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Leorato, S., Peracchi, F. and Tanase, A.V. (2012) Asymptotically Efficient Estimation of the Conditional Expected Shortfall. Computational Statistics & Data Analysis, 56, 768-784. https://doi.org/10.1016/j.csda.2011.02.020

has been cited by the following article:

• TITLE: Estimation of Conditional Weighted Expected Shortfall under Adjusted Extreme Quantile Autoregression

  AUTHORS: Martin M. Kithinji, Peter N. Mwita, Ananda O. Kube

  KEYWORDS: Exreme Quantile Autoregression, Expected Shortfall, Value at Risk, Coherence, Risk Measurement

  JOURNAL NAME: Journal of Mathematical Finance, Vol.11 No.3, July 14, 2021

  ABSTRACT: In this paper, we present an estimator that improves the well-calibrated coherent risk measure: expected shortfall by restructuring its functional form to incorporate dynamic weights on extreme conditional quantiles used in its definition. Adjusted Extreme Quantile Autoregression will is used in estimating intermediary location measures. Consistency and coherence of the estimator are also proved. The resulting estimator was found to be less conservative compared to the expected shortfall.