High-frequency data for volatility modelling

用于波动率建模的高频数据

• 批准号: 1927170
• 金额: --
• 依托单位: London School of Economics and Political Science
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1927170
• 关键词: frequency; data; volatility; modelling

The covariance matrix of asset returns is important for a wide range of individuals. Academics use estimates of the covariance matrix to test asset-pricing theories. Risk managers use the matrix to construct measures such as "value at risk." Corporate managers require accurate measures of covariances for hedging strategies. Last but not least, portfolio managers use the covariance matrix in designing tracking strategies where the return on their portfolio is designed to closely follow the return on a benchmark portfolio. But, covariance matrix, can be difficult to estimate in the context of portfolio optimization when the investable universe consists of a large number of assets. For example, in the Markowitz model of mean-variance optimization, an unconstrained covariance matrix with n assets necessitates the estimation of n(n + 1)/2 elements, which quickly becomes unmanageable as n grows, and even if feasible would often result in optimal asset allocation weights that have undesirable properties, such as extreme long and short positions. Various approaches have been proposed in the literature to deal with this problem. One approach consists in imposing some further structure on the covariance matrix to reduce the number of parameters to be estimated, typically in the form of a factor model. One of the most popular methods for analysing large cross-sectional data sets is factor analysis. Some of the most influential economic theories, e.g. the arbitrage pricing theory of Ross (1976) are based on factor models.

资产收益的协方差矩阵对于范围广泛的个人来说很重要。学者们使用协方差矩阵的估计来检验资产定价理论。风险经理使用该矩阵来构建诸如“风险价值”之类的度量。企业经理需要准确衡量套期保值策略的协方差。最后但同样重要的是，投资组合经理在设计跟踪策略时使用协方差矩阵，其中投资组合的回报被设计为密切跟踪基准投资组合的回报。但是，当可投资领域由大量资产组成时，在投资组合优化的背景下，协方差矩阵可能很难估计。例如，在均值-方差优化的Markowitz模型中，具有n个资产的无约束协方差矩阵需要估计n(n+1)/2个元素，随着n的增长，这很快变得无法管理，即使可行，也往往会导致具有不希望的属性的最优资产配置权重，例如极端的多头和空头头寸。在文献中已经提出了各种方法来处理这个问题。一种方法是在协方差矩阵上施加一些进一步的结构，以减少要估计的参数的数量，通常是以因子模型的形式。分析大型横截面数据集最流行的方法之一是因子分析。一些最有影响力的经济理论，如Ross(1976)的套利定价理论，都是基于要素模型的。