Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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Mahesan Niranjan


This paper shows how the prices of option contracts traded in finan(cid:173) cial markets can be tracked sequentially by means of the Extended Kalman Filter algorithm. I consider call and put option pairs with identical strike price and time of maturity as a two output nonlin(cid:173) ear system. The Black-Scholes approach popular in Finance liter(cid:173) ature and the Radial Basis Functions neural network are used in modelling the nonlinear system generating these observations. I show how both these systems may be identified recursively using the EKF algorithm. I present results of simulations on some FTSE 100 Index options data and discuss the implications of viewing the pricing problem in this sequential manner.