Premium
Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall
Author(s) -
Scaillet O.
Publication year - 2004
Publication title -
mathematical finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.98
H-Index - 81
eISSN - 1467-9965
pISSN - 0960-1627
DOI - 10.1111/j.0960-1627.2004.00184.x
Subject(s) - expected shortfall , econometrics , estimator , quantile , nonparametric statistics , portfolio , context (archaeology) , economics , portfolio optimization , mathematics , statistics , financial economics , paleontology , biology
We consider a nonparametric method to estimate the expected shortfall—that is, the expected loss on a portfolio of financial assets knowing that the loss is larger than a given quantile. We derive the asymptotic properties of the kernel estimators of the expected shortfall and its first‐order derivative with respect to portfolio allocation in the context of a stationary process satisfying strong mixing conditions. An empirical illustration is given for a portfolio of stocks. Another empirical illustration deals with data on fire insurance losses.