Band 21, Heft 5

Earlier studies in the finance literature show that macroeconomic fundamentals can predict excess bond returns. We employ a multi-level factor model to estimate global and sectoral factors separately and show that (i) the real factors possess most important predictive power existing in the panel; (ii) the financial factors might have some predictive power but less than the real factors; (iii) the inflation factors have almost no predictive power and (iv) the excess bond returns have a countercyclical component.

We consider the determinants of the 2008 crisis and address two main forms of model uncertainty: the uncertainty in selecting theoretical groups and the uncertainty in selecting explanatory variables. We introduce Bayesian hierarchical formulation that allows for the joint treatment of group and variable selection using the Group-wise Gibbs sampler. Our group variable selection shows that pre-crisis financial policies and trade linkages play a particularly important role in explaining the severity of the crisis, alongside institutions, and within the selected groups we identify a broader set of variables correlated with the crisis, which in turn leads to an improvement in prediction performance. In the robustness analysis we also find that our results are not qualitatively changed on alternative measures of crisis intensity, different groupings of variables, or prior assumptions. We further argue that the established results in the literature may well be attributed to different prior choices used in the analysis.

Savin et al. [Savin, G., P. Weller, and J. Zvingelis. 2007. “The Predictive Power of “Head-and-Shoulders” Price Patterns in the US Stock Market.” Journal of Financial Econometrics 5: 243–265.] and Lo et al. [Lo, A. W., H. Mamaysky, and J. Wang. 2000. “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation.” Journal of Finance 55: 1705–1765.] analysed the predictive power of head-and-shoulders (HS) patterns in the U.S. stock market. The algorithms in both studies ignored the relative position of the HS pattern in a price trend. In this paper, a filter that removes invalid HS patterns is proposed. It is found that the risk-adjusted excess returns for the HST pattern generally improve through the use of our filter.

This paper analyzes pass-through from money market rates to consumer retail loan and deposit rates in Canada from 1983 to 2015 using a nonlinear vector error-correction model. This model permits estimation of long-run pass-through coefficients while simultaneously accounting for asymmetric adjustments and short-run dynamics. In contrast to empirical frameworks used in previous studies, it also allows testing of commonly made assumptions such as exogeneity of the market rate, making inference more robust. I find that pass-through was complete for all rates before the financial crisis although only after the mid 1990s for the 1 year mortgage rate. Since the end of the 2008–2009 recession, pass-through remains complete in the mortgage market but has significantly declined for deposit rates. Furthermore, many rates adjust asymmetrically but the direction of rigidity differs among rates and time periods.

Prediction bands for time series are usually generated point-wise by bootstrap methods. Such bands only convey the prediction uncertainty for each horizon separately. The joint distribution is not taken into account. To represent the forecast uncertainty over the entire horizon, methods for constructing joint prediction bands for path forecasts from SETAR models are proposed. This class of nonlinear models is increasingly used in time series analysis and forecasting as it is useful for capturing nonlinear dynamics. Approaches based on statistical theory and explicit sequential and global optimization methods are both considered. Monte Carlo simulation is used to assess the performance of the proposed methods. The comparison is done with regard to the actual coverage of the constructed prediction bands for full path forecasts as well as with regard to the width of the bands. An empirical application demonstrates the relevance of the choice of bands for indicating the uncertainty of path forecasts in nonlinear models.