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 financial assets. Lead-lag analysis based on the Discrete Wavelet Transform has been proposed as one method for identifying and extracting that predictive information. Unfortunately 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 volatilities. After filtering the long memory components we produce a decomposition of cross-correlation by using wavelet packet methods. A threshold cost functional based on asymptotic confidence intervals was used along with the best basis algorithm in order to select an adaptive frequency partition of the sample 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 recommend further testing in a range of applied contexts.