Factor models are used to explain asset returns on all major capital markets. We argue that standard econometric analyses implicitly assume that the relationships between prices, spreads, and interest rates and their respective risk factors are time-scale independent. Furthermore, by applying wavelet analysis, we do not have to assume capital market efficiency; in fact, we explicitly allow for inefficiencies such as noise trading, dispersed information, technical, feedback, fundamental, and rational trading to allow for typical characteristics of capital market data. We use wavelet analysis to decompose capital markets’ developments, and the risk factors, using the maximal overlap discrete wavelet transform (MODWT). We proceed by estimating the relationships on a scale-by-scale basis. Our respective empirical analyses for stock and bond markets are summarized and new research is presented with regards to European corporate bonds markets. On stock market, this approach finds more stable relationships between risk factors and price movements. On the bond markets, we find empirical evidence for four significantly evaluated factors. For the European corporate bonds market, the results show that the amount of credit spreads explained by risk factors is in fact high for certain time scales only which is similar to the findings for the other capital markets.
Part of the book: Wavelet Theory and Its Applications