Semi-Parametric Forecasting of Realized Volatility
Semi-Parametric Forecasting of Realized Volatility
复制标题
已实现波动率的半参数预测
DOI：
10.2202/1558-3708.1814
复制
发表时间：
2011
影响因子：
0.8
通讯作者：
S. Hurn
中科院分区：
文献类型：
--
作者：
R. Becker;A. Clements;S. Hurn
时间序列模型生成的预测传统上更重视最近的观测结果。本文开发了一种不依赖于该约定的替代半参数预测方法，并将其应用于预测资产回报波动性的问题。在这种方法中，预测是历史波动性的加权平均值，其中最大的权重给予与预测形成时表现出相似市场状况的时期。权重是通过多元核方案比较随时间波动的短期趋势（作为市场状况的衡量标准）来确定的。研究发现，半参数方法产生的预测在短期和长期预测范围内都比许多竞争方法更准确。
Forecasts generated by time series models traditionally place greater weight on more recent observations. This paper develops an alternative semi-parametric method for forecasting that does not rely on this convention and applies it to the problem of forecasting asset return volatility. In this approach, a forecast is a weighted average of historical volatility, with the greatest weight given to periods that exhibit similar market conditions to the time at which the forecast is being formed. Weighting is determined by comparing short-term trends in volatility across time (as a measure of market conditions) by means of a multivariate kernel scheme. It is found that the semi-parametric method produces forecasts that are significantly more accurate than a number of competing approaches at both short and long forecast horizons.