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Estimating the market risk of clean energy technologies companies using the expected shortfall approach

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  • Pradhan, Ashis Kumar
  • Tiwari, Aviral Kumar

Abstract

In this study, we assess and estimate the market risk of firms that use clean energy technologies in their production process by using the expected shortfall (ES) regression-based backtest approach and value at risk (VaR) method. We use the WilderHill Clean energy Index from 2001 to 2018 and jointly assess the tail distribution of the risk model. Our findings show that ES forecast results are not misleading during the financial turmoil or during the full sample period. Therefore, our findings indicate that the ES approach can be an alternative valuable diagnostic tool to VaR for the estimation of market risk for financial institutions and regulators.

Suggested Citation

  • Pradhan, Ashis Kumar & Tiwari, Aviral Kumar, 2021. "Estimating the market risk of clean energy technologies companies using the expected shortfall approach," Renewable Energy, Elsevier, vol. 177(C), pages 95-100.
    • Handle: RePEc:eee:renene:v:177:y:2021:i:c:p:95-100
    DOI: 10.1016/j.renene.2021.05.134
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    References listed on IDEAS

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    More about this item

    Keywords

    Financial crises; Clean energy; Risk management; Multi-quantile regression; Forecast validation;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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