平方根因素过程和模型模糊下的稳健最优资产负债管理:一种BSDE方法

IF 0.5 4区 数学 Q4 STATISTICS & PROBABILITY
Yumo Zhang
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引用次数: 0

摘要

摘要本文研究了具有随机投资机会的可能非马尔可夫环境中规避模糊性的管理者的鲁棒最优资产负债管理问题。经理可以在金融市场上使用一种无风险资产和一种风险资产。风险的市场价格依赖于满足仿射形式的平方根马尔可夫模型的随机因素过程,而风险资产的收益率和波动性则可能由一般的非马尔可夫无界随机过程给出。这个金融框架包括,但不限于,恒定弹性方差(CEV)模型,4/2随机波动模型家族,以及一些路径相关的非马尔可夫模型,作为例外情况。与大多数论文使用Hamilton-Jacobi-Bellman-Issacs (HJBI)方程来处理马尔可夫情况下的模型模糊不同,我们提出了一种倒向随机微分方程(BSDE)方法来解决非马尔可夫情况。通过显式求解相关的BSDEs,我们分别以封闭形式导出了幂效用和指数效用的鲁棒最优控制和鲁棒最优值函数。此外,对模型的一些特殊情况给出了解析解。最后,通过数值算例分析了CEV模型和4/2模型下模型模糊度和市场参数对稳健最优投资策略的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust optimal asset-liability management under square-root factor processes and model ambiguity: a BSDE approach

Abstract

This article studies robust optimal asset-liability management problems for an ambiguity-averse manager in a possibly non-Markovian environment with stochastic investment opportunities. The manager has access to one risk-free asset and one risky asset in a financial market. The market price of risk relies on a stochastic factor process satisfying an affine-form, square-root, Markovian model, whereas the risky asset’s return rate and volatility are potentially given by general non-Markovian, unbounded stochastic processes. This financial framework includes, but is not limited to, the constant elasticity of variance (CEV) model, the family of 4/2 stochastic volatility models, and some path-dependent non-Markovian models, as exceptional cases. As opposed to most of the papers using the Hamilton-Jacobi-Bellman-Issacs (HJBI) equation to deal with model ambiguity in the Markovian cases, we address the non-Markovian case by proposing a backward stochastic differential equation (BSDE) approach. By solving the associated BSDEs explicitly, we derive, in closed form, the robust optimal controls and robust optimal value functions for power and exponential utility, respectively. In addition, analytical solutions to some particular cases of our model are provided. Finally, the effects of model ambiguity and market parameters on the robust optimal investment strategies are illustrated under the CEV model and 4/2 model with numerical examples.

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来源期刊
Stochastic Models
Stochastic Models 数学-统计学与概率论
CiteScore
1.30
自引率
14.30%
发文量
42
审稿时长
>12 weeks
期刊介绍: Stochastic Models publishes papers discussing the theory and applications of probability as they arise in the modeling of phenomena in the natural sciences, social sciences and technology. It presents novel contributions to mathematical theory, using structural, analytical, algorithmic or experimental approaches. In an interdisciplinary context, it discusses practical applications of stochastic models to diverse areas such as biology, computer science, telecommunications modeling, inventories and dams, reliability, storage, queueing theory, mathematical finance and operations research.
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