FOREIGN CURRENCY OPTION PRICING WITH PROPORTIONAL TRANSACTION COSTS

The purpose of present work is to examine the financial problem of finding the universal reservation prices of a European call option written on exchange rate when there is proportional transaction costs of trading foreign currency in the market. An approach is suggested to compute the reservation bid-ask price of foreign currency call option based on maximizing the investor＇s expected utility. Option prices are determined from the investor＇s basic portfolio selection problem, without the need to solve a more complex optimization problem involving the insertion of the option payoffs into the terminal value function. Option prices are computed numerically in a Markov chain approximation for the case of exponential utility. Numerical results show that the option price bounds are almost independent of the alternative risk aversion parameter, but the bounds of NT region becomes narrower and the range of values of the initial holding for which the fair price lies within the bid-ask spread is shifted to a lower value when the risk aversion parameter increases.

Optimal proportioning of iron ore in sintering process based on improved multi-objective beluga whale optimisation algorithm

Proportioning is an important part of sintering,as it affects the cost of sintering and the quality of sintered ore.To address the problems posed by the complex raw material information and numerous constraints in the sintering process,a multi-objective optimisation model for sintering proportioning was established,which takes the proportioning cost and TFe as the optimisation objectives.Additionally,an improved multi-objective beluga whale optimisation(IMOBWO)algorithm was proposed to solve the nonlinear,multi-constrained multi-objective optimisation problems.The algorithm uses the con-strained non-dominance criterion to deal with the constraint problem in the model.Moreover,the algorithm employs an opposite learning strategy and a population guidance mechanism based on angular competition and two-population competition strategy to enhance convergence and population diversity.The actual proportioning of a steel plant indicates that the IMOBWO algorithm applied to the ore proportioning process has good convergence and obtains the uniformly distributed Pareto front.Meanwhile,compared with the actual proportioning scheme,the proportioning cost is reduced by 4.3361¥/t,and the TFe content in the mixture is increased by 0.0367%in the optimal compromise solution.Therefore,the proposed method effectively balances the cost and total iron,facilitating the comprehensive utilisation of sintered iron ore resources while ensuring quality assurance.

Stock Trading via Feedback Control： Stochastic Model Predictive or Genetic？

We seek a discussion about the most suitable feedback control structure for stock trading under the consideration of proportional transaction costs. Suitability refers to robustness and performance capability. Both are tested by considering different one-step ahead prediction qualities, including the ideal case （perfect price-ahead prediction）, correct prediction of the direction of change in daily stock prices and the worst-case （wrong price rate sign-prediction at all sampling intervals）. Feedback control structures are partitioned into two general classes： stochastic model predictive control （SMPC） and genetic. For the former class, three controllers are discussed, whereby it is distinguished between two Markowitz- and one dynamic hedging-inspired SMPC formulation. For the latter class, five trading algorithms are disucssed, whereby it is distinguished between two different moving average （MA） based strategies, two trading range （TR） based strategies, and one strategy based on historical optimal （HistOpt） trajectories. This paper also gives a preliminary discussion about how modified dynamic hedging-inspired SMPC formulations may serve as alternatives to Markowitz portfolio optimization. The combinations of all of the eight controllers with five different one-step ahead prediction methods are backtested for daily trading of the 30 components of the German stock market index DAX for the time period between November 27, 2015 and November 25, 2016.