Econometric Time Series
http://lists.repec.org/mailman/listinfo/nep-ets
Econometric Time Series2015-06-27Yong YinGeneralized Dynamic Factor Models and Volatilities: Estimation and Forecasting
http://d.repec.org/n?u=RePEc:eca:wpaper:2013/200436&r=ets
Matteo Barigozzi, Marc Hallin2015-06volatility; dynamic factor models; GARCH modelsTesting for Level Shifts in Fractionally Integrated Processes: a State Space Approach
http://d.repec.org/n?u=RePEc:aah:create:2015-30&r=ets
Short memory models contaminated by level shifts have similar long-memory features as fractionally integrated processes. This makes hard to verify whether the true data generating process is a pure fractionally integrated process when employing standard estimation methods based on the autocorrelation function or the periodogram. In this paper, we propose a robust testing procedure, based on an encompassing parametric specification that allows to disentangle the level shifts from the fractionally integrated component. The estimation is carried out on the basis of a state-space methodology and it leads to a robust estimate of the fractional integration parameter also in presence of level shifts. Once the memory parameter is correctly estimated, we use the KPSS test for presence of level shift. The Monte Carlo simulations show how this approach produces unbiased estimates of the memory parameter when shifts in the mean, or other slowly varying trends, are present in the data. Therefore, the subsequent robust version of the KPSS test for the presence of level shifts has proper size and by far the highest power compared to other existing tests. Finally, we illustrate the usefulness of the proposed approach on financial data, such as daily bipower variation and turnover.Davide Delle Monache, Stefano Grassi, Paolo Santucci de Magistris2015-06-17Long Memory, ARFIMA Processes, Level Shifts, State-Space methods, KPSS testAre the shocks obtained from SVAR fundamental?
http://d.repec.org/n?u=RePEc:pra:mprapa:65126&r=ets
This paper provides new conditions under which the shocks recovered from the estimates of structural vector autoregressions are fundamental. I prove that the Wold innovations are unpredictable if and only if the model is fundamental. I propose a test based on a generalized spectral density to check the unpredictability of the Wold innovations. The test is applied to study the dynamic effects of government spending on economic activity. I find that standard SVAR models commonly employed in the literature are non-fundamental. Moreover, I formally show that introduction of a narrative variable that measures anticipation restores fundamentalness.Hamidi Sahneh, Mehdi2015-06-15Fundamentalness; Identification; Invertible Moving Average; Vector AutoregressiveSeasonal Stochastic Volatility and Correlation together with the Samuelson Effect in Commodity Futures Markets
http://d.repec.org/n?u=RePEc:arx:papers:1506.05911&r=ets
We introduce a multi-factor stochastic volatility model based on the CIR/Heston volatility process that incorporates seasonality and the Samuelson effect. First, we give conditions on the seasonal term under which the corresponding volatility factor is well-defined. These conditions appear to be rather mild. Second, we calculate the joint characteristic function of two futures prices for different maturities in the proposed model. This characteristic function is analytic. Finally, we provide numerical illustrations in terms of implied volatility and correlation produced by the proposed model with five different specifications of the seasonality pattern. The model is found to be able to produce volatility smiles at the same time as a volatility term-structure that exhibits the Samuelson effect with a seasonal component. Correlation, instantaneous or implied from calendar spread option prices via a Gaussian copula, is also found to be seasonal.Lorenz Schneider, Bertrand Tavin2015-06