跳至正文
返回文库全部文档

利用序列交叉相关性缩放投资组合风险

文章 arXiv papers · 作者: Nikolaus Rab et al.
阅读原文

总结

本文探讨如何估算超过一天的持有期内的投资组合波动率和资产风险贡献。常见的平方根时间缩放法假设收益不存在序列相关。在这一假设下，波动率贡献也可以按同样方式缩放。

作者解释了在投资组合包含不同时区交易的资产时，这些假设为何可能失效：由于市场收盘时间不同，收盘到收盘收益可能出现序列交叉相关。这类异步相关性会使缩放后的波动率估计和资产层面的风险贡献都产生误导。论文提出了适用于任意持有期的替代方法。所提供的描述未说明具体公式或实证测试，因此仅阐明了问题及所提方法的范围，未提供足够信息来说明其详细实现或表现。

核心观点

  • 平方根时间缩放法依赖于收益不存在序列相关这一假设。
  • 不同时区的市场收盘时间可能导致投资组合收盘到收盘收益出现序列交叉相关。
  • 这些相关性可能使标准波动率缩放方法失效，并扭曲估计的资产风险贡献。
  • 论文提出了适用于任意持有期的波动率缩放和风险贡献替代方法。

标签

多资产波动率风险管理投资组合构建统计学
全文
# Scaling portfolio volatility and calculating risk contributions in the presence of serial cross-correlations


# Scaling portfolio volatility and calculating risk contributions in the presence of serial cross-correlations









In practice daily volatility of portfolio returns is transformed to longer holding periods by multiplying by the square-root of time which assumes that returns are not serially correlated. Under this assumption this procedure of scaling can also be applied to contributions to volatility of the assets in the portfolio. Close prices are often used to calculate the profit and loss of a portfolio. Trading at exchanges located in distant time zones this can lead to significant serial cross-correlations of the closing-time returns of the assets in the portfolio. These serial correlations cause the square-root-of-time rule to fail. Moreover volatility contributions in this setting turn out to be misleading due to non-synchronous correlations. We address this issue and provide alternative procedures for scaling volatility and calculating risk contributions for arbitrary holding periods.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。