基于波动择时绩效的高维波动率估计量与预测模型研究

对协方差矩阵高频估计量和预测模型的选择,共同影响协方差的预测效果,从而影响波动择时投资组合策略的绩效。资产维数很高时,协方差矩阵高频估计量的构建会因非同步交易而丢弃大量数据,降低信息利用效率。鉴于此,将可以充分利用资产日内价格信息的KEM估计量用于估计中国股市资产的高维协方差矩阵,并与两种常用协方差矩阵估计量进行比较。进一步地,将三种估计量分别用于多元异质自回归模型、指数加权移动平均模型以及短、中、长期移动平均模型进行样本外预测,并比较在三种基于风险的投资组合策略下的经济效益。采用上证50指数中20只不同流动性成份股逐笔高频数据的实证研究发现:(1)无论是在市场平稳时期还是市场剧烈震荡期,长期移动平均模型都是高维协方差估计量预测建模的最优选择,在应用于各种波动择时策略时都可以实现最低成本和最高收益。(2)在市场平稳时期,KEM估计量是高维协方差估计的最优选择,应用于各种波动择时策略时基本都可以实现最低成本和最高收益;在市场剧烈震荡期,使用KEM估计量进行波动择时仍然可以在成本方面保持优势,但在收益上并不占优。(3)无论是在市场平稳时期还是市场剧烈震荡期,最低的成本都是在采用等风险贡献投...

The Study of High-dimensional Volatility Estimators and Forecasting Models based on Volatility Timing Performance

The selection of high-frequency data based covariance matrix estimators and forecasting models, jointly influence the forecasting performance of covariance, which therefore influences the performance of volatility timing portfolio strategies. When the number of assets is large, a lot of intraday data are not utilized in common high-frequency data based covariance matrix estimators due to non-synchronous trading, implying a loss of efficiency in information usage. Therefore, the KEM estimator which employs all the intraday price information is used to construct the high dimensional covariance matrix estimator in China's stock market, and its performance is compared, with two commonly used estimators. In addition, each of these three estimators is used in five forecasting models, namely the multivariate heterogeneous autoregressive model, the exponentially weighted moving average model, and the short-term, medium-term and long-term moving average models, and their economic performance under three risk-based portfolio strategies is compared. Empirical experiments with the tick-by-tick high-frequency data for 20 constituent stocks of the SSE 50 index show that: (1) The long-term moving average model is the best choice to forecast high-dimensional covariance estimators, since it achieves the lowest cost and the highest return for all the volatility timing strategies, no matter the market is stable or extremely volatile. (2) The KEM estimator is the best choice to estimate high-dimensional covariance matrix when the market is stable, since it achieves the lowest cost and the highest return for all the volatility timing strategies. However, the KEM estimator only achieves the lowest cost when the market is extremely volatile. (3) Among the volatility timing strategies, the equal risk contribution portfolio strategy always achieves the lowest cost, while the global minimum variance portfolio strategy always achieves the highest return, regardless of the market condition. The research not only for the first time evaluates the effectiveness of the KEM estimator in common volatility timing strategies, but for the first time empirically proves that the easily implemented long-term moving average model has significant superiority in high dimensional covariance matrix forecasting, and thus is of critical importance for applications such as investment decision making and risk management.