197,325 research outputs found
Generalized Dynamic Factor Models and Volatilities Estimation and Forecasting
No full textIn large panels of financial time series with dynamic factor structure on the levels or returns, the volatilities of the common and idiosyncratic components often exhibit strong correlations, indicating that both are exposed to the same market volatility shocks. This suggests, alongside the dynamic factor decomposition of returns, a dynamic factor decomposition of volatilities or volatility proxies. Based on this observation, Barigozzi and Hallin (2016) proposed an entirely non-parametric and model-free two-step general dynamic factor approach which accounts for a joint factor structure of returns and volatilities, and allows for extracting the market volatility shocks. Here, we go one step further, and show how the same two-step approach naturally produces volatility forecasts for the various stocks under study. In an applied exercise, we consider the panel of asset returns of the constituents of the S&P100 index over the period 2000–2009. Numerical results show that the predictors based on our two-step method outperform existing univariate and multivariate GARCH methods, as well as static factor GARCH models, in the prediction of daily high–low range—while avoiding the usual problems associated with the curse of dimensionality
Generalized Dynamic Factor Models and Volatilities: Consistency, Rates, and Prediction Intervals
No full textVolatilities, in high-dimensional panels of economic time series with a dynamic factor structure on the levels or returns, typically also admit a dynamic factor decomposition. We consider a two-stage dynamic factor model method recovering the common and idiosyncratic components of both levels and log-volatilities. Specifically, in a first estimation step, we extract the common and idiosyncratic shocks for the levels, from which a log-volatility proxy is computed. In a second step, we estimate a dynamic factor model, which is equivalent to a multiplicative factor structure for volatilities, for the log- volatility panel. By exploiting this two-stage factor approach, we build one-step-ahead conditional prediction intervals for large n × T panels of returns. Those intervals are based on empirical quantiles, not on conditional variances; they can be either equal- or unequal-tailed. We provide uniform consistency and consistency rates results for the proposed estimators as both n and T tend to infinity. We study the finite-sample properties of our estimators by means of Monte Carlo simulations. Finally, we apply our methodology to a panel of asset returns belonging to the S&P100 index in order to compute one-step-ahead conditional prediction intervals for the period 2006–2013. A comparison with the componentwise GARCH benchmark (which does not take advantage of cross-sectional information) demonstrates the superiority of our approach, which is genuinely multivariate (and high-dimensional), nonparametric, and model-free
A network analysis of the volatility of high-dimensional financial series
No full textInterconnectedness between stocks and firms plays a crucial role in the volatility contagion phenomena that characterize financial crises, and graphs are a natural tool in their analysis. We propose graphical methods for an analysis of volatility interconnections in the Standard & Poor’s 100 data set during the period 2000–2013, which contains the 2007–2008 Great Financial Crisis. The challenges are twofold: first, volatilities are not directly observed and must be extracted from time series of stock returns; second, the observed series, with about 100 stocks, is high dimensional, and curse-of-dimensionality problems are to be faced. To over- come this double challenge, we propose a dynamic factor model methodology, decomposing the panel into a factor-driven and an idiosyncratic component modelled as a sparse vector auto-regressive model. The inversion of this auto-regression, along with suitable identification constraints, produces networks in which, for a given horizon h, the weight associated with edge .i, j/ represents the h-step-ahead forecast error variance of variable i accounted for by variable j’s innovations. Then, we show how those graphs yield an assessment of how systemic each firm is. They also demonstrate the prominent role of financial firms as sources of contagion during the 2007–2008 crisis
Generalized dynamic factor models and volatilities: Recovering the market volatility shocks
No full textDecomposing volatilities into a common market-driven component and an idiosyncratic item-specific component is an important issue in financial econometrics. However, this requires the statistical analysis of large panels of time series, and hence faces the usual challenges associated with high-dimensional data. Factor model methods in such a context are an ideal tool, but they do not readily apply to the analysis of volatilities. Focusing on the reconstruction of the unobserved market shocks and the way they are loaded by the various items (stocks) in the panel, we propose an entirely non-parametric and model-free two-step general dynamic factor approach to the problem, which avoids the usual curse of dimensionality. Applied to the Standard & Poor’s 100 asset return data set, the method provides evidence that a non-negligible proportion of the market-driven volatility of returns originates in the volatilities of the idiosyncratic components of returns
Groupe I/b Rapports sur le mémoire de M. Marc Hallin
No full textGillis Paul P., Nicolis Grégoire, Garnir Henri Georges. Groupe I/b Rapports sur le mémoire de M. Marc Hallin . In: Bulletin de la Classe des sciences, tome 69, 1983. pp. 602-603
The generalized dynamic factor model: One-sided estimation and forecasting
No full textSCOPUS: re.jinfo:eu-repo/semantics/publishe
Dynamic Factors in the Presence of Block Structure
No full textMacroeconometric data often come under the form of large panels of time series, themselves decomposing into smaller but still quite large subpanels or blocks. We show how the dynamic factor analysis method proposed in Forni et al (2000), combined with the identification method of Hallin and Liska (2007), allows for identifying and estimating joint and block-specific common factors. This leads to a more sophisticated analysis of the structures of dynamic interrelations within and between the blocks in such datasets, along with an informative decomposition of explained variances. The method is illustrated with an analysis of the Industrial Production Index data for France, Germany, and Italy.Panel data; Time series; High dimensional data; Dynamic factor model; Business cycle; Block specific factors; Dynamic principal components; Information criterion.
Identification of Global and Local Shocks in International Financial Markets via General Dynamic Factor Models
No full textWe 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
Dynamic Factor Models with Infinite-Dimensional Factor Space: Asymptotic Analysis
No full textFactor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni et al., (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni et al. (2004). Those estimators, however, rely on Brillinger’s concept of dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard (static) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the factors has finite dimension, which severely restricts their generality—prohibiting, for instance, autoregressive factor loadings. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni et al., (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional factor space. A Monte Carlo experiment and an empirical exercise on US macroeconomic data corroborate those theoretical results and demonstrate the excellent performance of those estimators in out-of-sample forecasting
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