Please use this identifier to cite or link to this item:
Title: Using autoregressive logit models to forecast the exceedance probability for financial risk management
Authors: Yu, K
Taylor, JW
Keywords: Financial risk management;Probability forecasting;Asymmetric Laplace distribution;Extreme value theory
Issue Date: 2014
Citation: Journal of the Royal Statistical Society, 2014
Abstract: We present new autoregressive logit models for forecasting the probability of a time series of financial asset returns exceeding a threshold. The models can be estimated by maximizing a Bernoulli likelihood. Alternatively, to account for the extent to which an observation does or does not exceed the threshold, we propose that the likelihood is based on the asymmetric Laplace distribution, which has been found to be useful for quantile estimation. We incorporate the exceedance probability forecasts within a new time-varying extreme value approach to value at risk and expected shortfall estimation. We provide empirical illustration using daily stock index data.
Appears in Collections:Dept of Mathematics Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf723.6 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.