Bounds of Adj-TVaR Prediction for Aggregate Risk
Abstract
In financial and insurance industries, risks may come from several sources. It is therefore important to predict future risk by using the concept of aggregate risk. Risk measure prediction plays important role in allocating capital as well as in controlling (and avoiding) worse risk. In this paper, we consider several risk measures such as Value-at-Risk (VaR), Tail VaR (TVaR) and its extension namely Adjusted TVaR (Adj-TVaR). Specifically, we perform an upper bound for such risk measure applied for aggregate risk models. The concept and property of comonotonicity and convex order are utilized to obtain such upper bound.
Keywords: Coherent property, comonotonic rv, convex order, tail property, Value-at-Risk (VaR).References
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DOI: 10.15408/inprime.v1i1.12788
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