Nonparametric Test for Volatility in Clustered Multiple Time Series

04/28/2021
by   Erniel B. Barrios, et al.
0

Contagion arising from clustering of multiple time series like those in the stock market indicators can further complicate the nature of volatility, rendering a parametric test (relying on asymptotic distribution) to suffer from issues on size and power. We propose a test on volatility based on the bootstrap method for multiple time series, intended to account for possible presence of contagion effect. While the test is fairly robust to distributional assumptions, it depends on the nature of volatility. The test is correctly sized even in cases where the time series are almost nonstationary. The test is also powerful specially when the time series are stationary in mean and that volatility are contained only in fewer clusters. We illustrate the method in global stock prices data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/26/2019

Volatility Models Applied to Geophysics and High Frequency Financial Market Data

This work is devoted to the study of modeling geophysical and financial ...
research
01/23/2021

Unraveling S P500 stock volatility and networks – An encoding and decoding approach

We extend the Hierarchical Factor Segmentation(HFS) algorithm for discov...
research
04/23/2019

Seasonal FIEGARCH Processes

Here we develop the theory of seasonal FIEGARCH processes, denoted by SF...
research
04/23/2020

Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility

When self-adaptive systems encounter changes within their surrounding en...
research
07/19/2018

Quantifying Volatility Reduction in German Day-ahead Spot Market in the Period 2006 through 2016

In Europe, Germany is taking the lead in the switch from the conventiona...
research
07/17/2022

Testing for explosive bubbles: a review

This review discusses methods of testing for explosive bubbles in time s...
research
04/21/2020

Clustering volatility regimes for dynamic trading strategies

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

Please sign up or login with your details

Forgot password? Click here to reset