Fractal Market Analysis: Applying Chaos Theory to Investment and Economics
A leading pioneer in the field offers practical applications of this innovative science. Peters describes complex concepts in an easy-to-follow manner for the non-mathematician. He uses fractals, rescaled range analysis and nonlinear dynamical models to explain behavior and understand price movements. These are specific tools employed by chaos scientists to map and measure physical and now, economic phenomena.
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Introduction to Fractal Time Series
Failure of the Gaussian Hypothesis
A Fractal Market Hypothesis
Measuring MemoryThe Hurst Process and RS Analysis
Testing RS Analysis
Periodic and Nonperiodic
Case Study Methodology
Applying Fractal Statistics
Noisy Chaos and RS Analysis
Fractal Statistics Noisy Chaos and the FMH
The Chaos Game
Fractal Distribution Tables
1/f noise antipersistent ARCH attractor autoregressive behavior black noise calculated capital markets changes chaotic Chapter correlation currencies deterministic Dow Jones Dow Jones Industrials dynamical system estimate examine exchange rate five-day returns fractal dimension fractal distributions Fractal Market Hypothesis fractal statistics fractional noise frequency distribution GARCH Gaussian graph Hurst exponent Hurst process implied volatility increase infinite variance Log(Number of Observations log/log plot Logistic Equation long-memory long-term investors Lyapunov exponent Mackey-Glass equation Mandelbrot moving average noise process noisy chaos nonperiodic cycles normal distribution number of observations observational noise option parameter period persistent phase space pink noise points portfolio problem R/S analysis R/S values random number random walk Regression output rescaled range residuals risk sample scale self-similar short-term shows significant simulated stable distributions standard deviation Standard error stochastic structure of volatility system noise Table tails theory tion trading value of H variable white noise
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