1,721,002 research outputs found
Testing for Common Trends in Nonstationary Large Datasets
Full text linkWe propose a testing-based procedure to determine the number of common trends in a large nonstationary dataset. Our procedure is based on a factor representation, where we determine whether there are (and how many) common factors (i) with linear trends, and (ii) with stochastic trends. Cointegration among the factors is also permitted. Our analysis is based on the fact that those largest eigenvalues of a suitably scaled covariance matrix of the data corresponding to the common factor part diverge, as the dimension N of the dataset diverges, whilst the others stay bounded. Therefore, we propose a class of randomized test statistics for the null that the pth largest eigenvalue diverges, based directly on the estimated eigenvalue. The tests only requires minimal assumptions on the data-generating process. Monte Carlo evidence shows that our procedure has very good finite sample properties, clearly dominating competing approaches when no common trends are present. We illustrate our methodology through an application to the U.S. bond yields with different maturities observed over the last 30 years
Generalized Dynamic Factor Models and Volatilities: Consistency, Rates, and Prediction Intervals
Full text linkVolatilities, 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
On the Stability of Euro Area Money Demand and its Implications for Monetary Policy
No full textWe employ a recent time-varying cointegration test to revisit the usefulness of long-run money demand equations for the ECB, addressing the issue of their instability by means of a model evaluation exercise. Building on the results, we make a twofold contribution. First, we propose a novel stable money demand equation relying on two crucial factors: a speculative motive, represented by domestic and foreign price-earnings ratios, and a precautionary motive, measured by changes in unemployment. Second, we use the model to derive relevant policy implications for the ECB, since excess liquidity looks more useful for forecasting stock market busts than future inflation. Overall, this evidence points to (i) a possible evolution of the monetary pillar in the direction of pursuing financial stability and (ii) the exclusion of a sudden liquidity–driven inflationary burst after the exit from the prolonged period of unconventional monetary measures
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
NETS: Network Estimation for Time Series
No full textWe model a large panel of time series as a vector autoregression where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system has a network representation in terms of a directed graph representing predictive Granger relations and an undirected graph representing contemporaneous partial correlations. A LASSO algorithm called NETS is introduced to estimate the model. We apply the methodology to analyze a panel of volatility measures of 90 blue chips. The model captures an important fraction of total variability, on top of what is explained by volatility factors, and improves out-of-sample forecasting
Sequential testing for structural stability in approximate factor models
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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
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
Multinetwork of international trade: A commodity-specific analysis
No full textWe study the topological properties of the multinetwork of commodity-specific trade relations among world countries over the 1992–2003 period, comparing them with those of the aggregate-trade network, known in the literature as the international-trade network ITN. We show that link-weight distributions of commodity- specific networks are extremely heterogeneous and quasi log normality of aggregate link-weight distribution is generated as a sheer outcome of aggregation. Commodity-specific networks also display average connectiv- ity, clustering, and centrality levels very different from their aggregate counterpart. We also find that ITN complete connectivity is mainly achieved through the presence of many weak links that keep commodity- specific networks together and that the correlation structure existing between topological statistics within each single network is fairly robust and mimics that of the aggregate network. Finally, we employ cross-commodity correlations between link weights to build hierarchies of commodities. Our results suggest that on the top of a relatively time-invariant “intrinsic” taxonomy based on inherent between-commodity similarities, the roles played by different commodities in the ITN have become more and more dissimilar, possibly as the result of an increased trade specialization. Our approach is general and can be used to characterize any multinetwork emerging as a nontrivial aggregation of several interdependent layers
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