Can a Hierarchical Approach Using Interval Information Improve Gasoline Volatility Forecast?

Yao YUE, Qi ZHANG, Yuying SUN, Shouyang WANG

系统科学与信息学报(英文) ›› 2025, Vol. 13 ›› Issue (3) : 325-344.

PDF(487 KB)
PDF(487 KB)
系统科学与信息学报(英文) ›› 2025, Vol. 13 ›› Issue (3) : 325-344. DOI: 10.12012/JSSI-2024-0047

    Yao YUE1,2(Email), Qi ZHANG3,4(Email), Yuying SUN4,5,*(Email), Shouyang WANG4,5(Email)
作者信息 +

Can a Hierarchical Approach Using Interval Information Improve Gasoline Volatility Forecast?

    Yao YUE1,2(Email), Qi ZHANG3,4(Email), Yuying SUN4,5,*(Email), Shouyang WANG4,5(Email)
Author information +
文章历史 +

Abstract

Accurately forecasting gasoline volatility is significant for risk management, economic analysis, and option pricing formulas for future contracts. This study proposes a novel interval-valued hierarchical decomposition and ensemble (IHDE) approach to investigate gasoline price volatility. Our interval-based IHDE method can decompose the complex price process into different components to capture the distinct features of each component, which is helpful for forecasting and analyzing complex price processes. By using interval-valued data, the dynamics of gasoline prices in terms of levels and variations can be fully utilized in this study. Fully utilizing the informational gain of interval-valued data improves forecasting performance. In forecasting weekly gasoline volatility, we document that the proposed IHDE approach outperforms the GARCH, EGARCH, CARR, and ACI models, indicating the importance of capturing features of different frequency components and utilizing the informational gain of interval-valued data for gasoline volatility forecasts.

Key words

volatility forecast / interval-valued time series / variational mode decomposition / ACI model / interval neutral network / interval Holt's model

引用本文

导出引用
Yao YUE, Qi ZHANG, Yuying SUN, Shouyang WANG. . 系统科学与信息学报(英文), 2025, 13(3): 325-344 https://doi.org/10.12012/JSSI-2024-0047
Yao YUE, Qi ZHANG, Yuying SUN, Shouyang WANG. Can a Hierarchical Approach Using Interval Information Improve Gasoline Volatility Forecast?. Journal of Systems Science and Information, 2025, 13(3): 325-344 https://doi.org/10.12012/JSSI-2024-0047

参考文献

1
Kilian L, Zhou X. The impact of rising oil prices on US inflation and inflation expectations in 2020-2023. Energy Economics, 2022, 113: 106228.
2
Baumeister C, Kilian L. Forty years of oil price fluctuations: Why the price of oil may still surprise us. Journal of Economic Perspectives, 2016, 30(1): 139-160.
3
Kilian L, Zhou X. Heterogeneity in the pass-through from oil to gasoline prices: A new instrument for estimating the price elasticity of gasoline demand. Journal of Public Economics, 2024, 232: 105099.
4
Yang H, Tian X, Jin X, et al. A novel hybrid model for gasoline prices forecasting based on Lasso and CNN. Journal of Social Computing, 2022, 3(3): 206-218.
5
Bollerslev T. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 1986, 31(3): 307-327.
6
Nelson D B. Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 1991: 347-370.
7
Segnon M, Gupta R, Wilfling B. Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks. International Journal of Forecasting, 2024, 40(1): 29-43.
8
Shi Y. Modeling and forecasting volatilities of financial assets with an asymmetric zero-drift GARCH model. Journal of Financial Econometrics, 2023, 21(4): 1308-1345.
9
Song Y, Tang X, Wang H, et al. Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH-MIDAS and deep learning models. Journal of Forecasting, 2023, 42(1): 51-59.
10
Afuecheta E, Okorie I E, Nadarajah S, et al. Forecasting value at risk and expected shortfall of foreign exchange rate volatility of major african currencies via GARCH and dynamic conditional correlation analysis. Computational Economics, 2024, 63(1): 271-304.
11
You Y, Liu X. Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach. Journal of Banking & Finance, 2020, 116: 105849.
12
Tian S, Hamori S. Modeling interest rate volatility: A realized GARCH approach. Journal of Banking & Finance, 2015, 61: 158-171.
13
Aras S. Stacking hybrid GARCH models for forecasting Bitcoin volatility. Expert Systems with Applications, 2021, 174: 114747.
14
Zhang C, Ma H, Arkorful G B, et al. The impacts of futures trading on volatility and volatility asymmetry of Bitcoin returns. International Review of Financial Analysis, 2023, 86: 102497.
15
Zhang Y J, Zhang H. Volatility forecasting of crude oil market: Which structural change based GARCH models have better performance? The Energy Journal, 2023, 44(1): 175-194.
16
Lin Y, Xiao Y, Li F. Forecasting crude oil price volatility via a HM-EGARCH model. Energy Economics, 2020, 87: 104693.
17
Parkinson M. The extreme value method for estimating the variance of the rate of return. Journal of Business, 1980: 61-65.
18
Alizadeh S, Brandt M W, Diebold F X. Range-based estimation of stochastic volatility models. The Journal of Finance, 2002, 57(3): 1047-1091.
19
Chou R Y. Forecasting financial volatilities with extreme values: The conditional autoregressive range (CARR) model. Journal of Money, Credit and Banking, 2005: 561-582.
20
Yang W, Han A, Hong Y, et al. Analysis of crisis impact on crude oil prices: A new approach with interval time series modelling. Quantitative Finance, 2016, 16(12): 1917-1928.
21
Neto E A L, De Carvalho F D A T. Centre and range method for fitting a linear regression model to symbolic interval data. Computational Statistics & Data Analysis, 2008, 52(3): 1500-1515.
22
Han A, Hong Y, Lai K K, et al. Interval time series analysis with an application to the sterling-dollar exchange rate. Journal of Systems Science and Complexity, 2008, 21(4): 558-573.
23
Teles P, Brito P. Modeling interval time series with space-time processes. Communications in Statistics-Theory and Methods, 2015, 44(17): 3599-3627.
24
Lin W, González-Rivera G. Interval-valued time series models: Estimation based on order statistics exploring the agriculture marketing service data. Computational Statistics & Data Analysis, 2016, 100: 694-711.
25
Sun Y, Han A, Hong Y, et al. Threshold autoregressive models for interval-valued time series data. Journal of Econometrics, 2018, 206(2): 414-446.
26
Qiao K, Liu Z, Huang B, et al. Brexit and its impact on the US stock market. Journal of Systems Science and Complexity, 2021, 34(3): 1044-1062.
27
Mate C, Jimeńez L. Forecasting exchange rates with the iMLP: New empirical insight on one multi-layer perceptron for interval time series (ITS). Engineering Applications of Artificial Intelligence, 2021, 104: 104358.
28
Lu Q, Sun Y, Hong Y, et al. Forecasting interval-valued crude oil prices using asymmetric interval models. Quantitative Finance, 2022, 22(11): 2047-2061.
29
Sun Y, Zhang X, Wan A T K, et al. Model averaging for interval-valued data. European Journal of Operational Research, 2022, 301(2): 772-784.
30
Zhong W, Qian C, Liu W, et al. Feature screening for interval-valued response with application to study association between posted salary and required skills. Journal of the American Statistical Association, 2023, 118(542): 805-817.
31
Shen T, Tao Z, Chen H. Exploring long-memory process in the prediction of interval-valued financial time series and its application. Journal of Systems Science and Complexity, 2024, 37(2): 759-775.
32
He Y, Han A, Hong Y, et al. Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models. Econometric Reviews, 2021, 40(6): 584-606.
33
González-Rivera G, Lin W. Constrained regression for interval-valued data. Journal of Business & Economic Statistics, 2013, 31(4): 473-490.
34
Sun S, Sun Y, Wang S, et al. Interval decomposition ensemble approach for crude oil price forecasting. Energy Economics, 2018, 76: 274-287.
35
Yang D, Guo J, Sun S, et al. An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting. Applied Energy, 2022, 306: 117992.
36
Zhu B, Wan C, Wang P. Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach. Energy Economics, 2022, 115: 106361.
37
Zheng L, Sun Y, Wang S. A novel interval-based hybrid framework for crude oil price forecasting and trading. Energy Economics, 2024, 130: 107266.
38
ur Rehman N, Aftab H. Multivariate variational mode decomposition. IEEE Transactions on Signal Processing, 2019, 67(23): 6039-6052.
39
Maia A L S, de Carvalho F A T. Holt's exponential smoothing and neural network models for forecasting interval-valued time series. International Journal of Forecasting, 2011, 27(3): 740-759.
40
Engle R F, Ghysels E, Sohn B. Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 2013, 95(3): 776-797.
41
Han A, Hong Y, Wang S, et al. A vector autoregressive moving average model for interval-valued time series data. Essays in honor of Aman Ullah. Emerald Group Publishing Limited, 2016: 417-460.
42
Zhang G P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 2003, 50: 159-175.
43
Kang S H, Yoon S M. Modeling and forecasting the volatility of petroleum futures prices. Energy Economics, 2013, 36: 354-362.
44
Brandt M W, Jones C S. Volatility forecasting with range-based EGARCH models. Journal of Business & Economic Statistics, 2006, 24(4): 470-486.
45
Zhu M, Hong Y, Wang S. Can interval data improve volatility forecasts? Evidence from foreign exchange markets. Evidence From Foreign Exchange Markets, 2024. Available at SSRN: https://ssrn.com/abstract=4785170.
PDF(487 KB)

830

Accesses

0

Citation

Detail

段落导航
相关文章

/