On the efficient synthesis of short financial time series: A Dynamic Factor Model approach

In this paper, we present a fully data-driven statistical approach to building a synthetic index based on intrinsic information of the considered ecosystem, namely the financial one. Among the several methods made available in the literature, we propose the employment of a Dynamic Factor Model approach which allows us to compare observations at hand in space and time. We contribute to the research field by offering a statistically sound methodology which goes beyond state-of-the-art techniques on dimension reduction, mainly based on Principal Component Analysis. We adopt a country-by-country fitting strategy to elicit the inner country-specific characteristics and then we combine results together by means of a Vector Autoregressive and Kalman filter approach. To this aim, we analyze a set of 17 Financial Soundness Indicators provided by the International Monetary Fund ranging from 2010 to 2020 for 116 countries that span the globe, including both strong and developing economies. Results show that our index is able to identify banking and debt crisis and the contribution of the latent variables can isolate countries that experienced crisis, representing a valid aid to policy makers and institutions in understanding countries movements, reactions and suffering periods.