Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/162300 
Year of Publication: 
2017
Series/Report no.: 
Tinbergen Institute Discussion Paper No. 17-034/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
We propose a semiparametric estimator to determine the effects of explanatory variables on the conditional interquantile expectation (IQE) of the random variable of interest, without specifying the conditional distribution of the underlying random variables. IQE is the expected value of the random variable of interest given that its realization lies in an interval between two quantiles, or in an interval that covers the range of the distribution to the left or right of a quantile. Our so-called interquantile expectation regression (IQER) estimator is based on the GMM framework. We derive consistency and the asymptotic distribution of the estimator, and provide a consistent estimator of the asymptotic covariance matrix. Our results apply to stationary and ergodic time series. In a simulation study we show that our asymptotic theory provides an accurate approximation in small samples. We provide an empirical illustration in finance, in which we use the IQER estimator to estimate one-step-ahead daily expected shortfall conditional on previously observed daily, weekly, and monthly aggregated realized measures.
Subjects: 
quantile
interquantile expectation
regression
generalized method of moments
risk management
expected shortfall
JEL: 
C13
C14
C32
C58
G32
Document Type: 
Working Paper

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