Predicting financial volatility: High-frequency time-series forecasts vis-à-vis implied volatility: Predicting Financial Volatility
Predicting financial volatility: High-frequency time-series forecasts vis-à-vis implied volatility: Predicting Financial Volatility
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DOI：
10.1002/fut.20126
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发表时间：
2004-11
影响因子：
1.9
通讯作者：
M. Martens;Jason Zein
中科院分区：
文献类型：
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作者：
M. Martens;Jason Zein
最近的证据表明，期权隐含波动率比基于历史日收益的时间序列模型更能预测金融波动。在本研究中，利用高频数据和长记忆模型（最新提出的波动率建模方法）改进了金融波动率的测量和预测。这是第一个提取三种不同资产类别（股票、外汇和大宗商品）结果的研究。标准普尔500指数、日元/美元和轻质低硫原油的结果提供了一个强有力的迹象，表明基于历史日内回报的波动率预测确实提供了很好的波动率预测，可以与隐含波动率竞争，甚至优于隐含波动率。©2004 Wiley期刊公司《马可福音》24:5 - 10,2004
Recent evidence suggests option implied volatilities provide better forecasts of financial volatility than time‐series models based on historicaldailyreturns. In this study both the measurement and the forecasting of financial volatility is improved using high‐frequency data and long memory modeling, the latest proposed method to model volatility. This is the first study to extract results for three separate asset classes, equity, foreign exchange, and commodities. The results for the S&P 500, YEN/USD, and Light, Sweet Crude Oil provide a robust indication that volatility forecasts based on historicalintradayreturns do provide good volatility forecasts that can compete with and even outperform implied volatility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:1005–1028, 2004