Next Article in Journal
Econometrics Best Paper Award 2018
Previous Article in Journal
The Relation between Monetary Policy and the Stock Market in Europe
Open AccessArticle

Detecting and Measuring Nonlinearity

EconomiX-CNRS (UMR7235), Bureau G-517, Université Paris Nanterre, 92000 Nanterre, France
Econometrics 2018, 6(3), 37;
Received: 28 January 2018 / Revised: 18 March 2018 / Accepted: 2 August 2018 / Published: 9 August 2018
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. View Full-Text
Keywords: conditional expectation; nonlinearity; orthogonal polynomials; return-risk trade-off conditional expectation; nonlinearity; orthogonal polynomials; return-risk trade-off
Show Figures

Figure 1

MDPI and ACS Style

Kotchoni, R. Detecting and Measuring Nonlinearity. Econometrics 2018, 6, 37.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop