Analyzing Oil Futures with a Dynamic Nelson‐Siegel Model

Grønborg, Niels S.; Lunde, Asger

doi:10.1002/fut.21713

Cited by 24 publications

(13 citation statements)

References 33 publications

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“…Guo et al [58] used the Nelson-Siegel model to model the term structure of implied volatility. GrØnborg and Lunde [59] used it to model the term structure of future oil contracts and forecast the prices of these contracts, while West [60] determined the future price of agricultural commodities. In particular, the CDS term structure has a strong relationship with the interest rate term structure.…”

Section: Nelson-siegel Model Nelson and Siegelmentioning

confidence: 99%

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

2020

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In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. Furthermore, we evaluate the change in the forecasting performance of the models through a subperiod analysis. According to the empirical results, we confirm that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure. Additionally, we demonstrate that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods (in this case, the Nelson–Siegel model). Among the machine learning approaches, GMDH demonstrates the best performance in forecasting the CDS term structure. According to the subperiod analysis, the performance of all models was inconsistent with the data period. All the models were less predictable in highly volatile data periods than in less volatile periods. This study will enable traders and policymakers to invest efficiently and make policy decisions based on the current and future risk factors of a company or country.

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Section: Nelson-siegel Model Nelson and Siegelmentioning

confidence: 99%

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

2020

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“…The similarities has been discussed in detail by (Grønborg and Lunde, 2015), who compare the five stylized facts about government bonds yield curves (Diebold and Li, 2006) to the stylized facts about crude oil term structures. The discussion is important as we rely on the dynamic Nelson-Siegel approach (Diebold and Li, 2006) for modeling term structure.…”

Section: Stylized Facts About Term Structurementioning

confidence: 99%

“…6 The most successful approach used in the recent literature to model and forecast yield curves has been introduced by Diebold and Li (2006). The model is a dynamic representation of Nelson-Siegel model (Nelson and Siegel, 1987), and has been recently used in the crude oil markets successfully by Grønborg and Lunde (2015). Contrary to affine general equilibrium models, which assume concrete functional relationship for yield curve, this class of models does not stem from any theoretical grounds and is based only on parametrization of curve shapes.…”

Section: Modeling the Term Structurementioning

confidence: 99%

“…7 Result of the optimization is in line with reviewed literature. Grønborg and Lunde (2015), who analyzed oil futures (although in different period) arrived to λ equal to 0.005.…”

Section: Decay Parametermentioning

confidence: 99%

“…Almansour (2014) model the futures term structure of crude oil and natural gas markets with switching regimes, and Heidorn et al (2015) regress futures curve factors extracted from dynamic Nelson-Siegel model on fundamental and financial traders. While dynamic Nelson-Siegel model explains the dynamics of factors underlying term structure of commodity prices, literature is silent about the future predictions with only exception of Grønborg and Lunde (2015). In their original work, Diebold and Li (2006) propose to use a simple autoregressive time series models to successfully forecast the dynamics of term structure factors, and hence prices in the interest rates market.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the term structure of crude oil futures prices with neural networks

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Malinská

2016

Applied Energy

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The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic NelsonSiegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month, 3-month, 6-month and 12-month-ahead forecasts obtained from focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.

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Dynamics in the VIX complex

Posselt

2021

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This paper provides a characterization of the dynamic interactions in the Volatility Index (VIX) complex, composed of the VIX itself, the term structure of VIX futures, and VIX exchange-traded products (ETPs). I investigate a model that summarizes the VIX futures term structure using latent factors (level, slope, and curvature) and expand it with the VIX and VIX futures demand stemming from VIX ETPs. I find evidence of VIX ETPs impacting the VIX futures term structure, but no evidence of any impacts on the VIX.

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Analyzing Oil Futures with a Dynamic Nelson‐Siegel Model

Cited by 24 publications

References 33 publications

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

Forecasting the term structure of crude oil futures prices with neural networks

Dynamics in the VIX complex

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