𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Forecasting volatility: Roles of sampling frequency and forecasting horizon

✍ Scribed by Wing Hong Chan; Xin Cheng; Joseph K.W. Fung


Book ID
102842696
Publisher
John Wiley and Sons
Year
2010
Tongue
English
Weight
576 KB
Volume
30
Category
Article
ISSN
0270-7314

No coin nor oath required. For personal study only.

✦ Synopsis


Abstract

This study empirically tests how and to what extent the choice of the sampling frequency, the realized volatility (RV) measure, the forecasting horizon and the time‐series model affect the quality of volatility forecasting. Using highly synchronous executable quotes retrieved from an electronic trading platform, the study avoids the influence of various market microstructure factors in measuring RV with high‐frequency intraday data and in inferring implied volatility (IV) from option prices. The study shows that excluding non‐trading‐time volatility produces significant downward bias of RV by as much as 36%. Quality of prediction is significantly affected by the forecasting horizon and RV model, but is largely immune from the choice of sampling frequency. Consistent with prior research, IV outperforms time‐series forecasts; however, the information content of historical volatility critically depends on the choice of RV measure. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark


📜 SIMILAR VOLUMES


The Role of Volatility in Forecasting
✍ Bernadette A. Minton; Catherine M. Schrand; Beverly R. Walther 📂 Article 📅 2002 🏛 Springer US 🌐 English ⚖ 118 KB
Forecasting exchange rate volatility: a
✍ David T. L. Siu; John Okunev 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 160 KB

## Abstract Recent studies suggest realized volatility provides forecasts that are as good as option‐implied volatilities, with improvement stemming from the use of high‐frequency data instead of a long‐memory specification. This paper examines whether volatility persistence can be captured by a lo

Optimal sampling frequency for volatilit
✍ Malay Bhattacharyya; Dileep Kumar M; Ramesh Kumar 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 192 KB

## Abstract This paper evaluates the performance of conditional variance models using high‐frequency data of the National Stock Index (S&P CNX NIFTY) and attempts to determine the optimal sampling frequency for the best daily volatility forecast. A linear combination of the realized volatilities ca

Forecasting performance of extreme-value
✍ Vipul; Joshy Jacob 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 149 KB

## Abstract This study evaluates the forecasting performance of extreme‐value volatility estimators for the equity‐based Nifty Index using two‐scale realized volatility. This benchmark mitigates the effect of microstructure noise in the realized volatility. Extreme‐value estimates with relatively s