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on Econometric Time Series |
| By: | Claudio Morana |
| Abstract: | In the paper a general framework for large scale modeling of macroeconomic and financial time series is introduced. The proposed approach is characterized by simplicity of implementation, performing well independently of persistence and heteroskedasticity properties, accounting for common deterministic and stochastic factors. Monte Carlo results strongly support the proposed methodology, validating its use also for relatively small cross-sectional and temporal samples. |
| Keywords: | long and short memory, structural breaks, common factors, principal components analysis, fractionally integrated heteroskedastic factor vector autoregressive model |
| JEL: | C22 |
| Date: | 2014–05 |
| URL: | http://d.repec.org/n?u=RePEc:mib:wpaper:273&r=ets |
| By: | Jia Chen; Jiti Gao |
| Abstract: | In this paper, we consider a model selection issue in semiparametric panel data models with fixed effects. The modelling framework under investigation can accommodate both nonlinear deterministic trends and cross-sectional dependence. And we consider the so-called “large panels†where both the time series and cross sectional sizes are very large. A penalised profile least squares method with first-stage local linear smoothing is developed to select the significant covariates and estimate the regression coefficients simultaneously. The convergence rate and the oracle property of the resulting semiparametric estimator are established by the joint limit approach. The developed semiparametric model selection methodology is illustrated by two Monte-Carlo simulation studies, where we compare the performance in model selection and estimation of three penalties, i.e., the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD), and the minimax concave penalty (MCP). |
| Keywords: | Cross-sectional dependence, fixed effects, large panel, local linear fitting, penalty function, profile likelihood, semiparametric regression. |
| JEL: | C13 C14 C23 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2014-15&r=ets |
| By: | Kevin Sheppard |
| Abstract: | �We propose a new class of multivariate volatility models utilizing realized measures of asset volatility and covolatility extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional factor loadings. Statistical properties of the model, including conditions that ensure covariance stationary or returns, are established. The model is applied to modeling the conditional covariance data of large U.S. financial institutions during the financial crisis, where empirical results show that the new model has both superior in- and out-of-sample properties. We show that the superior performance applies to a wide range of quantities of interest, including volatilities, covolatilities, betas and scenario-based risk measures, where the model's performance is particularly strong at short forecast horizons. � |
| Keywords: | Conditional Beta, Conditional Covariance, Forecasting, HEAVY, Marginal Expected Shortfall, Realized Covariance, Realized Kernel, Systematic Risk |
| JEL: | C32 C53 C58 G17 G21 |
| Date: | 2014–05–30 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:710&r=ets |