling the long run: valuation in dynamic stochastic economies. In: Fisher–Schultz Lecture at the European Meetings of the Econometric Society (2008) Hansen, L.P., Scheinkman, J.A.: Long-term risk: an operator approach. Econometrica 77(1), 177–234 (2009) Hansen, L.P., Scheinkman, J.A.: Pricing growth-rate risk. Finance Stoch. 16(1), 1–15 (2012) Hong, L.J., Hu, Z., Liu, G.: Monte Carlo methods for value-at-risk and conditional value-at-risk: a review. ACM Trans. Model. Comput. Simul. (TOMACS) 24(4), 1–37 (2014) Hurd, T., Kuznetsov, A.: Explicit formulas for Laplace transforms of stochastic integrals. Markov Process Relat. Fields 14, 277–290 (2008) Linetsky, V.: The spectral representation of Bessel processes with constant drift: applications in queueing and finance. J. Appl. Probab. 41, 327–344 (2004) Palmowski, Z., Rolski, T.: A technique for exponential change of measure for Markov processes. Bernoulli 8(6), 767–785 (2002) Pham, H.: A large deviations approach to optimal long term investment. Finance Stoch. 7(2), 169–195 (2003) Ruf, J.: The martingale property in the context of stochastic differential equations. Electron. Commun. Probab. 20(34), 1–10 (2015) Sin, C.: Complications with stochastic volatility models. Adv. Appl. Probab. 30(1), 256–268 (1998) Yu, J.: On leverage in a stochastic volatility model. J. Econom. 127(2), 165–178 (2005)