Modeling the Dependence of Losses of a Financial Portfolio Using Nested Archimedean Copulas

International Journal of Mathematics and Mathematical Sciences ◽

10.1155/2021/4651044 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Wendkouni Yaméogo ◽

Diakarya Barro

Keyword(s):

At Risk ◽

Stochastic Models ◽

Value At Risk ◽

Financial Analysis ◽

Weibull Model ◽

Potential Outcomes ◽

Archimedean Copulas ◽

Stochastic Behavior ◽

Default Time ◽

Potential Loss

In financial analysis, stochastic models are more and more used to estimate potential outcomes in a risky framework. This paper proposes an approach of modeling the dependence of losses on securities, and the potential loss of the portfolio is divided into sectors each including two subsectors. The Weibull model is used to describe the stochastic behavior of the default time while a nested class of Archimedean copulas at three levels is used to model the maximum of the value at risk of the portfolio.

Download Full-text

Macro-financial analysis of value-at-risk from banking stocks in an Indian scenario

International Journal of Business Continuity and Risk Management ◽

10.1504/ijbcrm.2021.119945 ◽

2021 ◽

Vol 11 (4) ◽

pp. 379

Author(s):

Riyanka Baral ◽

Sankalp Purushottam Naik ◽

Debasis Patnaik

Keyword(s):

At Risk ◽

Value At Risk ◽

Financial Analysis ◽

Indian Scenario

Download Full-text

Asymptotic Value-at-Risk Estimates for Sums of Dependent Random Variables

Astin Bulletin ◽

10.1017/s0515036100013313 ◽

2003 ◽

Vol 33 (1) ◽

pp. 75-92 ◽

Cited By ~ 14

Author(s):

Mario V. Wüthrich

Keyword(s):

At Risk ◽

Asymptotic Behaviour ◽

Value At Risk ◽

Random Variables ◽

Important Application ◽

Archimedean Copulas ◽

Dependent Random Variables ◽

Asymptotic Value ◽

Risk Estimates

We estimate Value-at-Risk for sums of dependent random variables. We model multivariate dependent random variables using archimedean copulas. This structure allows one to calculate the asymptotic behaviour of extremal events. An important application of such results are Value-at-Risk estimates for sums of dependent random variables.

Download Full-text

Macro-financial analysis of value-at-risk from banking stocks in an Indian scenario

International Journal of Business Continuity and Risk Management ◽

10.1504/ijbcrm.2021.10043710 ◽

2021 ◽

Vol 11 (4) ◽

pp. 379

Author(s):

Debasis Patnaik ◽

Riyanka Baral ◽

Sankalp Purushottam Naik

Keyword(s):

At Risk ◽

Value At Risk ◽

Financial Analysis ◽

Indian Scenario

Download Full-text

A New Class of Heavy-Tailed Distributions: Modeling and Simulating Actuarial Measures

Complexity ◽

10.1155/2021/5580228 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Jin Zhao ◽

Zubair Ahmad ◽

Eisa Mahmoudi ◽

E. H. Hafez ◽

Marwa M. Mohie El-Din

Keyword(s):

At Risk ◽

Value At Risk ◽

Weibull Model ◽

Financial Data ◽

Statistical Distributions ◽

New Family ◽

Heavy Tailed Distributions ◽

Beta Power ◽

Heavy Tailed ◽

Modeling Data

Statistical distributions play a prominent role for modeling data in applied fields, particularly in actuarial, financial sciences, and risk management fields. Among the statistical distributions, the heavy-tailed distributions have proven the best choice to use for modeling heavy-tailed financial data. The actuaries are often in search of such types of distributions to provide the best description of the actuarial and financial data. This study presents a new power transformation to introduce a new family of heavy-tailed distributions useful for modeling heavy-tailed financial data. A submodel, namely, heavy-tailed beta-power transformed Weibull model is considered to demonstrate the adequacy of the proposed method. Some actuarial measures such as value at risk, tail value at risk, tail variance, and tail variance premium are calculated. A brief simulation study based on these measures is provided. Finally, an application to the insurance loss dataset is analyzed, which revealed that the proposed distribution is a superior model among the competitors and could potentially be very adequate in describing and modeling actuarial and financial data.

Download Full-text

Overview of the Greek value at risk (VaR) legislation framework

Journal of Financial Regulation and Compliance ◽

10.1108/jfrc-08-2015-0043 ◽

2016 ◽

Vol 24 (2) ◽

pp. 213-226 ◽

Cited By ~ 4

Author(s):

Evangelos Vasileiou

Keyword(s):

At Risk ◽

Value At Risk ◽

Financial Analysis ◽

Estimation Method ◽

Data Input ◽

Data Set ◽

Content Type ◽

Increased Risk ◽

High Volatility ◽

Financial Environment

Purpose The purpose of this paper is to present the Greek value at risk (VaR) legislation framework and to highlight some of its major deficiencies, using not only theoretical scenarios but also empirical evidence. Moreover, this paper does not only highlight the VaR legislation’s framework deficiencies but also suggests legal interventions for its revision and a new-alternative, flexible and simple-to-be-applied filtered estimation method which improves the VaR evaluations. Design/methodology/approach The Greek legislation framework suggests that for the daily VaR to be estimated, a minimum data set of the previous year (250 observations) at the 99 per cent confidence level should be considered. This approach may lead to inaccurate VaR estimations, for example, when after a long-term growth period, there is a sudden recession period, because the data input is not representative to the current financial environment. Taking into serious consideration that high volatility periods are linked to a financial crisis, it is assumed that volatility could be an indicator for the financial environment representation. The conventional historical VaR back-tested results suggest that the specific methodology should be revised, especially during the high volatility period. For the newly suggested filtered VaR, the data sample is divided into several regimes depending on the volatility range. The filtered VaR estimation process applies the conventional historical methodology but uses different historical data input depending on the current volatility. This new approach improves the VaR estimation by reducing the VaR daily violations. Findings The findings regarding the current legislation framework suggest that the financial analysts in Greece have a motivation to adopt a relative VaR approach for risk asset class portfolios (e.g. Greek domestic equity mutual funds), which enables them to bear increased risk without presenting it to the investors. For lower risk portfolios, the absolute VaR may be useful for increased risk bearing strategies. The stricter VaR approaches are preferred to be adopted because stricter VaR estimations are linked to a reduced number of violations. The filtered volatility approach improves the VaR estimations (fewer violations are relative to the conventional approach). Research limitations/implications This methodology is designed to be applied for the VaR estimation, but it could be partly applied in other fields of the financial analysis study. Practical implications The suggested methodology could present efficient VaR estimation without using sophisticated procedures or expensive VaR systems. Therefore, it could be easily applied by the risk analysts. Moreover, the overview of the Greek legislation’s framework could be useful not only for the Greek regulators but also for the authorities in countries with a similar regulation. Originality/value The newly proposed methodology is so accurate and simple to apply that it could have far-reaching impact on practitioners. Finally, this is the first paper that examines the Greek VaR legislation framework in detail.

Download Full-text