knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
Functional time series data derived from financial markets exhibit conditional heteroscedasticity. The goal of 'CurVol' is to document useful functions to analyze the volatility of functional time series data. Methods and tools in this package replicate hypothesis testing, model estimation, and backtesting in a series of papers:
Hormann, S., Horvath, L., Reeder, R. (2013). A functional version of the
ARCH model. Econometric Theory. 29(2), 267-288.
Aue, A., Horvath, L., F. Pellatt, D. (2017). Functional generalized
autoregressive conditional heteroskedasticity. Journal of Time Series
Analysis. 38(1), 3-21.
Cerovecki, C., Francq, C., Hormann, S., Zakoian, J. M. (2019).
Functional GARCH models: The quasi-likelihood approach and its
applications. Journal of Econometrics. 209(2), 353-375.
Rice, G., Wirjanto, T., Zhao, Y. (2020) Tests for conditional heteroscedasticity of functional data. Journal of Time Series Analysis. 41(6), 733-758.
Rice, G., Wirjanto, T., Zhao, Y. (2020) Forecasting Value at Risk via intra-day return curves. International Journal of Forecasting.
Rice, G., Wirjanto, T., Zhao, Y. (2021) Exploring volatility of crude oil intra-day return curves: a functional GARCH-X model. MPRA Paper No.109231. https://mpra.ub.uni-muenchen.de/109231.
You can install the released version of CurVol from CRAN with:
install.packages("CurVol")
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