Rice, Gregory and Wirjanto, Tony and Zhao, Yuqian (2021): Exploring volatility of crude oil intra-day return curves: a functional GARCH-X model.
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Abstract
Crude oil intra-day return curves collected from the commodity futures market often appear to be serially uncorrelated and long-range dependent. Existing functional GARCH models, while able to accommodate short range conditional heteroscedasticity, are not designed to capture long-range dependence. We propose and study a new functional GARCH-X model for this purpose, where the covariate X is chosen to be weakly stationary and long-range dependent. Functional analogs of autocorrelation coefficients of squared processes for this model are derived, and compared to those estimated from crude oil return curves. The results show that the FGARCH-X model provides a significant correction to existing functional volatility models in terms of an in-sample fitting, while its out-of-sample performances do not appear to be more superior than those of the existing functional GARCH models.
Item Type: | MPRA Paper |
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Original Title: | Exploring volatility of crude oil intra-day return curves: a functional GARCH-X model |
English Title: | Exploring volatility of crude oil intra-day return curves: a functional GARCH-X model |
Language: | English |
Keywords: | Crude oil intra-day return curves, volatility modeling and forecasting, functional GARCH-X model, long-range dependence, basis selection |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics G - Financial Economics > G1 - General Financial Markets > G10 - General G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation |
Item ID: | 109423 |
Depositing User: | Dr Yuqian Zhao |
Date Deposited: | 29 Aug 2021 17:23 |
Last Modified: | 29 Aug 2021 17:23 |
References: | Andersen, T. G., Bollerslev, T. (1997). Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns. The Journal of Finance 52, 975-1005. Aue, A., Horvath, L., Pellatt, D. (2017).Functional generalized autoregressive conditional heteroskedasticity. Journal of Time Series Analysis 38, 3-21. Barndorff-Nielsen, O. E., Shephard, N. (2006).Econometrics of testing for jumps in financial economics using bipower variation. Journal of financial Econometrics 4, 1-30. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307-327. Bollerslev, T., Patton, A. J., Quaedvlieg, R. (2016). Exploiting the errors: A simple approach for improved volatility forecasting. Journal of Econometrics 192}, 1-18. Casas, I., Gao, J. (2008). Econometric estimation in long-range dependent volatility models: Theory and practice. Journal of Econometrics 147, 72-83. Cerovecki, C., Francq, C., Hormann, S., Zakoian, J. (2019).Functional GARCH models: the quasi-likelihood approach and its applications. Journal of Econometrics 209, 353-375. Charles, A., Darne, O. (2014). Volatility persistence in crude oil markets. Energy policy 65, 729-742. Christensen, K., Podolskij, M. (2007). Realized range-based estimation of integrated variance. Journal of Econometrics 141, 323-349. Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics 7, 174-196. Didericksen, D., Kokoszka, P., Zhang, X. (2012).Empirical properties of forecasts with the functional autoregressive model. Computational Statistics 27, 285-298. Ding, Z., Granger, C. W., Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of empirical finance 1, 83-106. Engle, R. (2002). New frontiers for ARCH models. Journal of Applied Econometrics 17, 425-446. Fink, H., Fuest, A., Port, H. (2018).The impact of sovereign yield curve differentials on value-at-risk forecasts for foreign exchange rates. Risks 6, 84. Francq, C., Zakoian, J. (2010). GARCH Models: Structure, Statistical Inference and Financial Applications. Wiley. Fuertes, A. M., Izzeldin, M., Kalotychou, E. (2009). On forecasting daily stock volatility: The role of intraday information and market conditions. International Journal of Forecasting 25, 259-281. Gorecki, T., Hormann, S., Horvath, L., Kokoszka, P. (2018). Testing normality of functional time series. Journal of time series analysis 39, 471-487. Gorgi, P., Hansen, P. R., Janus, P., Koopman, S. J. (2019). Realized Wishart-GARCH: A score-driven multi-asset volatility model. Journal of Financial Econometrics 17, 1-32. Han, H. (2015). Asymptotic properties of GARCH-X processes. Journal of Financial Econometrics 13, 188-221. Hansen, P. R., Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?. Journal of applied econometrics, 20}, 873-889. Hansen, P. R., Lunde, A. (2006). Realized variance and market microstructure noise. Journal of Business & Economic Statistics 24, 127-161. Hansen, P. R., Huang, Z., Shek, H. H. (2012). Realized GARCH: a joint model for returns and realized measures of volatility. Journal of Applied Econometrics 27, 877-906. Hormann, S., Horvath, L., Reeder, R. (2013).A functional version of the ARCH model. Econometric Theory 29, 267-288. Horvath, L., Kokoszka, P. (2012). Inference for functional data with applications. Springer. Horvath, L., Kokoszka, P., Rice, G. (2014). Testing stationarity of functional time series. Journal of Econometrics 179, 66-82. Horvath, L., Rice, G., Whipple, S. (2016). Adaptive bandwidth selection in the long run covariance estimator of functional time series. Computational Statistics & Data Analysis 100, 676-693. Kargin, V., Onatski, A. (2008). Curve forecasting by functional autoregression. Journal of Multivariate Analysis 99, 2508-2526. Kearney, F., Shang, H. L. (2019). Uncovering predictability in the evolution of the WTI oil futures curve. European Financial Management, In preprint. Kokoszka, P., Reimherr, M. (2013). Determining the order of the functional autoregressive model. Journal of Time Series Analysis 34, 116-129. Kokoszka, P., Rice, R., Shang, H. L. (2017). Inference for the autocovariance of a functional time series under conditional heteroscedasticity. Journal of Multivariate Analysis 162, 32-50. Li, D., Robinson, P. M., Shang, H. L. (2019) Long-Range Dependent Curve Time Series. Journal of the American Statistical Association, DOI: 10.1080/01621459.2019.1604362 Ma, F., Liao, Y., Zhang, Y., Cao, Y. (2019). Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks. Journal of Empirical Finance 52, 40-55. McLeod, A. I. (1998). Hyperbolic decay time series. Journal of Time Series Analysis 19, 473-483. Ramsay, J. O., Silverman, B. W. (2006). Functional Data Analysis. Wiley Online Library. Rice, G., Wirjanto, T., Zhao, Y. (2020a) Tests for conditional heteroscedasticity of functional data. Journal of Time Series Analysis, In press. Rice, G., Wirjanto, T., Zhao, Y. (2020b) Forecasting Value at Risk via intra-day curves. International Journal of Forecasting, In press. Sigg, C. D., Buhmann, J. M. (2008) Expectation-maximization for sparse and non-negative pca. Proceedings of the 25th International Conference on Machine Learning ICML'08, 960-967. New York, NY, USA, 2008. ACM. Spangenberg, F. (2013) Strictly stationary solutions of ARMA equations in Banach spaces. Journal of Multivariate Analysis 121, 127-138. Sun, H., Yu, B. (2020). Volatility asymmetry in functional threshold GARCH model. Journal of Time Series Analysis 41, 95-109. Wei, Y., Wang, Y., Huang, D. (2010). Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Economics 32, 1477-1484. Zhang, Y. J., Wang, J. L. (2019). Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models. Energy Economics 78}, 192-201. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/109423 |
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Exploring volatility of crude oil intra-day return curves: a functional GARCH-X Model. (deposited 22 Aug 2021 07:10)
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