A wavelet-based copula approach for modeling market risk in agricultural commodity markets

We consider the problem of accurate market risk modeling for agricultural commodity products over heterogeneous investment horizons using copulas and wavelet methods. Our results indicate that the degree and structure of the dependence of daily commodity returns on the three market risk factors (federal funds rate, USD/Euro exchange rate, and world stock market fluctuations) vary according to the time scale. Changes in the USD/EUR exchange rate and the stock market index are the dominant risks for agricultural commodity markets. Moreover, the tail dependence on the daily returns of the three market risk factors is also scale-dependent, and frequently asymmetric. Finally, there is evidence to suggest that the application of the wavelet-copula model improves the accuracy of VaR estimates, compared to traditional approaches.