Modeling Nonstationary Financial Volatility with
the R Package tvgarch

Modeling Nonstationary Financial Volatility with
the R Package tvgarch

tvgarch, financial volatility, nonstationary GARCH models, time-varying parameter models

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.

Susana Campos-Martins

file:///C:/Users/User/Downloads/v108i09.pdf

Catholic University of Portugal
University of Oxford

March 2024

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English

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