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on Econometric Time Series |
| By: | Siem Jan Koopman (VU University Amsterdam); Rutger Lit (VU University Amsterdam); Andre Lucas (VU University Amsterdam) |
| Abstract: | We introduce a dynamic Skellam model that measures stochastic volatility from high-frequency tick-by-tick discrete stock price changes. The likelihood function for our model is analytically intractable and requires Monte Carlo integration methods for its numerical evaluation. The proposed methodology is applied to tick-by-tick data of four stocks traded on the New York Stock Exchange. We require fast simulation methods for likelihood evaluation since the number of observations per series per day varies from 1000 to 10,000. Complexities in the intraday dynamics of volatility and in the frequency of trades without price impact require further non-trivial adjustments to the dynamic Skellam model. In-sample residual diagnostics and goodness-of-fit statistics show that the final model provides a good fit to the data. An extensive forecasting study of intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling approaches. |
| Keywords: | non-Gaussian time series models; volatility models; importance sampling; numerical integration; high-frequency data; discrete price changes. |
| JEL: | C22 C32 C58 |
| Date: | 2015–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20150076&r=ets |
| By: | Bartosz Mackowiak (European Central Bank) |
| Abstract: | We derive a closed-form expression for the posterior probability of Granger-noncausality in a Gaussian vector autoregression with a conjugate prior. We also express in closed form the posterior probability of Granger-causal-priority, a more general relation that accounts for indirect effects between variables and therefore is suitable in a multivariate context. We show how to use these results to choose variables for a vector autoregression, whether the goal is prediction or impulse response analysis. |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed015:66&r=ets |
| By: | Koen Jochmans (Département d'économie); Geert Dhaene (KU Leuven) |
| Abstract: | We derive bias-corrected least-squares estimators of panel vector autoregressions with fixed effects. The correction is straightforward to implement and yields an estimator that is asymptotically unbiased under asymptotics where the number of time series observations grows at the same rate as the number of cross-sectional observations. This makes the estimator well suited for most macroeconomic data sets. Simulation results show that the estimator yields substantial improvements over within-group least-squares estimation. We illustrate the bias correction in a study of the relation between the unemployment rate and the economic growth rate at the U.S. state level. |
| Keywords: | bias correction, fixed effects, panel data, vector autoregression |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/4ect7tfnam9poo2tioundd7pb3&r=ets |
| By: | Jozef Barunik; Tomas Krehlik |
| Abstract: | We propose a general framework for measuring frequency dynamics of connectedness in economic variables based on spectral representation of variance decompositions. We argue that the frequency dynamics is insightful when studying the connectedness of variables as shocks with heterogeneous frequency responses will create frequency dependent connections of different strength that remain hidden when time domain measures are used. Two applications support the usefulness of the discussion, guide a user to apply the methods in different situations, and contribute to the literature with important findings about sources of connectedness. Giving up the assumption of global stationarity of stock market data and approximating the dynamics locally, we document rich time-frequency dynamics of connectedness in US market risk in the first application. Controlling for common shocks due to common stochastic trends which dominate the connections, we identify connections of global economy at business cycle frequencies of 18 up to 96 months in the second application. In addition, we study the effects of cross-sectional dependence on the connectedness of variables. |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1507.01729&r=ets |
| By: | Marian Vavra (National Bank of Slovakia, Research Department) |
| Abstract: | This paper is concerned with the problem of testing for the equal forecast accuracy of competing models using a bootstrap-based Diebold-Mariano test statistic. The finite-sample properties of the test are assessed via Monte Carlo experiments. As an illustration, the forecast accuracy of the US Survey of Professional Forecasters is compared to that of an autoregressive model. The empirical results indicate that professionals beat AR models systematically only for a single economic variable – the unemployment rate |
| Keywords: | Forecast evaluation; Diebold-Mariano test; Sieve bootstrap |
| JEL: | C12 C15 C32 C53 |
| Date: | 2015–06 |
| URL: | http://d.repec.org/n?u=RePEc:svk:wpaper:1034&r=ets |
| By: | Yoann Potiron; Per Mykland |
| Abstract: | When estimating integrated covariation between two assets based on high-frequency data,simple assumptions are usually imposed on the relationship between the price processes and the observation times. In this paper, we introduce an endogenous 2-dimensional model and show that it is more general than the existing endogenous models of the literature. In addition, we establish a central limit theorem for the Hayashi-Yoshida estimator in this general endogenous model in the case where prices follow pure-diffusion processes. |
| Date: | 2015–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1507.01033&r=ets |