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Economic Analysis: Theory and Practice
 

Analysis and forecast for gas demand using the VARMA model

Vol. 20, Iss. 10, OCTOBER 2021

Received: 8 July 2021

Received in revised form: 20 July 2021

Accepted: 7 August 2021

Available online: 29 October 2021

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: C01, C51

Pages: 1973–1990

https://doi.org/10.24891/ea.20.10.1973

Tat'yana Yu. KUDRYAVTSEVA Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation
tankud28@mail.ru

https://orcid.org/0000-0003-1403-3447

Evgeniya A. KOZLOVA Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation
kozlovaevgeiya@list.ru

ORCID id: not available

Subject. The article assesses the fluctuations in market demand for gas to ensure market flexibility.
Objectives. The aim is to develop a methodology for forming an optimal capacity portfolio of oil and gas industry enterprises, taking into account the specifics of the industry to ensure market flexibility, and its testing, on the case of forming a capacity portfolio for three market zones (NCG – Germany, GASPOOL and AVTP – Austria), using the VARMA model.
Methods. We employ methods of financial econometrics and statistics.
Results. The study reveals the specifics of the state of the European gas market, considers the prerequisites for ensuring flexibility, and unveils the main tools to ensure the required level of flexibility. It also offers and tests an algorithm, based on the VARMA model, for assessing and forecasting gas demand in three market zones. We substantiate the need for determining the degree of interrelation between the indices of complementary trade zones, and consider models enabling to assess interrelationships and modeling of projected prices and demand in the future.
Conclusions. The findings may be used for integration of capacities into the capacity generation model, in order to calculate a portfolio of gas transportation and storage capacities, which will allow providing predicted fluctuations in gas demand without risk.

Keywords: VARMA model, high-frequency data, demand modeling, demand forecasting, market flexibility

References:

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ISSN 2311-8725 (Online)
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