Block Trading: Building up a Stock Position Under a Regime Switching Model

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This work is intended to systematically characterize the problem of block trading. The author characterizes it as a constrained optimal purchasing (or control) problem, on behalf of the vendee. The purpose of this work is to investigate the optimal purchasing strategies and give quantitative reference information on the pricing of block trade. Noting that large block of stock trading may result in the price fluctuation and different market environment may lead to distinct investing strategies, the controlled diffusion model with regime switching is adopted, which can be used to represent distinct environment of financial market, and the model is modified to allow the price impact, by allowing the drift depend on the purchase rate. Where the switching regimes can be completely observable or unobservable (hidden). Which is a significant difference in contrast to most market models in the existing literature. The objective is to maximize the total discounted shares of the underlying stock, subject to the expected fund for building up corresponding position with an upper bound. This work mainly focuses on the completely observable case, as to the case with hidden regimes, this work just briefly talk about how to reduce the unobservable model to a completely observable one. In the completely observable case, to solve this constrained control problem, the Lagrange multiplier methods and the dynamic programming approaches are used. Though optimality is obtained, what is still lacking is an explicit solution to the optimality equation. To overcome this difficulty, the two loop approximation scheme is given. For fixed Lagrange multiplier, the inner loop is to obtain optimal strategies, which is based mainly on the finite difference approximation. While, the outer loop is a stochastic recursive approximation algorithm to get the optimal Lagrange multiplier. Convergence results are obtained for both the inner and the outer approximations. The quantitative pricing information is related to the threshold levels of the optimal purchasing strategy. Numerical examples are also provided to illustrate our results. Finally, as to the unobservable case, by using the well known Wonham filter approach, one can reduce the unobservable case to a completely observable one.