Journal of Risk and Financial Management

Editorial

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3 pages, 172 KiB

Open AccessEditorial

Extreme Values and Financial Risk

by Stephen Chan and Saralees Nadarajah

J. Risk Financial Manag. 2020, 13(2), 32; https://doi.org/10.3390/jrfm13020032 - 11 Feb 2020

Cited by 1 | Viewed by 2038

Abstract

Since the 2008 financial crisis, modelling of the extreme values of financial risk has become important [...] Full article

(This article belongs to the Special Issue Extreme Values and Financial Risk)

Research

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12 pages, 1248 KiB

Open AccessArticle

Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis

by Carlos A. Dos Santos, Daniele C. T. Granzotto, Vera L. D. Tomazella and Francisco Louzada

J. Risk Financial Manag. 2018, 11(1), 13; https://doi.org/10.3390/jrfm11010013 - 07 Mar 2018

Cited by 2 | Viewed by 3296

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)

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14 pages, 341 KiB

Open AccessArticle

A New Generalization of the Pareto Distribution and Its Application to Insurance Data

by Mohamed E. Ghitany, Emilio Gómez-Déniz and Saralees Nadarajah

J. Risk Financial Manag. 2018, 11(1), 10; https://doi.org/10.3390/jrfm11010010 - 07 Feb 2018

Cited by 13 | Viewed by 8189

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)

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13 pages, 353 KiB

Open AccessArticle

Does the Assumption on Innovation Process Play an Important Role for Filtered Historical Simulation Model?

by Emrah Altun, Huseyin Tatlidil, Gamze Ozel and Saralees Nadarajah

J. Risk Financial Manag. 2018, 11(1), 7; https://doi.org/10.3390/jrfm11010007 - 23 Jan 2018

Cited by 4 | Viewed by 3287

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)

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14 pages, 1291 KiB

Open AccessArticle

Negative Binomial Kumaraswamy-G Cure Rate Regression Model

by Amanda D’Andrea, Ricardo Rocha, Vera Tomazella and Francisco Louzada

J. Risk Financial Manag. 2018, 11(1), 6; https://doi.org/10.3390/jrfm11010006 - 19 Jan 2018

Cited by 5 | Viewed by 3707

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)

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6 pages, 231 KiB

Open AccessArticle

Modified Stieltjes Transform and Generalized Convolutions of Probability Distributions

by Lev B. Klebanov and Rasool Roozegar

J. Risk Financial Manag. 2018, 11(1), 5; https://doi.org/10.3390/jrfm11010005 - 14 Jan 2018

Cited by 1 | Viewed by 3051

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)

463 KiB

Open AccessArticle

The Burr X Pareto Distribution: Properties, Applications and VaR Estimation

by Mustafa Ç. Korkmaz, Emrah Altun, Haitham M. Yousof, Ahmed Z. Afify and Saralees Nadarajah

J. Risk Financial Manag. 2018, 11(1), 1; https://doi.org/10.3390/jrfm11010001 - 21 Dec 2017

Cited by 20 | Viewed by 5008

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)

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278 KiB

Open AccessArticle

Bivariate Kumaraswamy Models via Modified FGM Copulas: Properties and Applications

by Indranil Ghosh

J. Risk Financial Manag. 2017, 10(4), 19; https://doi.org/10.3390/jrfm10040019 - 01 Nov 2017

Cited by 3 | Viewed by 3682

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)

262 KiB

Open AccessArticle

GARCH Modelling of Cryptocurrencies

by Jeffrey Chu, Stephen Chan, Saralees Nadarajah and Joerg Osterrieder

J. Risk Financial Manag. 2017, 10(4), 17; https://doi.org/10.3390/jrfm10040017 - 01 Oct 2017

Cited by 171 | Viewed by 22808

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)

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