交易成本下投资组合的非椭圆正交GARCH模型

Marc S. Paolella, Pawel Polak, Patrick S. Walker
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引用次数: 7

摘要

投资组合优化的协方差矩阵预测必须平衡对新数据点的敏感性和稳定性,以避免过度的再平衡。为了实现这一目标,提出了一种新的鲁棒正交GARCH模型,用于多元非高斯资产收益集。条件回报分布是多元广义双曲分布,色散矩阵动力学是由主成分分解中的主导因子驱动的。这些主要因素中的每一个都具有单变量GARCH结构,而其余特征值随时间保持不变。通过期望最大化算法对所有模型参数进行联合极大似然估计,适用于高维环境。新模型即使对大型资产领域也能产生现实的相关性预测,并且在市场低迷时期比许多竞争模型更好地捕捉到不断上升的两两相关性。此外,本文还改进了基于特征值的金融系统风险指标的预测。至关重要的是,它产生的投资组合周转率要低得多,扣除交易成本后的风险调整回报率更高,即使在高昂的交易费用下,其表现也优于等权重策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Non-Elliptical Orthogonal GARCH Model for Portfolio Selection under Transaction Costs
Covariance matrix forecasts for portfolio optimization have to balance sensitivity to new data points with stability in order to avoid excessive rebalancing. To achieve this, a new robust orthogonal GARCH model for a multivariate set of non-Gaussian asset returns is proposed. The conditional return distribution is multivariate generalized hyperbolic and the dispersion matrix dynamics are driven by the leading factors in a principle component decomposition. Each of these leading factors is endowed with a univariate GARCH structure, while the remaining eigenvalues are kept constant over time. Joint maximum likelihood estimation of all model parameters is performed via an expectation maximization algorithm, and is applicable in high dimensions. The new model generates realistic correlation forecasts even for large asset universes and captures rising pairwise correlations in periods of market distress better than numerous competing models. Moreover, it leads to improved forecasts of an eigenvalue-based financial systemic risk indicator. Crucially, it generates portfolios with much lower turnover and superior risk-adjusted returns net of transaction costs, outperforming the equally weighted strategy even under high transaction fees.
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