Tail Conditional Expectations Based on Kumaraswamy Dispersion Models

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