User: Guest  Login
Document type:
Zeitschriftenaufsatz 
Author(s):
Defend, M.; Min, A.; Portelli, L.; Ramsauer, F.; Sandrini, F. & Zagst, R. 
Non-TUM Co-author(s):
ja 
Cooperation:
international 
Title:
Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data 
Abstract:
This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns. 
Intellectual Contribution:
Discipline-based Research 
Journal title:
Forecasting 
Journal listet in FT50 ranking:
nein 
Year:
2021 
Journal volume:
Pages contribution:
56-90 
Fulltext / DOI:
Notes:
accepted for publication 
Key publication:
Nein 
Peer reviewed:
Ja 
Commissioned:
not commissioned 
Technology:
Nein 
Interdisciplinarity:
Nein 
Mission statement:
Ethics and Sustainability:
Nein 
versions