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Trailing stops are often used in stock trading to limit a maximum-possible loss and to lock in a profit. In this venue, it is important to identify the optimal trailing stop percentage, which is difficult to find and no apparent analytic technique can be applied directly. This work develops stochastic approximation algorithms to estimate the optimal trailing stop percentage. A modification using projection...
This work develops numerical methods using stochastic approximation approach for an optimal stock trading (buy and sell) strategy. Assuming the underlying asset price is governed by a mean-reverting stochastic process, we aim to find buying and selling strategies so as to maximize an overall expected return. One of the advantageous of our approach is that the underlying asset is model free. Only mean...
This paper focuses on option pricing using a stochastic optimization algorithm. The underlying stock price changes according to a set of geometric Brownian motions coupled by a continuous- time finite state Markov chain. A recursive stochastic optimization algorithm is constructed to estimate the implied volatility. Convergence analysis of the algorithm is provided together with rate of convergence...
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