Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Eyden Samunderu, Yvonne T. Murahwa
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	risk bonds equities hedge funds forecasting GARCH Risk in industry. Risk management HD61 Finance HG1-9999
Acceso en línea:	https://doaj.org/article/699e9bccefcd4fc59b533b785405cd14
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:699e9bccefcd4fc59b533b785405cd14
record_format	dspace
spelling	oai:doaj.org-article:699e9bccefcd4fc59b533b785405cd142021-11-25T18:08:40ZReturn Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach10.3390/jrfm141105401911-80741911-8066https://doaj.org/article/699e9bccefcd4fc59b533b785405cd142021-11-01T00:00:00Zhttps://www.mdpi.com/1911-8074/14/11/540https://doaj.org/toc/1911-8066https://doaj.org/toc/1911-8074Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.Eyden SamunderuYvonne T. MurahwaMDPI AGarticleriskbondsequitieshedge fundsforecastingGARCHRisk in industry. Risk managementHD61FinanceHG1-9999ENJournal of Risk and Financial Management, Vol 14, Iss 540, p 540 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	risk bonds equities hedge funds forecasting GARCH Risk in industry. Risk management HD61 Finance HG1-9999
spellingShingle	risk bonds equities hedge funds forecasting GARCH Risk in industry. Risk management HD61 Finance HG1-9999 Eyden Samunderu Yvonne T. Murahwa Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach
description	Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.
format	article
author	Eyden Samunderu Yvonne T. Murahwa
author_facet	Eyden Samunderu Yvonne T. Murahwa
author_sort	Eyden Samunderu
title	Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach
title_short	Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach
title_full	Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach
title_fullStr	Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach
title_full_unstemmed	Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach
title_sort	return based risk measures for non-normally distributed returns: an alternative modelling approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/699e9bccefcd4fc59b533b785405cd14
work_keys_str_mv	AT eydensamunderu returnbasedriskmeasuresfornonnormallydistributedreturnsanalternativemodellingapproach AT yvonnetmurahwa returnbasedriskmeasuresfornonnormallydistributedreturnsanalternativemodellingapproach
_version_	1718411563098963968

Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Ejemplares similares