Change-point detection for expected shortfall in time series

Expected shortfall (ES) is a popular risk measure and plays an important role in risk and portfolio management. Recently, change-point detection of risk measures has been attracting much attention in finance. Based on the self-normalized CUSUM statistic in Fan, Glynn and Pelger (2018) and the Wild B...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lingyu Sun, Dong Li
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2021
Materias:	C12 C22 C32 Industrial engineering. Management engineering T55.4-60.8
Acceso en línea:	https://doaj.org/article/3e4171cdc47246158df735e2bb78d00d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3e4171cdc47246158df735e2bb78d00d
record_format	dspace
spelling	oai:doaj.org-article:3e4171cdc47246158df735e2bb78d00d2021-11-10T04:24:33ZChange-point detection for expected shortfall in time series2096-232010.1016/j.jmse.2021.06.002https://doaj.org/article/3e4171cdc47246158df735e2bb78d00d2021-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2096232021000391https://doaj.org/toc/2096-2320Expected shortfall (ES) is a popular risk measure and plays an important role in risk and portfolio management. Recently, change-point detection of risk measures has been attracting much attention in finance. Based on the self-normalized CUSUM statistic in Fan, Glynn and Pelger (2018) and the Wild Binary Segmentation (WBS) algorithm in Fryzlewicz (2014), this paper proposes a variant WBS procedure to detect and estimate change points of ES in time series. The strengthened Schwarz information criterion is also introduced to determine the number of change points. Monte Carlo simulation studies are conducted to assess the finite-sample performance of our variant WBS procedure about ES in time series. An empirical application is given to illustrate the usefulness of our procedure.Lingyu SunDong LiKeAi Communications Co., Ltd.articleC12C22C32Industrial engineering. Management engineeringT55.4-60.8ENJournal of Management Science and Engineering, Vol 6, Iss 3, Pp 324-335 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	C12 C22 C32 Industrial engineering. Management engineering T55.4-60.8
spellingShingle	C12 C22 C32 Industrial engineering. Management engineering T55.4-60.8 Lingyu Sun Dong Li Change-point detection for expected shortfall in time series
description	Expected shortfall (ES) is a popular risk measure and plays an important role in risk and portfolio management. Recently, change-point detection of risk measures has been attracting much attention in finance. Based on the self-normalized CUSUM statistic in Fan, Glynn and Pelger (2018) and the Wild Binary Segmentation (WBS) algorithm in Fryzlewicz (2014), this paper proposes a variant WBS procedure to detect and estimate change points of ES in time series. The strengthened Schwarz information criterion is also introduced to determine the number of change points. Monte Carlo simulation studies are conducted to assess the finite-sample performance of our variant WBS procedure about ES in time series. An empirical application is given to illustrate the usefulness of our procedure.
format	article
author	Lingyu Sun Dong Li
author_facet	Lingyu Sun Dong Li
author_sort	Lingyu Sun
title	Change-point detection for expected shortfall in time series
title_short	Change-point detection for expected shortfall in time series
title_full	Change-point detection for expected shortfall in time series
title_fullStr	Change-point detection for expected shortfall in time series
title_full_unstemmed	Change-point detection for expected shortfall in time series
title_sort	change-point detection for expected shortfall in time series
publisher	KeAi Communications Co., Ltd.
publishDate	2021
url	https://doaj.org/article/3e4171cdc47246158df735e2bb78d00d
work_keys_str_mv	AT lingyusun changepointdetectionforexpectedshortfallintimeseries AT dongli changepointdetectionforexpectedshortfallintimeseries
_version_	1718440641232371712

Change-point detection for expected shortfall in time series

Ejemplares similares