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

Monica Defend (), Aleksey Min (), Lorenzo Portelli (), Franz Ramsauer (), Francesco Sandrini () and Rudi Zagst ()
Additional contact information
Monica Defend: Group Research and Macro Strategy, Amundi SGR, 20121 Milan, Italy
Aleksey Min: Mathematical Finance, Technical University of Munich, 85748 Garching, Germany
Lorenzo Portelli: Cross Asset Research, Amundi SGR, 20121 Milan, Italy
Franz Ramsauer: Mathematical Finance, Technical University of Munich, 85748 Garching, Germany
Francesco Sandrini: Multi Asset Balanced, Income and Real Returns Solution, Amundi SGR, 20121 Milan, Italy
Rudi Zagst: Mathematical Finance, Technical University of Munich, 85748 Garching, Germany

Forecasting, 2021, vol. 3, issue 1, 1-35

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.

Keywords: approximate dynamic factor model; expectation-maximization algorithm; forecasting; incomplete data; mixed-frequency information; prediction interval; trading strategy; vector autoregression (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
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