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Risk Estimation with a Time-Varying Probability of Zero Returns

Sucarrat, Genaro; Grønneberg, Steffen
Journal article, Peer reviewed
Accepted version
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Available from 2022-06-26 (Locked)
URI
https://hdl.handle.net/11250/2976304
Date
2020
Metadata
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  • Scientific articles [1667]
Original version
Journal of Financial Econometrics. 2020, .   10.1093/jjfinec/nbaa014
Abstract
The probability of an observed financial return being equal to zero is not necessarily zero, or constant. In ordinary models of financial return, however, e.g. ARCH, SV, GAS and continuous-time models, the zero-probability is zero, constant or both, thus frequently resulting in biased risk estimates (volatility, Value-at-Risk, Expected Shortfall, etc.). We propose a new class of models that allows for a time varying zero-probability that can either be stationary or non-stationary. The new class is the natural generalisation of ordinary models of financial return, so ordinary models are nested and obtained as special cases. The main properties (e.g. volatility, skewness, kurtosis, Value-at-Risk, Expected Shortfall) of the new model class are derived as functions of the assumed volatility and zero-probability specifications, and estimation methods are proposed and illustrated. In a comprehensive study of the stocks at New York Stock Exchange (NYSE) we find extensive evidence of time varying zero-probabilities in daily returns, and an out-of-sample experiment shows that corrected risk estimates can provide significantly better forecasts in a large number of instances.
Publisher
Oxford Uni. Press
Journal
Journal of Financial Econometrics

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