Extreme value theory (EVT) application on estimating the distribution of maxima

Ramadhani, Fitria; Nurrohmah, Siti; Novita, Mila

doi:10.1063/1.4991260

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…EVT is used to model the behaviour of maxima and minima of extreme tail loss of the distribution (Ramadhani et al, 2017). Taking into consideration, the probability of rare and extreme events, EVT provides a firm establishment needed for statistical modeling of extreme risk measures (Silva et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Modeling Bank of Kigali Stock Risks in Rwanda Stock Exchange Using Extreme Value Distribution

Edem¹,

Ndengo²

2021

JFRM

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Extreme Value Theory has come forth as one of the most significant probability theories in applied sciences. Modeling extreme events has always been of interest in many disciplines such as hydrology, insurance, and finance. This study seeks to model the Bank of Kigali's (BK) stock risks in Rwanda stock exchange using Extreme Value Distribution. Two major approaches are used. To model Bank of Kigali stock risks, the Generalised Extreme Value Distribution (GEVD), precisely the Block Maxima is implemented. To examine its associated exceedances, the Generalised Pareto Distribution (GPD) is also implemented. Risk measures considered are the Value at Risk (VaR) and the Expected Shortfalls (ES). Findings reveal that the Frechet distribution fits reasonably well the distribution of the BK stock returns and GPD the exceedances. Also, the risk measures such as Value at Risk and Expected shortfall were computed with high level (99.5%) quantiles to serve as a guide to investors to make a decision as to whether to invest in Bank of Kigali's stock or not. The findings show that GPD fits the tail of the data well.

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Section: Introductionmentioning

confidence: 99%

Modeling Bank of Kigali Stock Risks in Rwanda Stock Exchange Using Extreme Value Distribution

Edem¹,

Ndengo²

2021

JFRM

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Rainfall Prediction in East Java Using Spatial Extreme Value Theory

Artha

Sofro

2019

J. Phys.: Conf. Ser.

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Extreme rainfall is a phenomenon that occurs because of the high intensity of rainfall which causes many negative impacts. For example, is flooding that can ruin crops for farmers. To minimize this hazard, rainfall prediction is carried out. One methods that can be used is spatial extreme value theory using max stable approach and copula approach. From it, we will get the return level (predictive value) rainfall by paying attention to the location elements in it. This study discusses the max-stable approach using the Smith model and the copula approach using the Copula Gaussian model. Parameter estimation used is Maximum Likelihood Estimation (MLE) method. Because it takes into spatial elements, the trend surface model is also used. At the end of the study, Akaike Information Criterion (AIC) is used to compare and select the best model that can be used in predicting rainfall. Spatial extreme value theory will be applied to rainfall data in East Java which consists of nine rainfall observation stations. The results of spatial parameter estimation show that the highest of rainfall intensity value will occur around rainfall observation station namely Sawahan which is located in Nganjuk district in a period of two years. From AIC, it can be said that the model using copula Gaussian is the best model compare with max-stable approach as it provides the smallest AIC values.

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Extreme value theory (EVT) application on estimating the distribution of maxima

Cited by 2 publications

References 4 publications

Modeling Bank of Kigali Stock Risks in Rwanda Stock Exchange Using Extreme Value Distribution

Modeling Bank of Kigali Stock Risks in Rwanda Stock Exchange Using Extreme Value Distribution

Rainfall Prediction in East Java Using Spatial Extreme Value Theory

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