Tail Conditional Expectations Based on Kumaraswamy Dispersion Models

UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
Ghosh, Indranil (Creator)
The University of North Carolina Wilmington (UNCW )
Web Site: http://library.uncw.edu/

Abstract: Recently, there seems to be an increasing amount of interest in the use of the tail conditional expectation (TCE) as a useful measure of risk associated with a production process, for example, in the measurement of risk associated with stock returns corresponding to the manufacturing industry, such as the production of electric bulbs, investment in housing development, and financial institutions offering loans to small-scale industries. Companies typically face three types of risk (and associated losses from each of these sources): strategic (S); operational (O); and financial (F) (insurance companies additionally face insurance risks) and they come from multiple sources. For asymmetric and bounded losses (properly adjusted as necessary) that are continuous in nature, we conjecture that risk assessment measures via univariate/bivariate Kumaraswamy distribution will be efficient in the sense that the resulting TCE based on bivariate Kumaraswamy type copulas do not depend on the marginals. In fact, almost all classical measures of tail dependence are such, but they investigate the amount of tail dependence along the main diagonal of copulas, which has often little in common with the concentration of extremes in the copula’s domain of definition. In this article, we examined the above risk measure in the case of a univariate and bivariate Kumaraswamy (KW) portfolio risk, and computed TCE based on bivariate KW type copulas. For illustrative purposes, a well-known Stock indices data set was re-analyzed by computing TCE for the bivariate KW type copulas to determine which pairs produce minimum risk in a two-component risk scenario.

Additional Information

Language: English
Date: 2021
bounded risk, tail value-at-risk, asymmetric losses, tail conditional expectations, bivariate Kumaraswamy distribution, bivariate Kumaraswamy type copulas, copula-based tail conditional expectation

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