Refine
Year of publication
- 2017 (2) (remove)
Document Type
- Working Paper (2)
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- model uncertainty (2) (remove)
For some time now, structural macroeconomic models used at central banks have been predominantly New Keynesian DSGE models featuring nominal rigidities and forwardlooking decision-making. While these features are widely deemed crucial for policy evaluation exercises, most central banks have added more detailed characterizations of the financial sector to these models following the Great Recession in order to improve their fit to the data and their forecasting performance. We employ a comparative approach to investigate the characteristics of this new generation of New Keynesian DSGE models and document an elevated degree of model uncertainty relative to earlier model generations. Policy transmission is highly heterogeneous across types of financial frictions and monetary policy causes larger effects, on average. The New Keynesian DSGE models we analyze suggest that a simple policy rule robust to model uncertainty involves a weaker response to inflation and the output gap in the presence of financial frictions as compared to earlier generations of such models. Leaning-against-the-wind policies in models of this class estimated for the Euro Area do not lead to substantial gains. With regard to forecasting performance, the inclusion of financial frictions can generate improvements, if conditioned on appropriate data. Looking forward, we argue that model-averaging and embracing alternative modelling paradigms is likely to yield a more robust framework for the conduct of monetary policy.
We theoretically and empirically study large-scale portfolio allocation problems when transaction costs are taken into account in the optimization problem. We show that transaction costs act on the one hand as a turnover penalization and on the other hand as a regularization, which shrinks the covariance matrix. As an empirical framework, we propose a flexible econometric setting for portfolio optimization under transaction costs, which incorporates parameter uncertainty and combines predictive distributions of individual models using optimal prediction pooling. We consider predictive distributions resulting from highfrequency based covariance matrix estimates, daily stochastic volatility factor models and regularized rolling window covariance estimates, among others. Using data capturing several hundred Nasdaq stocks over more than 10 years, we illustrate that transaction cost regularization (even to small extent) is crucial in order to produce allocations with positive Sharpe ratios. We moreover show that performance differences between individual models decline when transaction costs are considered. Nevertheless, it turns out that adaptive mixtures based on high-frequency and low-frequency information yield the highest performance. Portfolio bootstrap reveals that naive 1=N-allocations and global minimum variance allocations (with and without short sales constraints) are significantly outperformed in terms of Sharpe ratios and utility gains.