nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒09‒18
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. Testing for Structural Breaks at Unknown Time: A Steeplechase By Makram El-Shagi; Sebastian Giesen
  2. Modelling Conditional Heteroscedasticity in Nonstationary Series By Cizek, P.
  3. A Cholesky-MIDAS model for predicting stock portfolio volatility By Ralf Becker; Adam Clements; Robert O'Neill
  4. Thresholds, News Impact Surfaces and Dynamic Asymmetric Multivariate GARCH By Massimiliano Caporin; Michael McAleer
  5. Realized Volatility Risk ( Revised in January 2010 ) By David E. Allen; Michael McAleer; Marcel Scharth
  6. Modelling Long Memory Volatility in Agricultural Commodity Futures Returns By Roengchai Tansuchat; Chia-Lin Chang; Michael McAleer
  7. Ranking Multivariate GARCH Models by Problem Dimension By Massimiliano Caporin; Michael McAleer
  8. A time series causal model By Chen, Pu
  9. Looking behind Granger causality By Chen, Pu; Hsiao, Chih-Ying

  1. By: Makram El-Shagi; Sebastian Giesen
    Abstract: This paper analyzes the role of common data problems when identifying structural breaks in small samples. Most notably, we survey small sample properties of the most commonly applied endogenous break tests developed by Brown, Durbin, and Evans (1975) and Zeileis (2004), Nyblom (1989) and Hansen (1992), and Andrews, Lee, and Ploberger (1996). Power and size properties are derived using Monte Carlo simulations. Results emphasize that mostly the CUSUM type tests are affected by the presence of heteroscedasticity, whereas the individual parameter Nyblom test and AvgLM test are proved to be highly robust. However, each test is significantly affected by leptokurtosis. Contrarily to other tests, where skewness is far more problematic than kurtosis, it has no additional effect for any of the endogenous break tests we analyze. Concerning overall robustness the Nyblom test performs best, while being almost on par to more recently developed tests in terms of power.
    Date: 2010–09
  2. By: Cizek, P. (Tilburg University, Center for Economic Research)
    Abstract: To accommodate the inhomogenous character of financial time series over longer time periods, standard parametric models can be extended by allow- ing their coeffcients to vary over time. Focusing on conditional heteroscedas- ticity models, we discuss various strategies to identify and estimate varying- coefficients models and compare all methods by means of a real-data applica- tion.
    Keywords: adaptive estimation;conditional heteroscedasticity;varying-coefficient models;time series
    JEL: C14 C22 C53
    Date: 2010
  3. By: Ralf Becker; Adam Clements; Robert O'Neill
    Abstract: This paper presents a simple forecasting technique for variance covariance matrices. It relies significantly on the contribution of Chiriac and Voev (2010) who propose to forecast elements of the Cholesky decomposition which recombine to form a positive definite forecast for the variance covariance matrix. The method proposed here combines this methodology with advances made in the MIDAS literature to produce a forecasting methodology that is flexible, scales easily with the size of the portfolio and produces superior forecasts in simulation experiments and an empirical application.
    Date: 2010
  4. By: Massimiliano Caporin (Department of Economic Sciences, University of Padova); Michael McAleer (Erasmus School of Economics, Erasmus University Rotterdam)
    Abstract: DAMGARCH is a new model that extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing multiple thresholds and time-dependent structure in the asymmetry of the conditional variances. Analytical expressions for the news impact surface implied by the new model are also presented. DAMGARCH models the shocks affecting the conditional variances on the basis of an underlying multivariate distribution. It is possible to model explicitly asset-specific shocks and common innovations by partitioning the multivariate density support. This paper presents the model structure, describes the implementation issues, and provides the conditions for the existence of a unique stationary solution, and for consistency and asymptotic normality of the quasimaximum likelihood estimators. The paper also presents an empirical example to highlight the usefulness of the new model.
    Date: 2010–05
  5. By: David E. Allen (School of Accounting, Finance and Economics,); Michael McAleer (Econometric Institute,); Marcel Scharth (VU University Amsterdam)
    Abstract: In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility (DARV) model, which incorporates the important fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
    Date: 2009–12
  6. By: Roengchai Tansuchat (Faculty of Economics, Maejo University); Chia-Lin Chang (Department of Applied Economics,); Michael McAleer (Econometric Institute, Erasmus School)
    Abstract: This paper estimates the long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGACH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1,d,1) and FIEGARCH(1,d,1) models are found to outperform their GARCH(1,1) and EGARCH(1,1) counterparts.
    Date: 2009–10
  7. By: Massimiliano Caporin (Department of Economics and Management); Michael McAleer (Erasmus School of Economics, Erasmus University Rotterdam,)
    Abstract: In the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC) of Aeilli (2008), CCC of Bollerslev (1990), Exponentially Weighted Moving Average, and covariance shrinking of Ledoit and Wolf (2004), using the historical data of 89 US equities. Our methods follow some of the approach described in Patton and Sheppard (2009), and contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC model and covariance shrinking. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Weighted Likelihood Ratio test of Amisano and Giacomini (2007). Third, we examine how the model rankings are influenced by the cross-sectional dimension of the problem.
    Date: 2010–05
  8. By: Chen, Pu
    Abstract: Cause-effect relations are central in economic analysis. Uncovering empirical cause-effect relations is one of the main research activities of empirical economics. In this paper we develop a time series casual model to explore casual relations among economic time series. The time series causal model is grounded on the theory of inferred causation that is a probabilistic and graph-theoretic approach to causality featured with automated learning algorithms. Applying our model we are able to infer cause-effect relations that are implied by the observed time series data. The empirically inferred causal relations can then be used to test economic theoretical hypotheses, to provide evidence for formulation of theoretical hypotheses, and to carry out policy analysis. Time series causal models are closely related to the popular vector autoregressive (VAR) models in time series analysis. They can be viewed as restricted structural VAR models identified by the inferred causal relations.
    Keywords: Inferred Causation; Automated Learning; VAR; Granger Causality; Wage-Price Spiral
    JEL: E31 C01
    Date: 2010–09
  9. By: Chen, Pu; Hsiao, Chih-Ying
    Abstract: Granger causality as a popular concept in time series analysis is widely applied in empirical research. The interpretation of Granger causality tests in a cause-effect context is, however, often unclear or even controversial, so that the causality label has faded away. Textbooks carefully warn that Granger causality does not imply true causality and preferably refer the Granger causality test to a forecasting technique. Applying theory of inferred causation, we develop in this paper a method to uncover causal structures behind Granger causality. In this way we re-substantialize the causal attribution in Granger causality through providing an causal explanation to the conditional dependence manifested in Granger causality.
    Keywords: Granger Causality; Time Series Causal Model; Graphical Model
    JEL: C1 E3
    Date: 2010–09

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