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Article

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

1
Group Research and Macro Strategy, Amundi SGR, 20121 Milan, Italy
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Mathematical Finance, Technical University of Munich, 85748 Garching, Germany
3
Cross Asset Research, Amundi SGR, 20121 Milan, Italy
4
Multi Asset Balanced, Income and Real Returns Solution, Amundi SGR, 20121 Milan, Italy
*
Author to whom correspondence should be addressed.
Received: 13 December 2020 / Revised: 24 January 2021 / Accepted: 2 February 2021 / Published: 8 February 2021
(This article belongs to the Special Issue New Frontiers in Forecasting the Business Cycle and Financial Markets)
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. View Full-Text
Keywords: approximate dynamic factor model; expectation-maximization algorithm; forecasting; incomplete data; mixed-frequency information; prediction interval; trading strategy; vector autoregression approximate dynamic factor model; expectation-maximization algorithm; forecasting; incomplete data; mixed-frequency information; prediction interval; trading strategy; vector autoregression
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MDPI and ACS Style

Defend, M.; Min, A.; Portelli, L.; Ramsauer, F.; Sandrini, F.; Zagst, R. Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data. Forecasting 2021, 3, 56-90. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010005

AMA Style

Defend M, Min A, Portelli L, Ramsauer F, Sandrini F, Zagst R. Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data. Forecasting. 2021; 3(1):56-90. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010005

Chicago/Turabian Style

Defend, Monica, Aleksey Min, Lorenzo Portelli, Franz Ramsauer, Francesco Sandrini, and Rudi Zagst. 2021. "Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data" Forecasting 3, no. 1: 56-90. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3010005

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