JOURNAL OF EMPIRICAL FINANCE，VOL46 ,MARCH 2018
预测全球股市隐含波动率指数
作者：Stavro sDegiannakis（Panteion University of Social and Political Sciences）
George Filis（Bournemouth University）
Hossein Hassani（University of Tehran）
摘要：本文比较了参数和非参数方法预测隐含波动率指数的能力。我们使用组合和模型平均模型来扩展我们的比较。预测模型应用于最重要股票市场指数的八个隐含波动率指数。我们提供的证据表明，奇异谱分析与Holt-Winters（SSA-HW）相结合的非参数模型在未来1个交易日和10个交易日的预测期内表现出统计上更优的预测能力。相比之下，基于参数（自回归集成模型）和非参数模型（SSA-HW）的模型平均预测能够为预测提供改进，特别是在未来10个交易日的预测期内。出于稳健性目的，我们基于上述预测构建了两种交易策略，这进一步证实了SSA-HW和ARI-SSA-HW能够在采样时间外带来更高的净日收益。
关键词：股市，隐含波动率，波动性预测，奇异谱分析，ARFIMA，HAR，Holt-Winters，模型置信度，模型平均预测
Forecasting Global Stock Market Implied Volatility Indices
Stavros Degiannakis (Panteion University of Social and Political Sciences), George Filis (Bournemouth University), Hossein Hassani (University of Tehran)
ABSTRACT
This study compares parametric and non-parametric techniques in terms of their forecasting power on implied volatility indices. We extend our comparisons using combined and model-averaging models. The forecasting models are applied on eight implied volatility indices of the most important stock market indices. We provide evidence that the non-parametric models of Singular Spectrum Analysis combined with Holt-Winters (SSA-HW) exhibit statistically superior predictive ability for the one and ten trading days ahead forecasting horizon. By contrast, the model-averaged forecasts based on both parametric (Autoregressive Integrated model) and non-parametric models (SSA-HW) are able to provide improved forecasts, particularly for the ten trading days ahead forecasting horizon. For robustness purposes, we build two trading strategies based on the aforementioned forecasts, which further confirm that the SSA-HW and the ARI-SSA-HW are able to generate significantly higher net daily returns in the out-of-sample period.
Keywords: Stock Market, Implied Volatility, Volatility Forecasting, Singular Spectrum Analysis, ARFIMA, HAR, Holt-Winters, Model Confidence Set, Model-Averaged Forecasts
原文链接：
https://www.sciencedirect.com/science/article/pii/S0927539817301263#!
翻译：王秭越
