Delta-Gamma分量VaR:任何类型基金的非线性风险分解

M. Dixon
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引用次数: 0

摘要

本文发展了一种分析方法,不仅可以通过工具,还可以通过基金经理或单个经理的子投资组合来分解非线性投资组合风险。此外,该方法还可用于定量投资组合经理在因子投资策略下按因子进行风险分解。我们将这种方法称为“δ - γ成分风险值”(DG CVaR),因为它使用解析近似来分解VaR。这种方法非常适合持有任何资产类别或工具类型以及期权的基金。这种分解方法在非线性投资组合收益下是可加的,充分捕捉了工具收益之间的相关性,因此非常适合按工具、经理、子投资组合或因素分解风险,并对VaR的限制进行模化。我们提供了一个具有代表性的CTA投资组合的例子,证明了分解方法比其他常见的风险分解方法的优越性。核心方法是用R实现的,并提供给读者。来源可在https://github.com/mfrdixon/RiskDecomposition找到。
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Delta-Gamma Component VaR: Non-Linear Risk Decomposition for any Type of Funds
This article develops an analytical methodology for decomposing non-linear portfolio risk not only by instrument, but also by fund managers or sub-portfolios for one single manager. Furthermore the approach may be used by quantitative portfolio managers for risk decomposition by factors under a factor investing strategy. We refer to this approach as ``Delta-Gamma Component Value-at-Risk'' (DG CVaR) as it decomposes VaR using an analytic approximation. The approach is well suited to funds holding any asset class or instrument type together with options. This decomposition approach is additive under non-linear portfolio returns, fully captures the correlations between instrument returns, and thus is well suited for decomposing risk by instrument, manager, sub-portfolio, or factor, modulo the limitations of VaR. We provide an example from a representative CTA portfolio that demonstrates superiority of the decomposition approach over other common practices for risk decomposition. The core methodology is implemented in R and made available to readers. The source can be found at https://github.com/mfrdixon/RiskDecomposition.
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