Bart Keijsers

We assess the predictive ability of 15 economic uncertainty measures in a real-time out-of-sample forecasting exercise for the quantiles of The Conference Board's coincident economic index and its components (industrial production, employment, personal income, and manufacturing and trade sales). The results show that the measures hold (real-time) predictive power for quantiles in the left tail. Because uncertainty measures are all proxies of an unobserved entity, we combine their information using principal component analysis. A large fraction of the variance of the uncertainty measures can be explained by two factors. First, a general economic uncertainty factor with a slight tilt toward financial conditions. Second, a consumer/media confidence index which remains elevated after recessions. Using a predictive regression model with the factors from the set of uncertainty measures yields more consistent gains compared to a model with an individual uncertainty measure. Further, although often better forecasts are obtained using the National Financial Conditions Index (NFCI), the uncertainty factor models are superior when forecasting employment and in general the uncertainty factors have predictive content that is complementary to the NFCI.

Links: Published version | Working paper (SSRN)

Winner of research grant at Europlace Institute for Finance

Based on unique data we show that macro variables, the default rate and loss given default of bank loans share common cyclical components. The innovation in our model is the distinction between loans with either severe or mild losses. The variation in the proportion of these two types drives the cyclic behavior of the loss given default, and constitutes the links with the default rate and macro variables. These links vary according to loan and borrower characteristics. During downturns, the proportion of defaults with severe losses increases, but the distribution of losses conditional on their being mild or severe does not change. Though loans are monitored more closely than bonds and are more senior, the cyclical variation in their losses resembles those for bonds, albeit around a lower average level. This variation leads to an increase in the capital reserves required for loan portfolios. 

Links: Published version | Working paper (SSRN)

The relationship between excess returns and the dividend price ratio is known to be unstable. However, there is no consensus on the type of instability, i.e. few or many breaks. Differences in parameter instability affect the long-term investor in particular, as misspecification errors are exacerbated as the investment horizon increases. Therefore, we investigate the consequences of different types of break processes for a long-term investor. The break process is inferred with a mixture innovation model using Bayesian methods. This allows us to estimate the break risk and the uncertainty around it. The estimated parameters show substantial instability, with an average break probability of 14.0%. Assuming constant parameters can lead to large losses in certainty equivalent return for the long-term investor, even if the break probability is small in reality. The costs of ignoring uncertainty regarding the instability are smaller, but non-negligible.

Available at SSRN