Volatility processes and volatility forecast with long memory

By Gilles Zumbach. 2004

107 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/77e27f44004e74e6ca39b4bcad89ff5169e5f474

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Thomas Lux
2. Taisei Kaizoji
3. Cars H. Hommes
4. Frank H. Westerhoff

In Finance

1. Thomas Lux
2. Taisei Kaizoji
3. Cars H. Hommes
4. Frank H. Westerhoff

Citations in the corpus

• The multi-fractal model of asset returns: Its estimation via GMM and its use for volatility forecasting (2003, methodology): “With respect to the FIGARCH model, we should note that the underlying concept and implementation has been extensively discussed in recent literature (cf. Chung, 2002; Zumbach, 2002).”
• Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching (2007, background): “Nippon Suisan Kaisha (1332), Hoko Fishing (1351), Fudo Construction (1813),Tekken Corp. (1815), Nakano Corp. (1827), Toda Corp. (1860), Penta-Ocean Construction (1893), Obayashi Road Corp.”
• The Markov-Switching Multifractal Model of Asset Returns (2008, methodology): “Despite certain recently emphasized ambiguities in the parameterization of FIGARCH models (cf., Chung 2002; Zumbach 2004), we stick to the original framework by Baillie, Bollerslev, and Mikkelsen (1996) to compare the multifractal model with a well-established benchmark.”