Modeling nonstationary financial volatility with the R Package tvgarch

Certain events can make the structure of volatility of financial returns to change, making it nonstationary. Models of time-varying conditional variance such as generalized autoregressive conditional heteroscedasticity (GARCH) models usually assume stationarity. However, this assumption can be inappropriate and volatility predictions can fail in the presence of structural changes in the unconditional variance. To overcome this problem, in the time-varying (TV-)GARCH model, the GARCH parameters are allowed to vary smoothly over time by assuming not only the conditional but also the unconditional variance to be time-varying. In this paper, we show how useful the R package tvgarch (Campos-Martins and Sucarrat 2023) can be for modeling nonstationary volatility in financial empirical applications. The functions for simulating, testing and estimating TV-GARCH-X models, where additional covariates can be included, are implemented in both univariate and multivariate settings.