scholarly journals Copula Models for Equity Portfolio Risk Estimation: A Case Study of Nairobi Securities Exchange

Author(s):  
Maina Stanley Muchiri ◽  
Mung’atu Joseph Kyalo ◽  
Wanjoya Antony Kibera
2015 ◽  
Vol 8 (1) ◽  
pp. 103-124
Author(s):  
Gabriel Gaiduchevici

AbstractThe copula-GARCH approach provides a flexible and versatile method for modeling multivariate time series. In this study we focus on describing the credit risk dependence pattern between real and financial sectors as it is described by two representative iTraxx indices. Multi-stage estimation is used for parametric ARMA-GARCH-copula models. We derive critical values for the parameter estimates using asymptotic, bootstrap and copula sampling methods. The results obtained indicate a positive symmetric dependence structure with statistically significant tail dependence coefficients. Goodness-of-Fit tests indicate which model provides the best fit to data.


2013 ◽  
Vol 17 (1) ◽  
Author(s):  
Frank J. Fabozzi ◽  
Stoyan V. Stoyanov ◽  
Svetlozar T. Rachev

2010 ◽  
Vol 16 (3) ◽  
pp. 213-217 ◽  
Author(s):  
Aleksandar Jovovic ◽  
Zoran Kovacevic ◽  
Dejan Radic ◽  
Dragoslava Stojiljkovic ◽  
Marko Obradovic ◽  
...  

Co-incineration of wastes started more than 20 years ago. In the last 10 years, the use of alternative fuels in the cement industry is continuously increasing. The use of solid wastes in cement kilns is one of the best technologies for a complete and safe destruction of these wastes, due to the fact that there is a simultaneous benefit of destroying wastes and getting the energy. However, particulate matters (PM) and gaseous chemicals emitted from a source into the environment could be directly transmitted to humans through air inhalation. Therefore, for accurate health risk estimation, the emission of pollutants must be determined. In this work, the analysis of the emission of different pollutants when replacing partially the fuel type used in a cement kiln is done. PM, PM10, heavy metals and inorganic pollutants are analyzed. The methods used for sampling and analysis are the standard methods suggested by the EU regulations for stack analysis. Experimental results have shown the encouraging results: in particular clinker characteristics were unmodified, and stack emissions (NOx, SO2 and CO mainly) were in the case of tires, slightly incremented but remaining almost always below the law imposed limits, and in some cases were even decreased.


2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Vitali Alexeev ◽  
Katja Ignatieva ◽  
Thusitha Liyanage

Abstract This paper investigates dependence among insurance claims arising from different lines of business (LoBs). Using bivariate and multivariate portfolios of losses from different LoBs, we analyse the ability of various copulas in conjunction with skewed generalised hyperbolic (GH) marginals to capture the dependence structure between individual insurance risks forming an aggregate risk of the loss portfolio. The general form skewed GH distribution is shown to provide the best fit to univariate loss data. When modelling dependency between LoBs using one-parameter and mixture copula models, we favour models that are capable of generating upper tail dependence, that is, when several LoBs have a strong tendency to exhibit extreme losses simultaneously. We compare the selected models in their ability to quantify risks of multivariate portfolios. By performing an extensive investigation of the in- and out-of-sample Value-at-Risk (VaR) forecasts by analysing VaR exceptions (i.e. observations of realised portfolio value that are greater than the estimated VaR), we demonstrate that the selected models allow to reliably quantify portfolio risk. Our results provide valuable insights with regards to the nature of dependence and fulfils one of the primary objectives of the general insurance providers aiming at assessing total risk of an aggregate portfolio of losses when LoBs are correlated.


2011 ◽  
Author(s):  
Manuela Pulina ◽  
Gary Norman Evans
Keyword(s):  

2019 ◽  
Vol 37 ◽  
pp. 195-202 ◽  
Author(s):  
Gregorio Gecchele ◽  
Federico Orsini ◽  
Massimiliano Gastaldi ◽  
Riccardo Rossi

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