Neural Network for Pricing and Universal Static Hedging of Contingent Claims

30 Pages Posted: 12 Dec 2019

See all articles by Vikranth Lokeshwar

Vikranth Lokeshwar

affiliation not provided to SSRN

Vikram Bhardawaj

affiliation not provided to SSRN

Shashi Jain

Indian Institute of Science (IISc) - Deptartment of Management Studies

Date Written: November 21, 2019

Abstract

We present here a regress later based Monte Carlo approach that uses neural networks for pricing high-dimensional contingent claims. The choice of specific architecture of the neural networks used in the proposed algorithm provides for interpretability of the model, a feature that is often desirable in the financial context. Specifically, the interpretation leads us to demonstrate that any contingent claim — possibly high dimensional and path-dependent — under Markovian and no- arbitrage assumptions, can be semi-statically hedged using a portfolio of short maturity options. We show how the method can be used to obtain an upper and lower bound to the true price, where the lower bound is obtained by following a sub-optimal policy, while the upper bound by exploiting the dual formulation. Unlike other duality based upper bounds where one typically has to resort to nested simulation for constructing super-martingales, the martingales in the current approach come at no extra cost, without the need for any sub-simulations. We demonstrate through numerical examples the simplicity and efficiency of the method for both pricing and semi-static hedging of path-dependent options.

Keywords: Neural network, Monte Carlo, Regress later, static hedging with options, American Monte Carlo

JEL Classification: 65C05, 91G60

Suggested Citation

Lokeshwar, Vikranth and Bhardawaj, Vikram and Jain, Shashi, Neural Network for Pricing and Universal Static Hedging of Contingent Claims (November 21, 2019). Available at SSRN: https://ssrn.com/abstract=3491209 or http://dx.doi.org/10.2139/ssrn.3491209

Vikranth Lokeshwar

affiliation not provided to SSRN

Vikram Bhardawaj

affiliation not provided to SSRN

Shashi Jain (Contact Author)

Indian Institute of Science (IISc) - Deptartment of Management Studies ( email )

Indian Institute of Science
Bangalore
India

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