Quantifying Chaos Theory in Finance: The Hidden Mathematics of Price

By Amit Ghosh

The graduate-level companion to the Chaos Theory in Finance series - a rigorous, self-contained treatise on the mathematics that governs rough, memory-laden markets.

Quantifying Chaos Theory in Finance develops fractal geometry, nonlinear dynamics, long memory and multifractal models from first principles and turns them on real price data. Where the first book taught the trader's craft, this one builds the machinery underneath it - every algorithm derived, every figure generated from the mathematics it illustrates, every method shown on a fully worked numerical example.

Inside you will master:

Fractal and Hausdorff dimension, self-similarity and the box-counting measure of a price seriesDeterministic chaos - the logistic map, strange attractors, Lyapunov exponents and Takens embedding - and whether markets truly are chaoticThe Hurst exponent, fractional Brownian motion and DFA estimation of long memoryMultifractals, the MMAR, MF-DFA and the Markov-switching multifractal modelRough volatility, heavy tails, the Fractal Market Hypothesis and Hawkes-process microstructure

Written for quantitative researchers, advanced students and the mathematically serious trader. Part of the Chaos Theory in Finance series.

• Format: Paperback
• Language: English
• ISBN: B0H9JMTCVX
• ISBN13: 9798188058951
• Release Date: July 2026
• Publisher: Independently Published
• Length: 272 Pages
• Weight: 1.05 lbs.
• Dimensions: 0.6" x 7.0" x 10.0"