nep-ets New Economics Papers
on Econometric Time Series
Issue of 2009‒11‒14
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Modeling Asymmetric Volatility Clusters Using Copulas and High Frequency Data By Cathy Ning; Dinghai Xu; Tony Wirjanto
  2. Extreme Dependence in International Stock Markets By Cathy Ning
  3. Modelling Realized Covariances By Xin Jin; John M Maheu
  4. Predictive density construction and accuracy testing with multiple possibly misspecified diffusion models By Valentina Corradi; Norman R. Swanson
  5. Testing the null hypothesis of no regime switching with an application to GDP growth rates By Marmer, Vadim
  6. Nonlinearity, Nonstationarity, and Spurious Forecasts By Marmer, Vadim

  1. By: Cathy Ning (Department of Economics, Ryerson University, Toronto, Canada); Dinghai Xu (Department of Economics, University of Waterloo, Waterloo, Ontario, Canada); Tony Wirjanto (School of Accounting & Finance and Department of Statistics & Actuarial Science,University of Waterloo, Waterloo, Ontario, Canada)
    Abstract: Volatility clustering is a well-known stylized feature of financial asset returns. In this paper, we investigate the asymmetric pattern of volatility clustering on both the stock and foreign exchange rate markets. To this end, we employ copula-based semi-parametric univariate time-series models that accommodate the clusters of both large and small volatilities in the analysis. Using daily realized volatilities of the individual company stocks, stock indices and foreign exchange rates constructed from high frequency data, we find that volatility clustering is strongly asymmetric in the sense that clusters of large volatilities tend to be much stronger than those of small volatilities. In addition, the asymmetric pattern of volatility clusters continues to be visible even when the clusters are allowed to be changing over time, and the volatility clusters themselves remain persistent even after forty days.
    Keywords: Volatility clustering, Copulas, Realized volatility, High-frequency data.
    JEL: C51 G32
    Date: 2009–11
  2. By: Cathy Ning (Department of Economics, Ryerson University, Toronto, Canada)
    Abstract: This paper investigates the structure and degree of extreme dependence in international equity markets using carefully selected tools from the theory of copulas. We examine both the static and dynamic dependence via unconditional and conditional copulas. We find significant asymmetric tail dependence in equity markets, with the overall larger lower tail dependence than upper tail dependence. Moreover, in Europe and East Asia but not in North America, the extreme dependence is time-varying in both its structure and degree. Our results also indicate a higher intra-continental than inter-continental tail dependence. Our findings have important implications in global risk management strategies.
    Keywords: Copulas; Tail dependence; Time varying dependence; International financial markets; Risk diversification.
    JEL: C14 C51 G15 G32
    Date: 2009–11
  3. By: Xin Jin; John M Maheu
    Abstract: This paper proposes a new dynamic model of realized covariance (RCOV) matrices based on recent work in time-varying Wishart distributions. The specifications can be linked to returns for a joint multivariate model of returns and covariance dynamics that is both easy to estimate and forecast. Realized covariance matrices are constructed for 5 stocks using high-frequency intraday prices based on positive semi-definite realized kernel estimates. We extend the model to capture the strong persistence properties in RCOV. Out-of-sample performance based on statistical and economic metrics show the importance of this. We discuss which features of the model are necessary to provide improvements over a traditional multivariate GARCH model that only uses daily returns.
    Keywords: eigenvalues, dynamic conditional correlation, predictive likelihoods, MCMC
    JEL: C11 C32 C53
    Date: 2009–11–10
  4. By: Valentina Corradi; Norman R. Swanson
    Abstract: This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, the authors first outline a simple simulation-based framework for constructing predictive densities for one-factor and stochastic volatility models. Then, they construct accuracy assessment tests that are in the spirit of Diebold and Mariano (1995) and White (2000). In order to establish the asymptotic properties of their tests, the authors also develop a recursive variant of the nonparametric simulated maximum likelihood estimator of Fermanian and Salanié (2004). In an empirical illustration, the predictive densities from several models of the one-month federal funds rates are compared.
    Keywords: Econometric models - Evaluation ; Stochastic analysis
    Date: 2009
  5. By: Marmer, Vadim
    Abstract: This paper presents tests for the null hypothesis of no regime switching in Hamilton's (1989) regime switching model. The test procedures exploit similarities between regime switching models, autoregressions with measurement errors, and finite mixture models. The proposed tests are computationally simple and, contrary to likelihood based tests, have a standard distribution under the null. When the methodology is applied to US GDP growth rates, no strong evidence of regime switching is found.
    Keywords: regime switching, LM tests, GMM, matching methods, GDP growth rates
    Date: 2009–11–02
  6. By: Marmer, Vadim
    Abstract: Implications of nonlinearity, nonstationarity and misspecification are considered from a forecasting perspective. Our model allows for small departures from the martingale difference sequence hypothesis by including a nonlinear component, formulated as a general, integrable transformation of the I(1) predictor. We assume that the true generating mechanism is unknown to the econometrician and he is therefore forced to use some approximating functions. It is shown that in this framework the linear regression techniques lead to spurious forecasts. Improvements of the forecast accuracy are possible with properly chosen nonlinear transformations of the predictor. The paper derives the limiting distribution of the forecasts' MSE. In the case of square integrable approximants, it depends on the Lâ‚‚-distance between the nonlinear component and approximating function. Optimal forecasts are available for a given class of approximants.
    Keywords: Forecasting; integrated time series; misspecified models; nonlinear transformations; stock returns
    Date: 2009–11–03

This nep-ets issue is ©2009 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.