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Titel:

Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Defend, M.; Min, A.; Portelli, L.; Ramsauer, F.; Sandrini, F. & Zagst, R.
Nicht-TUM Koautoren:
ja
Kooperation:
international
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
Zeitschriftentitel:
Forecasting
Journal gelistet in FT50 Ranking:
nein
Jahr:
2021
Band / Volume:
3
Seitenangaben Beitrag:
56-90
Volltext / DOI:
doi:10.3390/forecast3010005
WWW:
https://www.mdpi.com/2571-9394/3/1/5%252520
Hinweise:
accepted for publication
Urteilsbesprechung:
0
Key publication:
Nein
Peer reviewed:
Ja
commissioned:
not commissioned
Technology:
Nein
Interdisziplinarität:
Nein
Leitbild:
;
Ethics und Sustainability:
Nein
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