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on Econometric Time Series |
| By: | Stefano Grassi (Aarhus University and CREATES); Paolo Santucci de Magistris (Aarhus University and CREATES) |
| Abstract: | The persistent nature of equity volatility is investigated by means of a multi-factor stochastic volatility model with time varying parameters. The parameters are estimated by means of a sequential indirect inference procedure which adopts as auxiliary model a time-varying generalization of the HAR model for the realized volatility series. It emerges that during the recent financial crisis the relative weight of the daily component dominates over the monthly term. The estimates of the two factor stochastic volatility model suggest that the change in the dynamic structure of the realized volatility during the financial crisis is due to the increase in the volatility of the persistent volatility term. As a consequence of the dynamics in the stochastic volatility parameters, the shape and curvature of the volatility smile evolve trough time. |
| Keywords: | Time-Varying Parameters, On-line Kalman Filter, Simulation-based inference, Predictive Likelihood, Volatility Factors |
| JEL: | G01 C00 C11 C58 |
| Date: | 2013–02–18 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2013-03&r=ets |
| By: | Neil Shephard |
| Abstract: | I discuss models which allow the local level model, which rationalised exponentially weighted moving averages, to have a time-varying signal/noise ratio. I call this a martingale component model. This makes the rate of discounting of data local. I show how to handle such models effectively using an auxiliary particle filter which deploys M Kalman filters run in parallel competing against one another. Here one thinks of M as being 1,000 or more. The model is applied to inflation forecasting. The model generalises to unobserved component models where Gaussian shocks are replaced by martingale difference sequences. |
| Keywords: | Auxiliary particle filter, EM algorithm, EWMA, forecasting, Kalman filter, likelihood, martingale unobserved component model, particle filter, stochastic volatility |
| JEL: | C01 C14 C58 D53 D81 |
| Date: | 2013–02–10 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:644&r=ets |
| By: | Kevin Sheppard; Lily Liu; Andrew J. Patton |
| Abstract: | We study the accuracy of a wide variety of estimators of asset price variation constructed from high-frequency data (so-called "realized measures"), and compare them with a simple "realized variance" (RV) estimator. In total, we consider almost 400 different estimators, applied to 11 years of data on 31 different financial assets spanning five asset classes, including equities, equity indices, exchange rates and interest rates. We apply data-based ranking methods to the realized measures and to forecasts based on these measures. When 5-minute RV is taken as the benchmark realized measure, we find little evidence that it is outperformed by any of the other measures. When using inference methods that do not require specifying a benchmark, we find some evidence that more sophisticated realized measures significantly outperform 5-minute RV. In forecasting applications, we find that a low frequency "truncated" RV outperforms most other realized measures. Overall, we conclude that it is difficult to significantly beat 5-minute RV. |
| Keywords: | Realized variance, volatility forecasting, high frequency data |
| JEL: | C58 C22 C53 |
| Date: | 2013–02–12 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:645&r=ets |
| By: | Majid M. Al-Sadoon |
| Abstract: | This paper extends multivariate Granger causality to take into account the subspaces along which Granger causality occurs as well as long run Granger causality. The properties of these new notions of Granger causality, along with the requisite restrictions, are derived and extensively studied for a wide variety of time series processes including linear invertible process and VARMA. Using the proposed extensions, the paper demonstrates that: (i) mean reversion in L2 is an instance of long run Granger non-causality, (ii) cointegration is a special case of long run Granger non-causality along a subspace, (iii) controllability is a special case of Granger causality, and finally (iv) linear rational expectations entail (possibly testable) Granger causality restriction along subspaces. |
| Keywords: | Granger causality, long run Granger causality, L2 -mean-reversion, p-mixing, cointegration, VARMA, controllability, Kalman Decomposition, linear rational expectations |
| JEL: | C10 C32 C51 |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1356&r=ets |
| By: | Lijuan Huo (School of Economics, Yonsei University, Korea, Department of Applied Mathematics, School of Science, Changchun University of Science and Technology, China); Tae-Hwan Kim (School of Economics, Yonsei University, Korea); Yunmi Kim (Department of Economics, Kookmin University, Korea) |
| Abstract: | Quantile regression (QR) models have been increasingly employed in many applied areas in economics. At the early stage, applications took place usually using cross-section data, but recent development has seen a surge of the use of quantile regression in both time-series and panel datasets. However, how to test for possible autocorrelation, especially in the context of time-series models, has been paid little attention. As a rule of thumb, one might attempt to apply the usual Breusch-Godfrey LM test to the residuals from the baseline quantile regression. In this paper, we demonstrate by Monte Carlo simulations that such an application of the LM test can result in potentially large size distortions, especially in either low or high quantiles. We then propose two correct tests (named the F-test and the QR-LM test) for autocorrelation in quantile models, which do not suffer from any size distortion. Monte Carlo simulation demonstrate that the two tests perform fairly well in finite samples, across either different quantiles or different underlying error distributions. |
| Keywords: | Quantile regression, autocorrelation, LM test |
| JEL: | C12 C22 |
| Date: | 2013–02–13 |
| URL: | http://d.repec.org/n?u=RePEc:yon:wpaper:2013rwp-54&r=ets |
| By: | Marczak, Martyna; Gómez, Victor |
| Abstract: | This article proposes a new multivariate method to construct business cycle indicators. The method is based on a decomposition into trend-cycle and irregular. To derive the cycle, a multivariate band-pass filter is applied to the estimated trend-cycle. The whole procedure is fully model-based. Using a set of monthly and quarterly US time series, two monthly business cycle indicators are obtained for the US. They are represented by the smoothed cycles of real GDP and the industrial production index. Both indicators are able to reproduce previous recessions very well. Series contributing to the construction of both indicators are allowed to be leading, lagging or coincident relative to the business cycle. Their behavior is assessed by means of the phase angle and the mean phase angle after cycle estimation. The proposed multivariate method can serve as an attractive tool for policy making, in particular due to its good forecasting performance and quite simple setting. The model ensures reliable realtime forecasts even though it does not involve elaborate mechanisms that account for, e.g., changes in volatility. -- |
| Keywords: | business cycle,multivariate structural time series model,univariate band-pass filter,forecasts,phase angle |
| JEL: | E32 E37 C18 C32 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:fziddp:642013&r=ets |