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Nonparametric Expectile Regression for Conditional Autoregressive Expected Shortfall Estimation

In: Risk Management Post Financial Crisis: A Period of Monetary Easing

Author

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  • Marcelo Brutti Righi
  • Yi Yang
  • Paulo Sergio Ceretta

Abstract

In this chapter, we estimate the Expected Shortfall (ES) in conditional autoregressive expectile models by using a nonparametric multiple expectile regression via gradient tree boosting. This approach has the advantages generated by the flexibility of not having to rely on data assumptions and avoids the drawbacks and fragilities of a restrictive estimator such as Historical Simulation. We consider distinct specifications for the information sets that produce the ES estimates. The results obtained with simulated and real market data indicate that the proposed approach has good performance, with some distinctions between the specifications.

Suggested Citation

  • Marcelo Brutti Righi & Yi Yang & Paulo Sergio Ceretta, 2014. "Nonparametric Expectile Regression for Conditional Autoregressive Expected Shortfall Estimation," Contemporary Studies in Economic and Financial Analysis, in: Risk Management Post Financial Crisis: A Period of Monetary Easing, volume 96, pages 83-95, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:csefzz:s1569-375920140000096003
    DOI: 10.1108/S1569-375920140000096003
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    Cited by:

    1. Michael Adegoke Ogunlade & Saheed Lekan Gbadamosi & Israel Esan Owolabi & Nnamdi I. Nwulu, 2023. "Noise Measurement, Characterization, and Modeling for Broadband Indoor Power Communication System: A Comprehensive Survey," Energies, MDPI, vol. 16(3), pages 1-26, February.

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