nep-ets New Economics Papers
on Econometric Time Series
Issue of 2011‒12‒19
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Marginal Likelihood for Markov-switching and Change-point Garch Models By Luc Luc; Arnaud Dufays; Jeroen V.K. Rombouts
  2. Forecasting with Option Implied Information By Peter Christoffersen; Kris Jacobs; Bo Young Chang
  3. Asymptotic theory of range-based multipower variation By Kim Christensen; Mark Podolskij
  4. Bayesian analysis of coefficient instability in dynamic regressions By Emanuela Ciapanna; Marco Taboga
  5. A method to estimate power parameter in Exponential Power Distribution via polynomial regression By Daniele Coin
  6. GMM Estimation of Fixed Effects Dynamic Panel Data Models with Spatial Lag and Spatial Errors By Cizek, P.; Jacobs, J.P.A.M.; Ligthart, J.E.; Vrijburg, H.
  7. Long Memory Dynamics for Multivariate Dependence under Heavy Tails By Pawel Janus; Siem Jan Koopman; André Lucas
  8. Detecting multiple breaks in long memory: The case of US inflation By Hassler, Uwe; Meller, Barbara

  1. By: Luc Luc (Université catholique de Louvain, CORE); Arnaud Dufays (Université catholique de Louvain, CORE); Jeroen V.K. Rombouts (Institute of Applied Economics at HEC Montréal, CIRANO, CIRPEE, and CORE)
    Abstract: GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved issue is the computation of their marginal likelihood, which is essential for determining the number of regimes or change-points. We solve the problem by using particle MCMC, a technique proposed by Andrieu, Doucet, and Holenstein (2010). We examine the performance of this new method on simulated data, and we illustrate its use on several return series.
    Keywords: Bayesian inference, Simulation, GARCH, Markov-switching model, Changepoint model, Marginal likelihood, Particle MCMC
    JEL: C11 C15 C22 C58
    Date: 2011–11–24
  2. By: Peter Christoffersen (University of Toronto - Rotman School of Management and CREATES); Kris Jacobs (University of Houston - C.T. Bauer College of Business); Bo Young Chang (Bank of Canada)
    Abstract: This chapter surveys the methods available for extracting forward-looking information from option prices. We consider volatility, skewness, kurtosis, and density forecasting. More generally, we discuss how any forecasting object which is a twice differentiable function of the future realization of the underlying risky asset price can utilize option implied information in a well-defi?ned manner. Going beyond the univariate option-implied density, we also consider results on option-implied covariance, correlation and beta forecasting as well as the use of option-implied information in cross-sectional forecasting of equity returns.
    Keywords: Volatility, skewness, kurtosis, density forecasting, risk-neutral.
    JEL: G13 G17 C53
    Date: 2011–12–08
  3. By: Kim Christensen (Aarhus University and CREATES); Mark Podolskij (University of Heidelberg and CREATES)
    Abstract: In this paper, we present a realised range-based multipower variation theory, which can be used to estimate return variation and draw jump-robust inference about the diffusive volatility component, when a high-frequency record of asset prices is available. The standard range-statistic – routinely used in financial economics to estimate the variance of securities prices – is shown to be biased when the price process contains jumps. We outline how the new theory can be applied to remove this bias by constructing a hybrid range-based estimator. Our asymptotic theory also reveals that when high-frequency data are sparsely sampled, as is often done in practice due to the presence of microstructure noise, the range-based multipower variations can produce significant efficiency gains over comparable subsampled returnbased estimators. The analysis is supported by a simulation study and we illustrate the practical use of our framework on some recent TAQ equity data.
    Keywords: High-frequency data, Integrated variance, Realised multipower variation, Realised range-basedmultipower variation, Quadratic variation.
    JEL: C10 C80
    Date: 2011–10–30
  4. By: Emanuela Ciapanna (Bank of Italy); Marco Taboga (Bank of Italy)
    Abstract: This paper proposes a Bayesian regression model with time-varying coefficients (TVC) that makes it possible to estimate jointly the degree of instability and the time-path of regression coefficients. Thanks to its computational tractability, the model proves suitable to perform the first (to our knowledge) Monte Carlo study of the finite-sample properties of a TVC model. Under several specifications of the data generating process, the proposed model’s estimation precision and forecasting accuracy compare favourably with those of other methods commonly used to deal with parameter instability. Furthermore, the TVC model leads to small losses of efficiency under the null of stability and it is robust to mis-specification, providing a satisfactory performance also when regression coefficients experience discrete structural breaks. As a demonstrative application, we use our TVC model to estimate the exposures of S&P 500 stocks to market-wide risk factors: we find that a vast majority of stocks have time-varying risk exposures and that the TVC model helps to forecast these exposures more accurately.
    Keywords: time-varying regression, coefficient instability
    JEL: C11 C32 C50
    Date: 2011–11
  5. By: Daniele Coin (Bank of Italy)
    Abstract: The Exponential Power Distribution (EPD), also known as Generalized Error Distribution (GED), is a flexible symmetrical unimodal family belonging to the exponential family. The EPD becomes the density function of a range of symmetric distributions with different values of its power parameter B. A closed-form estimator for B does not exist, so the power parameter is usually estimated numerically. Unfortunately the optimization algorithms do not always converge, especially when the true value of B is close to its parametric space frontier. In this paper we present an alternative method for estimating B, based on the Normal Standardized Q-Q Plot and exploiting the relationship between B and the kurtosis. It is a direct method that does not require computational efforts or the use of optimization algorithms.
    Keywords: Exponential Power Distribution, kurtosis, normal standardized Q-Q plot.
    JEL: C14 C15 C63
    Date: 2011–11
  6. By: Cizek, P.; Jacobs, J.P.A.M.; Ligthart, J.E.; Vrijburg, H. (Tilburg University, Center for Economic Research)
    Abstract: We extend the three-step generalized methods of moments (GMM) approach of Kapoor et al. (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor et al. (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and specifying moment conditions for various time lags, spatial lags, and sets of exogenous variables yields new spatial dynamic panel data estimators. We prove their consistency and asymptotic normality for a large number of spatial units N and a xed small number of time periods T. Monte Carlo simulations demonstrate that the root mean squared error of spatially corrected GMM estimates|which are based on a spatial lag and spatial error correction|is generally smaller than that of corresponding spatial GMM estimates in which spatial error correlation is ignored. We show that the spatial Blundell-Bond estimators outperform the spatial Arellano-Bond estimators.
    Keywords: Dynamic panel models;spatial lag;spatial error;GMM estimation.
    JEL: C15 C21 C22 C23
    Date: 2011
  7. By: Pawel Janus (VU University Amsterdam); Siem Jan Koopman (VU University Amsterdam); André Lucas (VU University Amsterdam)
    Abstract: We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to outliers or jumps in the data. In the empirical study for four Dow Jones equities, we find that the degree of memory in the volatilities of the equity return series is similar, while the degree of memory in correlations between the series varies significantly. The forecasts from our model are compared with high-frequency realised volatility and dependence measures. The forecast accuracy is overall higher compared to those from some well-known competing benchmark models.
    Keywords: fractional integration; correlation; Student's t copula; time-varying dependence; multivariate volatility
    JEL: C10 C22 C32 C51
    Date: 2011–12–12
  8. By: Hassler, Uwe; Meller, Barbara
    Abstract: Multiple structural change tests by Bei and Perron (1998) are applied to the regression by Demetrescu, Kuzin and Hassler (2008) in order to detect breaks in the order of fractional integration. With this instrument we tackle time-varying inflation persistence as an important issue for monetary policy. We determine not only the location and significance of breaks in persistence, but also the number of breaks. Only one significant break in U.S. inflation persistence (measured by the long-memory parameter) is found to have taken place in 1973, while a second break in 1980 is not significant. --
    Keywords: Fractional integration,break in persistence,unknown break point,inflation dynamics
    JEL: C22 E31
    Date: 2011

This nep-ets issue is ©2011 by Yong Yin. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.