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Inexact arithmetic considerations for direct control and penalty methods: American options under jump diffusion

机译:直接控制和惩罚方法的不精确算术考虑:跳扩散下的美式期权

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摘要

Solutions of Hamilton-Jacobi-Bellman (HJB) Partial Integro-Differential Equations (PIDEs) arising in financial option problems are not necessarily unique. In order to ensure convergence of a numerical scheme to the viscosity solution, it is common to use a positive coefficient discretization for such PIDEs. However in finite precision arithmetic one often encounters difficulties in solving the discretized nonlinear algebraic equations. In this paper we focus on a specific HJB PIDE, arising from pricing American options under jump diffusion. We use two formulations of this problem, the first a penalty method and the second a direct control formulation. In each case we use a positive coefficient discretization which implies that a fixed point policy iteration will converge when used to solve the nonlinear discretized algebraic equations, under very mild restrictions on parameters. However, when using finite precision arithmetic, we observe that convergence may not occur for either formulation, even if the theoretical conditions are satisfied. We estimate bounds for the penalty parameter (penalty method) and the scaling parameter (direct control formulation) so that convergence of the fixed point policy iteration in inexact arithmetic can be expected. Numerical tests verify that these bounds are conservative. The lower bound is of more practical importance, and conveniently this has a very simple form. We remark that similar issues also arise in more complicated HJB PIDEs in finance, for example when pricing American options under regime switching or guaranteed minimum withdrawal benefits (GMWB) under jump diffusion.
机译:金融期权问题中产生的汉密尔顿-雅各比-贝尔曼(HJB)偏微分方程(PIDE)的解不一定是唯一的。为了确保数值方案与粘度解的收敛,通常对此类PIDE使用正系数离散化。然而,在有限精度算术中,在求解离散非线性代数方程时常常会遇到困难。在本文中,我们将重点放在特定的HJB PIDE上,该价格是由于跳级扩散下的美国期权定价而产生的。我们使用两种解决方案,第一种是惩罚方法,第二种是直接控制方法。在每种情况下,我们都使用正系数离散化,这意味着在非常有限的参数限制下,定点策略迭代将在求解非线性离散化代数方程时收敛。但是,当使用有限精度算法时,即使满足理论条件,我们也观察到两种形式都不会发生收敛。我们估计惩罚参数(惩罚方法)和缩放参数(直接控制公式)的界限,以便可以预期不精确算法中定点策略迭代的收敛性。数值测试证明这些界限是保守的。下限具有更实际的重要性,并且方便地具有非常简单的形式。我们注意到,类似的问题也发生在金融中更为复杂的HJB债券中,例如,在体制转换下对美国期权定价或在跳跃扩散下保证最低提款收益(GMWB)时。

著录项

  • 来源
    《Applied numerical mathematics》 |2013年第10期|33-51|共19页
  • 作者单位

    Department of Electrical and Computer Engineering. University of Waterloo. Waterloo ON, N2L 3G1 Canada;

    Cheriton School of Computer Science. University of Waterloo, Waterloo ON, N2L 3G1 Canada;

    Cheriton School of Computer Science. University of Waterloo, Waterloo ON, N2L 3G1 Canada;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    American options; Jump diffusion; Inexact arithmetic;

    机译:美式期权;跳跃扩散;不精确的算术;

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