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Detecting Nonlinearity in Time Series: Surrogate and Bootstrap Approaches
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Melvin J Hinich
Veröffentlicht/Copyright:
8. Dezember 2005
Detecting nonlinearity in financial time series is a key point when the main interest is to understand the generating process. One of the main tests for testing linearity in time series is the Hinich Bispectrum Nonlinearity Test (HINBIN). Although this test has been succesfully applied to a vast number of time series, further improvement in the size power of the test is possible. A new method that combines the bispectrum and the surrogate method and bootstrap is then presented for detecting nonlinearity, gaussianity and time reversibility. Simulated and real data examples are given to demonstrate the efficacy of the new tests.
Published Online: 2005-12-8
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Are Real Exchange Rates Nonlinear or Nonstationary? Evidence from a New Threshold Unit Root Test
- Detecting Nonlinearity in Time Series: Surrogate and Bootstrap Approaches
- The International CAPM and a Wavelet-Based Decomposition of Value at Risk
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Artikel in diesem Heft
- Article
- Can GARCH Models Capture Long-Range Dependence?
- Are Real Exchange Rates Nonlinear or Nonstationary? Evidence from a New Threshold Unit Root Test
- Detecting Nonlinearity in Time Series: Surrogate and Bootstrap Approaches
- The International CAPM and a Wavelet-Based Decomposition of Value at Risk
- Dual Long Memory in Inflation Dynamics across Countries of the Euro Area and the Link between Inflation Uncertainty and Macroeconomic Performance
- Forecasting Stock Market Volatility with Regime-Switching GARCH Models
