Identification of Global and National Shocks in International Financial Markets Via General Dynamic Factor Models

2017 ◽  
Author(s):  
Matteo Barigozzi ◽  
Marc Hallin ◽  
Stefano Soccorsi
Keyword(s):  
Financial Markets ◽  
Factor Models ◽  
International Financial Markets ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
General Dynamic

scholarly journals Identification of Global and Local Shocks in International Financial Markets via General Dynamic Factor Models*

2018 ◽  
Vol 17 (3) ◽  
pp. 462-494 ◽  
Author(s):  
Matteo Barigozzi ◽  
Marc Hallin ◽  
Stefano Soccorsi
Keyword(s):  
Financial Markets ◽  
Sovereign Debt ◽  
Factor Model ◽  
Debt Crisis ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Heterogeneous Effects ◽  
Global And Local ◽  
General Dynamic

AbstractWe employ a two-stage general dynamic factor model to analyze co-movements between returns and between volatilities of stocks from the U.S., European, and Japanese financial markets. We find two common shocks driving the dynamics of volatilities—one global shock and one United States–European shock—and four local shocks driving returns, but no global one. Co-movements in returns and volatilities increased considerably in the period 2007–2012 associated with the Great Financial Crisis and the European Sovereign Debt Crisis. We interpret this finding as the sign of a surge, during crises, of interdependencies across markets, as opposed to contagion. Finally, we introduce a new method for structural analysis in general dynamic factor models which is applied to the identification of volatility shocks via natural timing assumptions. The global shock has homogeneous dynamic effects within each individual market but more heterogeneous effects across them, and is useful for predicting aggregate realized volatilities.

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

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 56-90
Author(s):  
Monica Defend ◽  
Aleksey Min ◽  
Lorenzo Portelli ◽  
Franz Ramsauer ◽  
Francesco Sandrini ◽  
...  
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Estimation Method ◽  
Selection Criterion ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Model Selection Criterion ◽  
Mixed Frequency Data

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.

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