Fourier-type monitoring procedures for strict stationarity

08/27/2019
by   Sangyeol Lee, et al.
0

We consider model-free monitoring procedures for strict stationarity of a given time series. The new criteria are formulated as L2-type statistics incorporating the empirical characteristic function. Asymptotic as well as Monte Carlo results are presented. The new methods are also employed in order to test for possible stationarity breaks in time-series data from the financial sector.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/19/2020

Tests for circular symmetry of complex-valued random vectors

We propose tests for the null hypothesis that the law of a complex-value...
research
04/17/2019

Indirect Inference for Time Series Using the Empirical Characteristic Function and Control Variates

We estimate the parameter of a time series process by minimizing the int...
research
12/05/2018

Estimation of multivariate asymmetric power GARCH models

It is now widely accepted that volatility models have to incorporate the...
research
01/26/2021

On the connection between orthant probabilities and the first passage time problem

This article describes a new Monte Carlo method for the evaluation of th...
research
01/11/2021

General Hannan and Quinn Criterion for Common Time Series

This paper aims to study data driven model selection criteria for a larg...
research
02/21/2018

A likelihood ratio approach to sequential change point detection

In this paper we propose a new approach for sequential monitoring of a p...
research
03/27/2013

Robust Inference Policies

A series of monte carlo studies were performed to assess the extent to w...

Please sign up or login with your details

Forgot password? Click here to reset