A1702
Title: Recurrent conditional heteroskedasticity
Authors: Minh-Ngoc Tran - University of Sydney (Australia) [presenting]
Robert Kohn - University of New South Wales (Australia)
Nghia Nguyen Trong - University of Sydney (Australia)
Abstract: A new class of financial volatility models, which we call the REcurrent Conditional Heteroskedastic (RECH) models, is proposed to improve both the in-sample analysis and out-of-sample forecast performance 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, e.g. the GARCH-type models, to flexibly capture the dynamics of the underlying volatility. The 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 structures of the econometric GARCH-type models. These properties are illustrated through simulation studies and applications to four real stock index datasets. A user-friendly software package, together with the examples, is available at github.
