Detecting and Measuring Nonlinearity
James "Jim" Melenkevitz PhD
Quantitative Analysis, Data Science, Finance, Advanced Mathematical Methods, Specialized Computations, Software Development, Professor
by Rachidi Kotchoni
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiH3q6l5dvwAhXtQd8KHZ9MB2MQFjAMegQIChAD&url=https%3A%2F%2Fwww.mdpi.com%2F2225-1146%2F6%2F3%2F37%2Fpdf&usg=AOvVaw2izbtLjpPE5suVAlRCytW7
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 theSP500. 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.