Special Issue "Extreme Values and Financial Risk"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematical Finance".

Deadline for manuscript submissions: closed (31 December 2017).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Saralees Nadarajah
E-Mail Website
Guest Editor
School of Mathematics, University of Manchester, Manchester M13 9PL, UK
Interests: extreme value theory and its applications; distribution theory; nonparametric statistics; information theory; reliability; sampling theory; statistical software; time series
Special Issues and Collections in MDPI journals
Dr. Stephen Chan
E-Mail Website
Guest Editor
Department of Mathematics and Statistics, American University of Sharjah, UAE
Interests: Extreme Value Analysis and Distribution Theory in analysing financial commodities data and cryptocurrency data, and Financial Risk models
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Since the 2008 financial crisis, modeling of the extreme values of financial risk has become important. Postgraduate programs, as well as PhD research programs, in mathematical finance are cropping up in nearly every university. Additionally, many conferences are being held annually on the topic of extreme financial risk. The aim of this Special Issue is to provide a collection of papers from leading experts in the area of extreme financial risk. The topics covered in this Special Issue will include, but are not limited to:

- Catastrophic risk
- Drought risk
- Flood risk
- Health risk
- Financial risk

Dr. Saralees Nadarajah
Dr. Stephen  Chan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (8 papers)

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Research

Open AccessArticle
Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis
J. Risk Financial Manag. 2018, 11(1), 13; https://doi.org/10.3390/jrfm11010013 - 07 Mar 2018
Abstract
In this study, we propose to apply the transmuted log-logistic (TLL) model which is a generalization of log-logistic model, in a Bayesian context. The log-logistic model has been used it is simple and has a unimodal hazard rate, important characteristic in survival analysis. [...] Read more.
In this study, we propose to apply the transmuted log-logistic (TLL) model which is a generalization of log-logistic model, in a Bayesian context. The log-logistic model has been used it is simple and has a unimodal hazard rate, important characteristic in survival analysis. Also, the TLL model was formulated by using the quadratic transmutation map, that is a simple way of derivating new distributions, and it adds a new parameter λ , which one introduces a skewness in the new distribution and preserves the moments of the baseline model. The Bayesian model was formulated by using the half-Cauchy prior which is an alternative prior to a inverse Gamma distribution. In order to fit the model, a real data set, which consist of the time up to first calving of polled Tabapua race, was used. Finally, after the model was fitted, an influential analysis was made and excluding only 0.1 % of observations (influential points), the reestimated model can fit the data better. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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Open AccessArticle
A New Generalization of the Pareto Distribution and Its Application to Insurance Data
J. Risk Financial Manag. 2018, 11(1), 10; https://doi.org/10.3390/jrfm11010010 - 07 Feb 2018
Cited by 1
Abstract
The Pareto classical distribution is one of the most attractive in statistics and particularly in the scenario of actuarial statistics and finance. For example, it is widely used when calculating reinsurance premiums. In the last years, many alternative distributions have been proposed to [...] Read more.
The Pareto classical distribution is one of the most attractive in statistics and particularly in the scenario of actuarial statistics and finance. For example, it is widely used when calculating reinsurance premiums. In the last years, many alternative distributions have been proposed to obtain better adjustments especially when the tail of the empirical distribution of the data is very long. In this work, an alternative generalization of the Pareto distribution is proposed and its properties are studied. Finally, application of the proposed model to the earthquake insurance data set is presented. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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Open AccessArticle
Does the Assumption on Innovation Process Play an Important Role for Filtered Historical Simulation Model?
J. Risk Financial Manag. 2018, 11(1), 7; https://doi.org/10.3390/jrfm11010007 - 23 Jan 2018
Abstract
Most of the financial institutions compute the Value-at-Risk (VaR) of their trading portfolios using historical simulation-based methods. In this paper, we examine the Filtered Historical Simulation (FHS) model introduced by Barone-Adesi et al. (1999) theoretically and empirically. The main goal of [...] Read more.
Most of the financial institutions compute the Value-at-Risk (VaR) of their trading portfolios using historical simulation-based methods. In this paper, we examine the Filtered Historical Simulation (FHS) model introduced by Barone-Adesi et al. (1999) theoretically and empirically. The main goal of this study is to find an answer for the following question: “Does the assumption on innovation process play an important role for the Filtered Historical Simulation model?”. For this goal, we investigate the performance of FHS model with skewed and fat-tailed innovations distributions such as normal, skew normal, Student’s-t, skew-T, generalized error, and skewed generalized error distributions. The performances of FHS models are evaluated by means of unconditional and conditional likelihood ratio tests and loss functions. Based on the empirical results, we conclude that the FHS models with generalized error and skew-T distributions produce more accurate VaR forecasts. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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Open AccessArticle
Negative Binomial Kumaraswamy-G Cure Rate Regression Model
J. Risk Financial Manag. 2018, 11(1), 6; https://doi.org/10.3390/jrfm11010006 - 19 Jan 2018
Abstract
In survival analysis, the presence of elements not susceptible to the event of interest is very common. These elements lead to what is called a fraction cure, cure rate, or even long-term survivors. In this paper, we propose a unified approach using the [...] Read more.
In survival analysis, the presence of elements not susceptible to the event of interest is very common. These elements lead to what is called a fraction cure, cure rate, or even long-term survivors. In this paper, we propose a unified approach using the negative binomial distribution for modeling cure rates under the Kumaraswamy family of distributions. The estimation is made by maximum likelihood. We checked the maximum likelihood asymptotic properties through some simulation setups. Furthermore, we propose an estimation strategy based on the Negative Binomial Kumaraswamy-G generalized linear model. Finally, we illustrate the distributions proposed using a real data set related to health risk. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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Open AccessArticle
Modified Stieltjes Transform and Generalized Convolutions of Probability Distributions
J. Risk Financial Manag. 2018, 11(1), 5; https://doi.org/10.3390/jrfm11010005 - 14 Jan 2018
Abstract
The classical Stieltjes transform is modified in such a way as to generalize both Stieltjes and Fourier transforms. This transform allows the introduction of new classes of commutative and non-commutative generalized convolutions. A particular case of such a convolution for degenerate distributions appears [...] Read more.
The classical Stieltjes transform is modified in such a way as to generalize both Stieltjes and Fourier transforms. This transform allows the introduction of new classes of commutative and non-commutative generalized convolutions. A particular case of such a convolution for degenerate distributions appears to be the Wigner semicircle distribution. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
Open AccessArticle
The Burr X Pareto Distribution: Properties, Applications and VaR Estimation
J. Risk Financial Manag. 2018, 11(1), 1; https://doi.org/10.3390/jrfm11010001 - 21 Dec 2017
Cited by 2
Abstract
In this paper, a new three-parameter Pareto distribution is introduced and studied. We discuss various mathematical and statistical properties of the new model. Some estimation methods of the model parameters are performed. Moreover, the peaks-over-threshold method is used to estimate Value-at-Risk (VaR) by [...] Read more.
In this paper, a new three-parameter Pareto distribution is introduced and studied. We discuss various mathematical and statistical properties of the new model. Some estimation methods of the model parameters are performed. Moreover, the peaks-over-threshold method is used to estimate Value-at-Risk (VaR) by means of the proposed distribution. We compare the distribution with a few other models to show its versatility in modelling data with heavy tails. VaR estimation with the Burr X Pareto distribution is presented using time series data, and the new model could be considered as an alternative VaR model against the generalized Pareto model for financial institutions. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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Open AccessArticle
Bivariate Kumaraswamy Models via Modified FGM Copulas: Properties and Applications
J. Risk Financial Manag. 2017, 10(4), 19; https://doi.org/10.3390/jrfm10040019 - 01 Nov 2017
Abstract
A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of FGM (Farlie–Gumbel–Morgenstern) bivariate copula for constructing several different bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It [...] Read more.
A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of FGM (Farlie–Gumbel–Morgenstern) bivariate copula for constructing several different bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman’s correlation coefficient, ρ and Kendall’s τ . Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
Open AccessArticle
GARCH Modelling of Cryptocurrencies
J. Risk Financial Manag. 2017, 10(4), 17; https://doi.org/10.3390/jrfm10040017 - 01 Oct 2017
Cited by 39
Abstract
With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are [...] Read more.
With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria. Conclusions are drawn on the best fitting models, forecasts and acceptability of value at risk estimates. Full article
(This article belongs to the Special Issue Extreme Values and Financial Risk) Printed Edition available
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