Clustering volatility regimes for dynamic trading strategies

04/21/2020
by   Gilad Francis, et al.
0

We develop a new method to find the number of volatility regimes in a non-stationary financial time series. We use change point detection to partition a time series into locally stationary segments, then estimate the distributions of each piece. The distributions are clustered into a learned number of discrete volatility regimes via an optimisation routine. Using this method, we investigate and determine a clustering structure for indices, large cap equities and exchange-traded funds. Finally, we create and validate a dynamic portfolio allocation strategy that learns the optimal match between the current distribution of a time series with its past regimes, thereby making online risk-avoidance decisions in the present.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

06/25/2019

Dynamic time series clustering via volatility change-points

This note outlines a method for clustering time series based on a statis...
10/22/2021

Clustering Market Regimes using the Wasserstein Distance

The problem of rapid and automated detection of distinct market regimes ...
06/11/2021

Multivariate Pair Trading by Volatility Model Adaption Trade-off

Pair trading is one of the most discussed topics among financial researc...
04/28/2021

Nonparametric Test for Volatility in Clustered Multiple Time Series

Contagion arising from clustering of multiple time series like those in ...
08/22/2017

Dynamic correlations at different time-scales with Empirical Mode Decomposition

The Empirical Mode Decomposition (EMD) provides a tool to characterize t...
07/22/2021

Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces

This paper presents static and dynamic versions of univariate, multivari...
11/27/2018

Extracting conditionally heteroscedastic components using ICA

In the independent component model, the multivariate data is assumed to ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.