DeepAI AI Chat
Log In Sign Up

Dynamic time series clustering via volatility change-points

by   Nick Whiteley, et al.

This note outlines a method for clustering time series based on a statistical model in which volatility shifts at unobserved change-points. The model accommodates some classical stylized features of returns and its relation to GARCH is discussed. Clustering is performed using a probability metric evaluated between posterior distributions of the most recent change-point associated with each series. This implies series are grouped together at a given time if there is evidence the most recent shifts in their respective volatilities were coincident or closely timed. The clustering method is dynamic, in that groupings may be updated in an online manner as data arrive. Numerical results are given analyzing daily returns of constituents of the S&P 500.


page 11

page 13


A Semiparametric Approach to the Detection of Change-points in Volatility Dynamics of Financial Data

One of the most important features of financial time series data is vola...

Clustering volatility regimes for dynamic trading strategies

We develop a new method to find the number of volatility regimes in a no...

Parameter Learning and Change Detection Using a Particle Filter With Accelerated Adaptation

This paper presents the construction of a particle filter, which incorpo...

Clustering Financial Time Series: How Long is Enough?

Researchers have used from 30 days to several years of daily returns as ...

Multiple Change Point Estimation in Stationary Ergodic Time Series

Given a heterogeneous time-series sample, the objective is to find point...

Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants

In this study, we analyze behaviours of the well-known CMA-ES by extract...

Modeling Randomly Walking Volatility with Chained Gamma Distributions

Volatility clustering is a common phenomenon in financial time series. T...

Code Repositories


Dynamic time series clustering via volatility change-points

view repo