On Evaluating Forecasts of Models for Realized Volatility

关于评估已实现波动率模型的预测

• 批准号: 21730177
• 负责人: ASAI Manabu
• 金额: $ 0.58万
• 依托单位: Soka University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Young Scientists (B)
• 财政年份: 2009
• 资助国家: 日本
• 起止时间: 2009 至 2010
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21730177/
• 关键词: 実現ボラティリティ; 予測; ボラティリティ

Several methods have recently been proposed in the ultra high frequency financial literature to remove the effects of microstructure noise and to obtain consistent estimates of the integrated volatility (IV) as a measure of ex-post daily volatility. Even bias-corrected and consistent realized volatility (RV) estimates of IV can contain residual microstructure noise and other measurement errors. Such noise is called "realized volatility error". As such errors are ignored, we need to take account of them in estimating and forecasting IV. This paper investigates through Monte Carlo simulations the effects of RV errors on estimating and forecasting IV with RV data. It is found that : (i) neglecting RV errors can lead to serious bias in estimators ; (ii) the effects of RV errors on one-step ahead forecasts are minor when consistent estimators are used and when the number of intraday observations is large ; and (iii) even the partially corrected R^2 recently proposed in the literature should be fully corrected for evaluating forecasts. This paper proposes a full correction of R^2. An empirical example for S&P 500 data is used to demonstrate the techniques developed in the paper.

最近，超高频金融文献中提出了几种方法，以消除微观结构噪声的影响，并获得综合波动率（IV）的一致估计，作为事后每日波动率的衡量标准。即使经过偏差校正且一致的 IV 实际波动率 (RV) 估计值也可能包含残留的微观结构噪声和其他测量误差。这种噪音称为“已实现波动率误差”。由于此类误差被忽略，我们需要在估计和预测 IV 时将其考虑在内。本文通过蒙特卡罗模拟研究了 RV 误差对利用 RV 数据估计和预测 IV 的影响。研究发现：(i) 忽略 RV 误差可能会导致估计量出现严重偏差； (ii) 当使用一致估计量且日内观测数量较多时，RV 误差对一步预测的影响较小； (iii) 即使最近在文献中提出的部分修正的 R^2 也应该完全修正以评估预测。本文提出了对R^2的全面修正。 S&P 500 数据的实证示例用于演示本文开发的技术。

项目成果

Stochastic Covariance Models

• DOI: 10.2139/ssrn.1673764
• 发表时间: 2010-08
• 期刊: Capital Markets: Asset Pricing & Valuation eJournal
• 作者: Manabu Asai;Mike K. P. So

研究成果データベース Social Science Research Networkにて公開。論文タイトルは、"Modelling and Forecasting Noisy Realized Volatility"であり、URLは である。

研究成果发表在社会科学研究网数据库上。

The Structure of Conditional, Stochastic and Realized Covariance Matrices

条件、随机和实现协方差矩阵的结构

• 发表时间: 2010
• 作者: Asai;Manabu
• 通讯作者: Manabu

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