Saddlepoint methods for conditional expectations with applications to risk management. (English) Zbl 1392.60024

Kim, Sojung; Kim, Kyoung-Kuk

Bernoulli 23, No. 3, 1481-1517 (2017).

Summary: The paper derives saddlepoint expansions for conditional expectations in the form of \(\mathsf{E}[\overline{X}|\overline{\mathbf{Y}}=\mathbf{a}]\) and \(\mathsf{E}[\overline{X}|\overline{\mathbf{Y}}\geq\mathbf{a}]\) for the sample mean of a continuous random vector \((X,\mathbf{Y}^\top)\) whose joint moment generating function is available. Theses conditional expectations frequently appear in various applications, particularly in quantitative finance and risk management. Using the newly developed saddlepoint expansions, we propose fast and accurate methods to compute the sensitivities of risk measures such as value-at-risk and conditional value-at-risk, and the sensitivities of financial options with respect to a market parameter. Numerical studies are provided for the accuracy verification of the new approximations.

MSC:

60E99	Distribution theory
62P05	Applications of statistics to actuarial sciences and financial mathematics
91B30	Risk theory, insurance (MSC2010)

Keywords:

conditional expectation; risk management; saddlepoint approximation; sensitivity estimation