We use multi-scale analysis and a rolling 250-day window to estimate a widely used standard for empirical asset pricing. The asset pricing model employed is the Fama-French three-factor model. The model is estimated using stock returns for 49 industry stocks of US industry portfolios for the period from July 1969 to September 2017. The rolling window estimation approach allows us to capture the behavior of an investor who periodically reallocates his portfolio. Employing periodic estimates of expected return, we implement a set of long/short investment strategies based on the standard Fama-French three-factor model, and scale versions of the model. We find that during recessions, the higher scale long/short strategies tend to outperform the standard approach. Our results suggest distinct risk dynamics at specific horizons during recessions. We conclude that the information content of the economic phenomena that generate the three-factor model does not follow strict periodicity during recessions, making the wavelet approach more suitable for portfolio managers who must be prepared to rebalance portfolios during official downturns.
Part of the book: Wavelet Theory and Its Applications
Wavelet methodology is employed to investigate the statistical relationship between three well-accepted measures of uncertainty and both market and sector returns. Our primary goal is to determine whether uncertainty is sector specific. Although there are periods when the market works effectively as an oracle capturing uncertainty, we also find sector specific uncertainty. The wavelet equivalent of correlation, coherence, is used to determine the presence of sector specific uncertainty. We find that allowing localized information in the time frequency domain is critical for separating out sector specific uncertainty from market uncertainty.
Part of the book: Wavelet Theory