Author details

are significantly linked and the amount of variation explained is high. This means if we allow information from the previous 1.3–2.6 years at scales "d4"and "s4" to be relevant, the proportion of credit spreads explained by risk factors is higher. At the short horizon, technical trading is perceived to be the most important influence in forming expectations; therefore, the insignificance of the default rate to explain credit spreads for shorter time period is in line with

We conclude that aggregating over time scales "d1" to "s4" results in misleading interpretations of the influence of the various risk factors in explaining credit spreads. Only at time scales that represent medium terms, the default rate is of significant, positive influence. The amount of variation explainable with the fundamental risk factors is highest at that time scales. This supports the fact that fundamental considerations are more important in longer time periods

In this chapter, we give an overview of factor models that are applied to major capital markets. Ross' arbitrage pricing theory is chosen as the theoretical background for the stock and bond markets, since it allows to test for significant risk factors even if there are non-stationary features present in the data. In case of the corporate bond markets, Merton's approach is used to motivate which fundamental factors are chosen to explain market observations. We argue that the assumptions made in standard econometric procedures to test for significantly evaluated risk factors are responsible for the failure of finding the risk factors explain a higher proportion of developments on those markets in practice. We use the maximal overlap discrete wavelet transform to decompose the data into their time-scale components to allow for inefficiencies on capital markets and to allow for different time periods for adjustments to new information. The decomposition of the time series with wavelets in the time domain enables us to interpret data having features at different investment periods. This way we analyze the influence of various variables at different time scales. We examine the significance of risk factors and evaluate the proportion of variation explained at various time scales and find that fundamental factors are especially significant at longer time periods. Wavelet application allows for a thorough discrimination of various time horizons. The analysis is performed by the author for all major capital markets and we present new empirical research with regards to the European corporate bond market in detail as an example. A high percentage of variation in credit spreads explained by fundamental factors can be found in the medium terms (1.3– 2.6 years) for investment grade and high yield corporates. We conclude that the adjustment time period to new information is crucial for explaining the credit spreads by risk factors. Aggregating over the time scales veils the fact that a higher proportion in variation of credit spreads is explainable with the fundamental factors for the medium term and that the short term is driven by other factors. These findings confirm our previous findings for major capital markets where estimation and identification of significant fundamental risk factors improved

and that inefficiencies in the credit markets are present at shorter time periods.

when the analyses were done on a scale-by-scale basis.

previous results and market data.

78 Wavelet Theory and Its Applications

5. Conclusion

Michaela M. Kiermeier

Address all correspondence to: michaela.kiermeier@h-da.de

Department of Economics and Business Administration, University of Applied Sciences, Darmstadt, Germany
