A generalized multiscale analysis of the predictive content of eurodollar implied volatilities

It is widely believed that implied volatilities contains information that would enable prediction of spot volatility for a wide range of ¯nancial assets. Lead- lag analysis based on the Discrete Wavelet Transform has been proposed as one method for identifying and extracting that predictive information. Unfor- tunately this approach can fail to identify periodic components that are not proportional to an increasing dyadic scale. We propose a multiscale analysis of the Eurodollar realized volatility and at-the-money (ATM) implied volatili- ties. After ¯ltering the long memory components we produce a decomposition of cross-correlation by using wavelet packet methods. A threshold cost func- tional based on asymptotic con¯dence intervals was used along with the best basis algorithm in order to select an adaptive frequency partition of the sam- ple cross-correlation. We found substantial evidence that Eurodollar implied volatilities contain predictive information about realized volatilities. Moreover, in our analysis the new technique outperforms the lead-lag analysis based on the nondecimated Discrete Wavelet Transform. Therefore we contend that the proposed technique will improve detection of predictive information and rec- ommend further testing in a range of applied contexts.