A functional time series analysis of forward curves derived from commodity futures

Horváth, Lajos; Liu, Zhenya; Rice, Gregory; Wang, Shixuan

doi:10.1016/j.ijforecast.2019.08.003

Cited by 9 publications

(2 citation statements)

References 57 publications

(74 reference statements)

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“…FAR(1) was later extended to FAR(

p

), under which the autoregressive order,

p

, can be determined via sequential hypothesis testing as seen in Kokoszka and Reimherr (2013). Horváth et al (2020) compared the forecasting performance between FAR(1), FAR(

p

) and the warping FAR model of Chen et al (2019). There exist several extensions of the FAR model.…”

Section: Introductionmentioning

confidence: 99%

Functional time series forecasting: Functional singular spectrum analysis approaches

Trinka,

Haghbin,

Lin Shang

et al. 2023

Stat

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We introduce two novel nonparametric forecasting methods designed for functional time series (FTS), namely, functional singular spectrum analysis (FSSA) recurrent and vector forecasting. Our algorithms rely on extracted signals obtained from the FSSA method and innovative recurrence relations to make predictions. These techniques are model‐free, capable of predicting nonstationary FTS and utilize a computational approach for parameter selection. We also employ a bootstrap algorithm to assess the goodness‐of‐prediction. Through comprehensive evaluations on both simulated and real‐world climate data, we showcase the effectiveness of our techniques compared to various parametric and nonparametric approaches for forecasting nonstationary stochastic processes. Furthermore, we have implemented these methods in the Rfssa R package and developed a shiny web application for interactive exploration of the results.

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“…FAR(1) was later extended to FAR(

p

), under which the autoregressive order,

p

, can be determined via sequential hypothesis testing as seen in Kokoszka and Reimherr (2013). Horváth et al (2020) compared the forecasting performance between FAR(1), FAR(

p

) and the warping FAR model of Chen et al (2019). There exist several extensions of the FAR model.…”

Section: Introductionmentioning

confidence: 99%

Functional time series forecasting: Functional singular spectrum analysis approaches

Trinka,

Haghbin,

Lin Shang

et al. 2023

Stat

View full text Add to dashboard Cite

show abstract

“…Furthermore, Karstanje et al [84] studied futures prices comovements of the most traded commodities with a factor approach relying on the Diebold and Li [85] model; Jablonowski and Schicks [86] introduced a three-factor model based on Heath et al [87] to describe the relationship between gas term structure and temperature forecasts. Finally, Tang et al [88] developed a predictive method based on artificial neural networks and analyzed the impact of Google search data and internet news sentiment on the model's forecasting ability, Li [89] investigated the abilities of GARCH-type discrete-time models and different Poisson jumpdiffusion models to fit NG futures data, while Horváth et al [90] analyzed the forward curves of 24 different commodities with several polynomial interpolation techniques and provided a comparative study of the predictive abilities of methods based on functional autoregressive processes, Diebold and Li, and naïve approaches.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling

Castello

Resta

2023

Energies

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This work studies the term structure dynamics in the natural gas futures market, focusing on the Dutch Title Transfer Facility (TTF) daily futures prices. At first, using the whole dataset, we compared the in-sample fitting performance of three models: the four-factor dynamic Nelson–Siegel–Svensson (4F-DNSS) model, the five-factor dynamic De Rezende–Ferreira (5F-DRF) model, and the B-spline model. Our findings suggest that B-spline is the method that achieves the best in-line fitting results. Then, we turned our attention to forecasting, using data from 20 January 2011 to 13 May 2022 as the training set and the remaining data, from 16 May to 13 June 2022, for day-ahead predictions. In this second part of the work we combined the above mentioned models (4F-DNSS, 5F-DRF and B-spline) with a Nonlinear Autoregressive Neural Network (NAR-NN), asking the NAR-NN to provide parameter tuning. All the models provided accurate out-of-sample prediction; nevertheless, based on extensive statistical tests, we conclude that, as in the previous case, B-spline (combined with an NAR-NN) ensured the best out-of-sample prediction.

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