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on Econometric Time Series |
| By: | Michael McAleer (University of Canterbury) |
| Abstract: | The three most popular univariate conditional volatility models are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or EGARCH) model of Nelson (1990, 1991). The underlying stochastic specification to obtain GARCH was demonstrated by Tsay (1987), and that of EGARCH was shown recently in McAleer and Hafner (2014). These models are important in estimating and forecasting volatility, as well as capturing asymmetry, which is the different effects on conditional volatility of positive and negative effects of equal magnitude, and leverage, which is the negative correlation between returns shocks and subsequent shocks to volatility. As there seems to be some confusion in the literature between asymmetry and leverage, as well as which asymmetric models are purported to be able to capture leverage, the purpose of the paper is two-fold, namely: (1) to derive the GJR model from a random coefficient autoregressive process, with appropriate regularity conditions; and (2) to show that leverage is not possible in these univariate conditional volatility models. |
| Keywords: | Conditional volatility models, random coefficient autoregressive processes, random coefficient complex nonlinear moving average process, asymmetry, leverage |
| JEL: | C22 C52 C58 G32 |
| Date: | 2014–09–25 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:14/24&r=ets |
| By: | Ladislav Kristoufek |
| Abstract: | We study finite sample properties of estimators of power-law cross-correlations -- detrended cross-correlation analysis (DCCA), height cross-correlation analysis (HXA) and detrending moving-average cross-correlation analysis (DMCA) -- with a special focus on short-term memory bias as well as power-law coherency. Presented broad Monte Carlo simulation study focuses on different time series lengths, specific methods' parameter setting, and memory strength. We find that each method is best suited for different time series dynamics so that there is no clear winner between the three. The method selection should be then made based on observed dynamic properties of the analyzed series. |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1409.6857&r=ets |
| By: | Michael Wickens |
| Abstract: | This lecture is about how best to evaluate economic theories in macroeconomics and finance, and the lessons that can be learned from the past use and misuse of evidence. It is argued that all macro/finance models are ‘false’ so should not be judged solely on the realism of their assumptions. The role of theory is to explain the data, They should therefore be judged by their ability to do this. Data mining will often improve the statistical properties of a model but it does not improve economic understanding. These propositions are illustrated with examples from the last fifty years of macro and financial econometrics. |
| Keywords: | Theory and evidence in economics, DSGE modelling, time series modelling, asset price modelling |
| JEL: | B1 C1 E1 G1 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:yor:yorken:14/17&r=ets |
| By: | Christian MÜLLER |
| URL: | http://d.repec.org/n?u=RePEc:ekd:003306:330600100&r=ets |