 Recurrent conditional heteroskedasticityTrong‐Nghia Nguyen, Minh‐Ngoc Tran and Robert Kohn Journal of Applied Econometrics, 2022, vol. 37, issue 5, 1031-1054 Abstract: We propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models, to improve both in‐sample analysis and out‐of‐sample forecasting of the traditional conditional heteroskedastic models. In particular, we incorporate auxiliary deterministic processes, governed by recurrent neural networks, into the conditional variance of the traditional conditional heteroskedastic models, for example, GARCH‐type models, to flexibly capture the dynamics of the underlying volatility. RECH models can detect interesting effects in financial volatility overlooked by the existing conditional heteroskedastic models such as the GARCH, GJR, and EGARCH. The new models often have good out‐of‐sample forecasts while still explaining well the stylized facts of financial volatility by retaining the well‐established features of econometric GARCH‐type models. These properties are illustrated through simulation studies and applications to 31 stock indices and exchange rate data. An user‐friendly software package, together with the examples reported in the paper, is available at https://github.com/vbayeslab. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:37:y:2022:i:5:p:1031-1054 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Applied Econometrics is currently edited by M. Hashem Pesaran More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd. |
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