Time-varying autoregressive distributed lag model with changing volatility for stress test
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.
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Author's Biography
Leilei Zhou is a quantitative researcher at JPMorgan Chase & Co. He received his PhD from the Department of Applied Mathematics and Statistics, State University of New York at Stony Brook (SBU), in 2018. His doctoral thesis adviser was Professor Wei Zhu. This work is part of his doctoral thesis work. SBU is one of four university centres of the State University of New York system, located on Long Island, neighbouring New York City.
Wei Zhu is a full professor with the Department of Applied Mathematics and Statistics, State University of New York at Stony Brook. She received her PhD from the Department of Biostatistics, University of California at Los Angeles (UCLA), in 1996. Her research interests include Bayesian statistics, cancer modelling, machine learning and quantitative finance. She has published papers in refereed journals such as Nature, PNAS, Journal of the American Statistical Association, the American Statistician, etc.
Citation
Zhou, Leilei and Zhu, Wei (2021, March 1). Time-varying autoregressive distributed lag model with changing volatility for stress test. In the Journal of Risk Management in Financial Institutions, Volume 14, Issue 2. https://doi.org/10.69554/YVCU9899.
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