Modelling of non-stationarity in extreme share returns

Financial risk control depends on the assumptions made about the distribution of share returns. A study on the behaviour of share market returns provides a practical solution for identifying the adequate statistical distribution assumption and accurate predictive interpretation. Most studies on mode...

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Main Author: Marsani, Muhammad Fadhil
Format: Thesis
Language:English
Published: 2021
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Online Access:http://eprints.utm.my/id/eprint/102423/1/MuhammadFadhilMarsaniPFS2021.pdf.pdf
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spelling my-utm-ep.1024232023-08-28T06:30:51Z Modelling of non-stationarity in extreme share returns 2021 Marsani, Muhammad Fadhil QD Chemistry Financial risk control depends on the assumptions made about the distribution of share returns. A study on the behaviour of share market returns provides a practical solution for identifying the adequate statistical distribution assumption and accurate predictive interpretation. Most studies on modelling extreme returns only focus on traditional stationary sequences technique. In many cases, however, the interpretation of the extremes in return series clearly shows the existence of non-stationarity in the series. As an alternative, a non-stationarity algorithm is proposed to produce a more efficient model using a much simpler approach. In this study, a new statistical procedure based on the state of the time series namely a two-stage (TS) method are formed to classify the best extreme distribution fitting. In general, the extreme returns are illustrated by a parametric model which is driven by the asymptotic theory of extreme values of independent and identically distributed (i.i.d) random variables. The TS method is applied to several common distribution models typically used in modelling extreme share returns namely Generalized Lambda Distribution (GLD), Generalized Extreme Value Distribution (GEV), Generalized Pareto Distribution (GPA), Generalized Logistic Distribution (GLO) and Laplace Distribution (LAP). Monte Carlo simulations from known and unknown samples are carried out to appraise the performance of the non-stationary and the stationary techniques. The simulation results reveals that the TS method yields relatively more accurate parameter estimates than the stationary method, especially when estimating positive and monotonous cases trend sequences. The extreme quantile measures using the TS method are found to be more efficient than the conventional approach. This is because the TS method takes into consideration of the information in the time series when evaluating extreme quantile periods. The TS method also has the benefit of being computationally simpler since the transformed process is closer to the actual process. In this respect, the data appear to be more closely meet the assumptions of a statistical inference procedure that is to be applied. The overall results in this study conclude that the proposed TS method could improve the estimation of extreme returns and is a useful instrument for financial risk management. 2021 Thesis http://eprints.utm.my/id/eprint/102423/ http://eprints.utm.my/id/eprint/102423/1/MuhammadFadhilMarsaniPFS2021.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:146048 phd doctoral Universiti Teknologi Malaysia Faculty of Science
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic QD Chemistry
spellingShingle QD Chemistry
Marsani, Muhammad Fadhil
Modelling of non-stationarity in extreme share returns
description Financial risk control depends on the assumptions made about the distribution of share returns. A study on the behaviour of share market returns provides a practical solution for identifying the adequate statistical distribution assumption and accurate predictive interpretation. Most studies on modelling extreme returns only focus on traditional stationary sequences technique. In many cases, however, the interpretation of the extremes in return series clearly shows the existence of non-stationarity in the series. As an alternative, a non-stationarity algorithm is proposed to produce a more efficient model using a much simpler approach. In this study, a new statistical procedure based on the state of the time series namely a two-stage (TS) method are formed to classify the best extreme distribution fitting. In general, the extreme returns are illustrated by a parametric model which is driven by the asymptotic theory of extreme values of independent and identically distributed (i.i.d) random variables. The TS method is applied to several common distribution models typically used in modelling extreme share returns namely Generalized Lambda Distribution (GLD), Generalized Extreme Value Distribution (GEV), Generalized Pareto Distribution (GPA), Generalized Logistic Distribution (GLO) and Laplace Distribution (LAP). Monte Carlo simulations from known and unknown samples are carried out to appraise the performance of the non-stationary and the stationary techniques. The simulation results reveals that the TS method yields relatively more accurate parameter estimates than the stationary method, especially when estimating positive and monotonous cases trend sequences. The extreme quantile measures using the TS method are found to be more efficient than the conventional approach. This is because the TS method takes into consideration of the information in the time series when evaluating extreme quantile periods. The TS method also has the benefit of being computationally simpler since the transformed process is closer to the actual process. In this respect, the data appear to be more closely meet the assumptions of a statistical inference procedure that is to be applied. The overall results in this study conclude that the proposed TS method could improve the estimation of extreme returns and is a useful instrument for financial risk management.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Marsani, Muhammad Fadhil
author_facet Marsani, Muhammad Fadhil
author_sort Marsani, Muhammad Fadhil
title Modelling of non-stationarity in extreme share returns
title_short Modelling of non-stationarity in extreme share returns
title_full Modelling of non-stationarity in extreme share returns
title_fullStr Modelling of non-stationarity in extreme share returns
title_full_unstemmed Modelling of non-stationarity in extreme share returns
title_sort modelling of non-stationarity in extreme share returns
granting_institution Universiti Teknologi Malaysia
granting_department Faculty of Science
publishDate 2021
url http://eprints.utm.my/id/eprint/102423/1/MuhammadFadhilMarsaniPFS2021.pdf.pdf
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