Portfolio Tail Risk: A Multivariate Extreme Value Theory Approach

Božović, Miloš

doi:10.3390/e22121425

Cited by 3 publications

(2 citation statements)

References 59 publications

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“…The extreme values are the focus of extreme value theory (EVT), which enables evaluation and risk prediction of the occurrence of extreme values. EVT has been applied to analyze the possibility that severe events may occur in a variety of domains (such as financial risk [12][13][14][15], hydrological and environmental forecasts [16][17][18][19][20][21], road traffic etc.). In contrast, there haven't been many cases of EVT being applied to public health [22].…”

Section: Introductionmentioning

confidence: 99%

Extreme value analysis of the number of student absences in Jiangsu, China: Based on extreme value theory

Liu,

Yang,

Tian

et al. 2024

PLoS ONE

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Attendance absences have a substantial impact on student’s future physical and mental health as well as academic progress. Numerous personal, familial, and social issues are among the causes of student absences. Any kind of absence from school should be minimized. Extremely high rates of student absences may indicate the abrupt commencement of a serious school health crisis or public health crisis, such as the spread of tuberculosis or COVID-19, which provides school health professionals with an early warning. We take the extreme values in absence data as the object and attempt to apply the extreme value theory (EVT) to describe the distribution of extreme values. This study aims to predict extreme instances of student absences. School health professionals can take preventative measures to reduce future excessive absences, according to the predicted results. Five statistical distributions were applied to individually characterize the extreme values. Our findings suggest that EVT is a useful tool for predicting extreme student absences, thereby aiding preventative measures in public health.

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Section: Introductionmentioning

confidence: 99%

Extreme value analysis of the number of student absences in Jiangsu, China: Based on extreme value theory

Liu,

Yang,

Tian

et al. 2024

PLoS ONE

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“…The EVT has had a vast array of applications. It covers, among many others, environmental data [1][2][3][4][5][6][7][8][9], risk management [10][11][12][13][14][15][16][17][18], but also earthquake size distribution [19] and athletic records [20].…”

Section: Introductionmentioning

confidence: 99%

Extreme Value Theory in Application to Delivery Delays

Fałdziński

Osińska

Zalewski

2021

Entropy

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This paper uses the Extreme Value Theory (EVT) to model the rare events that appear as delivery delays in road transport. Transport delivery delays occur stochastically. Therefore, modeling such events should be done using appropriate tools due to the economic consequences of these extreme events. Additionally, we provide the estimates of the extremal index and the return level with the confidence interval to describe the clustering behavior of rare events in deliveries. The Generalized Extreme Value Distribution (GEV) parameters are estimated using the maximum likelihood method and the penalized maximum likelihood method for better small-sample properties. The findings demonstrate the advantages of EVT-based prediction and its readiness for application.

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A Conceptual Model of Investment-Risk Prediction in the Stock Market Using Extreme Value Theory with Machine Learning: A Semisystematic Literature Review

Melina

Napitupulu

Mohamed

2023

Risks

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The COVID-19 pandemic has been an extraordinary event, the type of event that rarely occurs but that has major impacts on the stock market. The pandemic has created high volatility and caused extreme fluctuations in the stock market. The stock market can be characterized as either linear or nonlinear. One method that can detect extreme fluctuations is extreme value theory (EVT). This study employed a semisystematic literature review on the use of the EVT method to estimate investment risk in the stock market. The literature used was selected by applying the preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines, sourced from the ScienceDirect.com, ProQuest, and Scopus databases. A bibliometric analysis was conducted to determine the study characteristics and identify any research gaps. The results of the analysis show that studies on this topic are rarely carried out. Research in this field is generally performed only in univariate cases and is very complicated in multivariate cases. Given these limitations, further research could focus on developing a conceptual model that is dynamic and sensitive to extreme fluctuations, with multivariable inputs, in order to predict investment risk. The model developed here considered the variables that affect stock price fluctuations as the input data. The combination of VaR–EVT and machine-learning methods is effective in increasing model accuracy because it combines linear and nonlinear models.

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Portfolio Tail Risk: A Multivariate Extreme Value Theory Approach

Cited by 3 publications

References 59 publications

Extreme value analysis of the number of student absences in Jiangsu, China: Based on extreme value theory

Extreme value analysis of the number of student absences in Jiangsu, China: Based on extreme value theory

Extreme Value Theory in Application to Delivery Delays

A Conceptual Model of Investment-Risk Prediction in the Stock Market Using Extreme Value Theory with Machine Learning: A Semisystematic Literature Review

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