Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching

DOI10.1016/j.jedc.2007.01.010zbMath1201.91232OpenAlexW2000033999MaRDI QIDQ1017067

Taisei Kaizoji, Thomas C. H. Lux

Publication date: 18 May 2009

Published in: Journal of Economic Dynamics \& Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jedc.2007.01.010

zbMATH Keywords

volumeforecastingvolatilitylong memory models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70)

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Uses Software

• longmemo

Cites Work

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• Volatility persistence and switching ARCH in Japanese stock returns
• Identifying permanent and temporary components in daily and monthly Japanese stock prices
• Fractionally integrated generalized autoregressive conditional heteroskedasticity
• A simple nonlinear time series model with misleading linear properties
• Forecasting exchange rate volatility.
• Econometric Evaluation of Linear Macro-Economic Models
• AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
• Intermittent turbulence in self-similar cascades: divergence of high moments and dimension of the carrier
• Stochastic volatility as a simple generator of apparent financial power laws and long memory
• Volatility processes and volatility forecast with long memory
• A NOISE TRADER MODEL AS A GENERATOR OF APPARENT FINANCIAL POWER LAWS AND LONG MEMORY
• Forecasting multifractal volatility
• Long memory and regime switching