nep-ets New Economics Papers
on Econometric Time Series
Issue of 2012‒10‒13
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Limit theorems for non-degenerate U-statistics of continuous semimartingales By Mark Podolskij; Christian Schmidt; Johanna Fasciati Ziegel
  2. Endogenous Crisis Dating and Contagion Using Smooth Transition Structural GARCH By Mardi Dungey; George Milunovich; Susan Thorp; Minxian Yang
  3. On trend-cycle decomposition and data revision By Norden, Simon van; Tian, Jing; Jacobs, Jan; Dungey, Mardi
  4. Understanding DSGE Filters in Forecasting and Policy Analysis By Andrle, Michal
  5. Combining predictive densities using Bayesian filtering with applications to US economic data By Monica Billio; Roberto Casarin; Francesco Ravazzolo; Herman K. van Dijk
  6. Directional forecasting in financial time series using support vector machines: The USD/Euro exchange rate By Plakandaras, Vasilios; Papadimitriou, Theophilos; Gogas, Periklis
  7. Systems of Brownian particles with asymmetric collisions By Ioannis Karatzas; Soumik Pal; Mykhaylo Shkolnikov
  8. Fourier--type estimation of the power garch model with stable--paretian innovations By Francq, Christian; Meintanis, Simos

  1. By: Mark Podolskij (Heidelberg University and CREATES); Christian Schmidt (Heidelberg University); Johanna Fasciati Ziegel (University of Bern)
    Abstract: This paper presents the asymptotic theory for non-degenerate U-statistics of high frequency observations of continuous Itô semimartingales. We prove uniform convergence in probability and show a functional stable central limit theorem for the standardized version of the U-statistic. The limiting process in the central limit theorem turns out to be conditionally Gaussian with mean zero. Finally, we indicate potential statistical applications of our probabilistic results.
    Keywords: High frequency data, Limit theorems, Semimartingales, Stable convergence, U-statistics
    JEL: C10 C13 C14
    Date: 2012–10–02
  2. By: Mardi Dungey (School of Economics and Finance, University of Tasmania); George Milunovich (Department of Economics, Macquarie University); Susan Thorp (Finance Discipline Group, UTS Business School, University of Technology, Sydney); Minxian Yang (School of Economics, University of New South Wales)
    Abstract: Detecting contagion during financial crises requires demarcation of crisis periods. This paper presents a method for endogenous dating of both the start and finish of crises, coupled with the statistical detection of contagion effects. We couple smooth transition functions with structural GARCH to identify both features of markets in crisis, and provide conditions under which these effects will be identified. To illustrate we apply the framework to US financial returns in REITS, S&P500 and Treasury bonds indices over the period 2001 to 2010, and clearly identify four phases consistent with a pre-crisis period to October 2007, two phases of crisis up to and following late August 2008, and a post-crisis phase dating from June 2009. The evidence strongly supports changes in the transmission mechanisms of shocks between asset returns during the crisis, and particularly contagion from equity markets to REITS. The post-crisis period has not returned to pre-crisis relationships.
    Keywords: contagion; structural GARCH; global financial crisis
    JEL: G01 C51
    Date: 2012–08–01
  3. By: Norden, Simon van; Tian, Jing; Jacobs, Jan; Dungey, Mardi (Groningen University)
    Abstract: A well-documented property of the Beveridge-Nelson trend-cycle decomposition is the perfect negative correlation between trend and cycle innovations. This paper gives a novel explanation for this negative correlation originating from the Jacobs-van Norden (2011) data revision model. Trend shocks may enter the equation for the cycle or cyclical shocks may enter the trend equation. We discuss economic interpretations and implications, including ltering and smoothing properties.We illustrate the idea with simulations based on the Morley, Nelson and Zivot (2003) outcomes
    Date: 2012
  4. By: Andrle, Michal
    Abstract: The paper introduces methods that allow analysts to (i) decompose the estimates of unobserved quantities into observed data and (ii) impose subjective prior constraints on path estimates of unobserved shocks in structural economic models. For instance, decomposition of output gap to output, inflation, interest rates and other observables contribution is feasible. The intuitive nature and the analytical clarity of procedures suggested are appealing for policy-related and forecasting models. The paper brings some of the power embodied in the theory of linear multivariate filters, namely relatinship between Kalman and Wiener-Kolmogorov filtering, into the area of structural multivariate models, expressed in linear state-space form.
    Keywords: filter; DSGE; state-space; observables decomposition; judgement
    JEL: C10 E50
    Date: 2012–10
  5. By: Monica Billio (Department of Economics, University Of Venice Cà Foscari); Roberto Casarin (Department of Economics, University Of Venice Cà Foscari); Francesco Ravazzolo (Norges Bank); Herman K. van Dijk (Erasmus University)
    Abstract: Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying combination strategies are introduced. In particular, a weight dynamics driven by the past performance of the predictive densities is considered and the use of learning mechanisms. The approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.
    Keywords: Density Forecast Combination, Survey Forecast, Bayesian Filtering, Sequential Monte Carlo.
    JEL: C11 C15 C53 E37
    Date: 2012
  6. By: Plakandaras, Vasilios (Democritus University of Thrace, Department of International Economic Relations and Development); Papadimitriou, Theophilos (Democritus University of Thrace, Department of International Economic Relations and Development); Gogas, Periklis (Democritus University of Thrace, Department of International Economic Relations and Development)
    Abstract: In this paper, we present a novel machine learning based forecasting system of the EU/USD exchange rate directional changes. Specifically, we feed an overcomplete variable set to a Support Vector Machines (SVM) model and refine it through a Sensitivity Analysis process. The dataset spans from 1/1/1999 to 30/11/2011; the data of the last 7 months are reserved for out-of-sample testing. Results show that the proposed scheme outperforms various other machine learning methods treating similar scenarios.
    Keywords: Machine Learning; Support Vector Machines; Exchange Rates; Forecasting
    JEL: C52 C59 F31 G17
    Date: 2012–01–27
  7. By: Ioannis Karatzas; Soumik Pal; Mykhaylo Shkolnikov
    Abstract: We study systems of Brownian particles on the real line, which interact by splitting the local times of collisions among themselves in an asymmetric manner. We prove the strong existence and uniqueness of such processes and identify them with the collections of ordered processes in a Brownian particle system, in which the drift coefficients, the diffusion coefficients, and the collision local times for the individual particles are assigned according to their ranks. These Brownian systems can be viewed as generalizations of those arising in first-order models for equity markets in the context of stochastic portfolio theory, and are able to correct for several shortcomings of such models while being equally amenable to computations. We also show that, in addition to being of interest in their own right, such systems of Brownian particles arise as universal scaling limits of systems of jump processes on the integer lattice with local interactions. A key step in the proof is the analysis of a generalization of Skorokhod maps which include `local times' at the intersection of faces of the nonnegative orthant. The result extends the convergence of TASEP to its continuous analogue. Finally, we identify those among the Brownian particle systems which have a probabilistic structure of determinantal type.
    Date: 2012–09
  8. By: Francq, Christian; Meintanis, Simos
    Abstract: We consider estimation for general power GARCH models under stable--Paretian innovations. Exploiting the simple structure of the conditional characteristic function of the observations driven by these models we propose minimum distance estimation based on the empirical characteristic function of corresponding residuals. Consistency of the estimators is proved, and we obtain a singular asymptotic distribution which is concentrated on a hyperplane. Efficiency issues are explored and finite--sample results are presented as well as applications of the proposed procedures to real data from the financial markets. A multivariate extension is also considered.
    Keywords: GARCH model; Minimum distance estimation; Heavy--tailed distribution; Empirical characteristic function
    JEL: C32 C13 C22
    Date: 2012–10–01

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