✦ LIBER ✦

Forecasting Value-at-Risk using high frequency data: The realized range model

✍ Scribed by Xi-Dong Shao; Yu-Jun Lian; Lian-Qian Yin

Book ID: 116511872
Publisher: Elsevier Science
Year: 2009
Tongue: English
Weight: 181 KB
Volume: 20
Category: Article
ISSN: 1044-0283
DOI: 10.1016/j.gfj.2008.11.003

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Correcting the Errors: Volatility Foreca

Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities

✍ Torben G. Andersen; Tim Bollerslev; Nour Meddahi 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 146 KB

Forecasting Value-at-Risk using nonlinea

Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range

✍ Cathy W.S. Chen; Richard Gerlach; Bruce B.K. Hwang; Michael McAleer 📂 Article 📅 2012 🏛 Elsevier Science 🌐 English ⚖ 605 KB

Daily FX Volatility Forecasts: Can the G

Daily FX Volatility Forecasts: Can the GARCH(1,1) Model be Beaten using High-Frequency Data?

✍ David G. Mcmillan; Alan E. H. Speight 📂 Article 📅 2011 🏛 John Wiley and Sons 🌐 English ⚖ 120 KB

## ABSTRACT Volatility forecasting remains an active area of research with no current consensus as to the model that provides the most accurate forecasts, though Hansen and Lunde (2005) have argued that in the context of daily exchange rate returns nothing can beat a GARCH(1,1) model. This paper ex