Detecting long and short memory via spectral methods
Simone Bianco
Abstract
We study the properties of memory of a financial time series adopting two different methods of analysis, the detrended fluctuation analysis (DFA) and the analysis of the power spectrum (PSA). The methods are applied on three time series: one of high-frequency returns, one of shuffled returns and one of absolute values of returns. We prove that both DFA and PSA give results in line with those obtained with standard econometrics measures of correlation.
Create a lesson
Related papers
A Human-AI Theorem Connecting Spontaneous and Field-Induced Mechanisms of Collective Behavior in One Dimension
Weiguo Yin
Exact joint eigenvalue densities of non-Hermitian random matrices are Calogero scattering states
Zhenyu Xiao, Ze Chen, Yifei Liu et al.
Duality between the level statistics of Hermitian and non-Hermitian random matrices
Ze Chen, Zhenyu Xiao, Yifei Liu et al.
Nonlinear Fluctuating Hydrodynamics from Interacting Noisy Quantum Matter
Alexios Christopoulos, João Costa, Stefano Scopa et al.
Bayesian Tracking of a Diffusing Target in Two and Three Dimensions
Ewan McCulloch, Adam Nahum
Overcoming critical slowing down in frustrated spin systems by learned multiscale sampling
Gabriele Bandini, Giulio Biroli, Patrick Charbonneau et al.