最小化CVaR对冲问题的随机分形搜索算法求解

Stochastic Fractal Search Algorithm for Solving Hedging Problem by Minimum CVaR

在考虑交易费用情形下和在给定初始成本约束条件下,针对期权对冲问题,采用条件风险价值来刻画损失风险,基于跳-扩散模型建立动态随机优化模型,并探寻用随机分形搜索算法来求解该非线性优化问题,获得最优对冲策略。数值模拟算例和实证研究的结果表明随机分形搜索算法求解最小化CVaR对冲问题是可行和有效的。

In this paper,based on the jump diffusion model,we consider the problem of hedging conditional value at risk of contingent claims on a stock under transaction costs and given initial cost.A dynamic stochastic optimization model is established,and Stochastic Fractal Search is used to solve the nonlinear optimization problem to obtain the optimal hedging strategy.The results of numerical simulation and empirical research show that Stochastic Fractal Search is feasible and effective for solving the hedging problem by minimum CVaR.