Econometric Time Series
http://lists.repec.org/mailman/listinfo/nep-ets
Econometric Time Series2014-12-19Yong YinModeling and Forecasting Volatility – How Reliable are modern day approaches?
http://d.repec.org/n?u=RePEc:pra:mprapa:59788&r=ets
This study explores the volatility models and evaluates the quality of one-step ahead forecasts of volatility constructed by (1) GARCH, (2) TGARCH, (3) Risk metrics and (4) Historical volatility. Volatility forecasts suggest that TGARCH performs relatively best in term of MSPE, followed by GARCH, Risk metrics and historical volatility. In terms of VaR, we test for correct unconditional coverage and index- Dependence of violations using Likelihood Ratio tests. The tests suggest that VaR forecasts at 90 % and 95% have desirable properties. Regarding 99% VaR forecasts, We find significant evidence that suggests none of the models can reliably predict at this confidence level.Mehta, Anirudh, Kanishka, Kunal2014-11-08Asset pricing, Volatility Forecasting, GARCH, T-GARCH, Risk metrics, LR ratio, VaRVariable Selection in Predictive MIDAS Models
http://d.repec.org/n?u=RePEc:bfr:banfra:520&r=ets
In short-term forecasting, it is essential to take into account all available information on the current state of the economic activity. Yet, the fact that various time series are sampled at different frequencies prevents an efficient use of available data. In this respect, the Mixed-Data Sampling (MIDAS) model has proved to outperform existing tools by combining data series of different frequencies. However, major issues remain regarding the choice of explanatory variables. The paper first addresses this point by developing MIDAS based dimension reduction techniques and by introducing two novel approaches based on either a method of penalized variable selection or Bayesian stochastic search variable selection. These features integrate a cross-validation procedure that allows automatic in-sample selection based on recent forecasting performances. Then the developed techniques are assessed with regards to their forecasting power of US economic growth during the period 2000-2013 using jointly daily and monthly data. Our model succeeds in identifying leading indicators and constructing an objective variable selection with broad applicability.C. Marsilli2014Forecasting, Mixed frequency data, MIDAS, Variable selection, GDP.Generalized Dynamic Factor Models and Volatilities. Recovering the Market Volatility Shocks
http://d.repec.org/n?u=RePEc:eca:wpaper:2013/177444&r=ets
Matteo Barigozzi, Marc Hallin2014-11volatility; dynamic factor models; block structureNEGLECTED SERIAL CORRELATION TESTS IN UCARIMA MODELS
http://d.repec.org/n?u=RePEc:cmf:wpaper:wp2014_1406&r=ets
We derive computationally simple and intuitive score tests of neglected serial correlation in unobserved component univariate models using frequency domain techniques. In some common situations in which the information matrix is singular under the null we derive extremum tests that are asymptotically equivalent to likelihood ratio tests, which become one-sided, and explain how to compute reliable Wald tests. We also explicitly relate the incidence of those problems to the model identification conditions and compare our tests with tests based on the reduced form prediction errors. Our Monte Carlo exercises assess the finite sample reliability and power of our proposed tests.Gabriele Fiorentini, Enrique Sentana2014-10Extremum tests, Kalman filter, LM tests, singular information matrix, spectral maximum likelihood, Wiener-Kolmogorov filter.Outlier detection in structural time series models: The indicator saturation approach
http://d.repec.org/n?u=RePEc:zbw:fziddp:902014&r=ets
Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has at- tracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general-to-specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit-root autoregressions. By focusing on impulse-and step-indicator saturation, we investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality and a stationary component. Further, we apply both kinds of indicator saturation to detect additive outliers and level shifts in the industrial production series in five European countries.Marczak, Martyna, Proietti, Tommaso2014indicator saturation,seasonal adjustment,structural time series model,outliers,structural change,general-to-specific approach,state space model