Using Autoregressive Logit Models to Forecast the Exceedance Probability for Financial Risk Management

Taylor, James and Yu, Keming (2016) Using Autoregressive Logit Models to Forecast the Exceedance Probability for Financial Risk Management. Journal of the Royal Statistical Society, Series A.

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.

Item Type: Article
Keywords: Asymmetric Laplace distribution; Extreme value theory; Financial risk management; Probability forecasting
Subject(s): Management science
Date Deposited: 12 Jul 2016 12:21
Last Modified: 12 Jul 2017 10:33
Funders: not applicable
URI: http://eureka.sbs.ox.ac.uk/id/eprint/6171

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