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Modelling Market Indices, Commodity Market Prices and Stock Prices of Energy Sector using VAR with Variance Decomposition Model

Author

Listed:
  • Bharat Kumar Meher

    (Darshan Sah College, Katihar (Under Purnea University), Bihar, India,)

  • Iqbal Thonse Hawaldar

    (College of Business Administration, Kingdom University, Bahrain)

  • Santosh Kumar

    (Darshan Sah College, Katihar (Under Purnea University), Bihar, India,)

  • Abhishek Kumar Gupta

    (Darshan Sah College, Katihar (Under Purnea University), Bihar, India,)

Abstract

The study aims to examine the existence of a correlation between the stock prices of the energy sector, commodities prices of the energy sector, and market indices. The study uses an empirical approach to develop various VAR (Vector Autoregression) with Variance Decomposition Models for each company under the energy sector indexed in NIFTY50 by considering daily prices for 3 years. For a comparative study, the data have been divided into two parts. The first part is considered pre-COVID era, i.e., from July 1, 2018, to December 31, 2019, and the second part is considered post-COVID era, i.e., from January 1, 2020, to June 30, 2021. While observing the estimates of VAR of different companies, it can be said that crude oil is significant in most of the models during pre-COVID whereas, during post COVID, lag term of crude oil and NIFTYENGERGY are significant. On the other hand, while observing the estimates of variance decomposition in all the VAR models, the first lag term of the particular company's share price is strongly endogenous. In comparison, the other independent variable, i.e., lag term of the price of crude oil and natural gas, values of NIFTY50 and NIFTY ENERGY are strongly exogenous to the stock prices of the energy sector.

Suggested Citation

  • Bharat Kumar Meher & Iqbal Thonse Hawaldar & Santosh Kumar & Abhishek Kumar Gupta, 2022. "Modelling Market Indices, Commodity Market Prices and Stock Prices of Energy Sector using VAR with Variance Decomposition Model," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 122-130, July.
    • Handle: RePEc:eco:journ2:2022-04-16
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    References listed on IDEAS

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    1. Lombardi, Marco J. & Ravazzolo, Francesco, 2016. "On the correlation between commodity and equity returns: Implications for portfolio allocation," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 45-57.
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    4. Bharat Kumar Meher & Iqbal Thonse Hawaldar & Latasha Mohapatra & Adel M. Sarea, 2020. "The Impact of COVID-19 on Price Volatility of Crude Oil and Natural Gas Listed on Multi Commodity Exchange of India," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 422-431.
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    Cited by:

    1. Kumar SANTOSH & Meher Kumar BHARAT & Ramona BIRAU & Mircea Laurentiu SIMION & Anand ABHISHEK & Singh MANOHAR, 2023. "Quantifying Long-Term Volatility for Developed Stock Markets: An Empirical Case Study Using PGARCH Model on Toronto Stock Exchange (TSX)," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-68.
    2. Santosh KUMAR & Bharat Kumar MEHER & Ramona BIRAU & Abhishek ANAND & Mircea Laurentiu SIMION, 2023. "Investigating Volatility Dynamics of the Portugal Stock Market using FIGARCH Models," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 39-45.

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    More about this item

    Keywords

    Vector Autoregression; VAR with Variance Decomposition; Market Index; NIFTY50; Nifty Energy; Commodity Market; COVID;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G1 - Financial Economics - - General Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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