Monoyios, Michael (2009) Optimal investment and hedging under partial and inside information. In: Radon Series on Computational and Applied Mathematics. Radon Series on Computational and Applied Mathematics . De Gruyter, Berlin. (In Press)

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Abstract
This article concerns optimal investment and hedging for agents who must use trading strategies which are adapted to the filtration generated by asset prices, possibly augmented with some inside information related to the future evolution of an asset price. The price evolution and observations are taken to be continuous, so the partial (and, when applicable, inside) information scenario is characterised by asset price processes with an unknown drift parameter, which is to be filtered from price observations. We first give an exposition of filtering theory, leading to the KalmanBucy filter. We outline the dual approach to portfolio optimisation, which is then applied to the Merton optimal investment problem when the agent does not know the drift parameter of the underlying stock. This is taken to be a random variable with a Gaussian prior distribution, which is updated via the Kalman filter. This results in a model with a stochastic drift process adapted to the observation filtration, and which can be treated as a full information problem, and an explicit solution to the optimal investment problem is possible. We also consider the same problem when the agent has noisy knowledge at time of the terminal value of the Brownian motion driving the stock. Using techniques of
enlargement of filtration to accommodate the insider's additional knowledge, followed by filtering the asset price drift, we are again able to obtain an explicit solution. Finally we treat an incomplete market hedging problem. A claim on a nontraded asset is hedged using a correlated traded asset. We summarise the full information case, then treat the partial information scenario in which the hedger is uncertain of the true values of the asset price
drifts. After filtering, the resulting problem with random drifts is solved in the case that each asset's prior distribution has the same variance, resulting in analytic approximations for the optimal hedging strategy.
Item Type:  Book Section 

Subjects:  O  Z > Probability theory and stochastic processes O  Z > Systems theory 
Research Groups:  Mathematical and Computational Finance Group 
ID Code:  763 
Deposited By:  Professor Michael Monoyios 
Deposited On:  07 Jan 2009 
Last Modified:  29 May 2015 18:28 
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