A method for predicting VaR by aggregating generalized distributions driven by the dynamic conditional score
نویسندگان
چکیده
Constructing a more effective value at risk (VaR) prediction model has long been goal in financial management. In this paper, we propose novel parametric approach and provide standard paradigm to demonstrate the modeling. We establish dynamic conditional score (DCS) based on high-frequency data generalized distribution (GD), namely, GD-DCS model, improve forecasts of daily VaR. The assumes that intraday returns different moments are independent each other obey same kind GD, whose parameters driven by DCS. By predicting motion law time-varying parameters, is determined; then, bootstrap method used simulate returns. An empirical analysis using from Chinese stock market shows Weibull-Pareto -DCS incorporating superior traditional benchmark models, such as RGARCH, VaR high levels, which proves contributes improvement measurement tools.
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ژورنال
عنوان ژورنال: The Quarterly Review of Economics and Finance
سال: 2023
ISSN: ['1878-4259', '1062-9769']
DOI: https://doi.org/10.1016/j.qref.2023.01.006