Time-varying autoregressive distributed lag model with changing volatility for stress test

Leilei Zhou and Wei Zhu
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Leilei Zhou: Quantitative Researcher, Department of Applied Mathematics and Statistics at State University of New York at Stony Brook, USA
Wei Zhu: Professor and Graduate Programme Director in the Department of Applied Mathematics and Statistics at the Center for Finance, State University of New York at Stony Brook, USA

Journal of Risk Management in Financial Institutions, 2021, vol. 14, issue 2, 195-208

Abstract: We present a novel time-varying autoregressive distributed lag (TV-ADL) model that allows for changes in both transmission mechanisms and innovation volatilities. The forecasting performance of the TV-ADL model has been substantially improved by removing the unrealistic traditional assumptions of constant volatility and constant inter-variable relationship. Our model is further adapted to stress tests mandated by the US Federal Reserve to generate conditional forecasts of the pre-provision net revenue of financial holding companies with large assets. The improvement of forecasting performance is demonstrated by the significant reduction of out-of-sample forecast errors at different horizons.

Keywords: financial supervision; stress tests; time-varying parameter; forecast; volatilities (search for similar items in EconPapers)
JEL-codes: E5 G2 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:aza:rmfi00:y:2021:v:14:i:2:p:195-208

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