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Detecting and Measuring Nonlinearity

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  • Rachidi Kotchoni

    (EconomiX-CNRS (UMR7235), Bureau G-517, Université Paris Nanterre, 92000 Nanterre, France)

Abstract

This paper proposes an approach to measure the extent of nonlinearity of the exposure of a financial asset to a given risk factor. The proposed measure exploits the decomposition of a conditional expectation into its linear and nonlinear components. We illustrate the method with the measurement of the degree of nonlinearity of a European style option with respect to the underlying asset. Next, we use the method to identify the empirical patterns of the return-risk trade-off on the SP500. The results are strongly supportive of a nonlinear relationship between expected return and expected volatility. The data seem to be driven by two regimes: one regime with a positive return-risk trade-off and one with a negative trade-off.

Suggested Citation

  • Rachidi Kotchoni, 2018. "Detecting and Measuring Nonlinearity," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    • Handle: RePEc:gam:jecnmx:v:6:y:2018:i:3:p:37-:d:162892
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    References listed on IDEAS

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    Cited by:

    1. Huang, Qiubin & de Haan, Jakob & Scholtens, Bert, 2020. "Does bank capitalization matter for bank stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).

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