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on Econometric Time Series |
| By: | Aurea Grané; Belén Martín-Barragán; Helena Veiga |
| Abstract: | Outliers of moderate magnitude cause large changes in financial time series of prices andreturns and affect both the estimation of parameters and volatilities when fitting a GARCH-typemodel. The multivariate setting is still to be studied, but similar biases and impacts oncorrelation dynamics are believed to exist. The accurate estimation of the correlation structure iscrucial in many applications, such as portfolio allocation and risk management. This paperfocuses on these issues by studding the impact of additive outliers (isolated and patches of leveloutliers and volatility outliers) on the estimation of correlations when fitting well knownmultivariate GARCH models and by proposing a general detection algorithm based on waveletsthat can be applied to a large class of multivariate volatility models. This procedure can be alsointerpreted as a model miss-specification test since it is based on residual diagnostics. Theeffectiveness of the new proposal is evaluated by an intensive Monte Carlo study before it isapplied to daily stock market indices. The simulation studies show that correlations are highlyaffected by the presence of outliers and that the new method is both effective and reliable, sinceit detects very few false outliers. |
| Keywords: | Additive Outliers, Correlations, Volatilities, Wavelets |
| JEL: | C10 C13 C53 C58 G17 |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws140503&r=ets |
| By: | Irma Hindrayanto; Jan Jacobs; Denise Osborn |
| Abstract: | Traditional unobserved component models assume that the trend, cycle and seasonal components of an individual time series evolve separately over time. Although this assumption has been relaxed in recent papers that focus on trend-cycle interactions, it remains at the core of all seasonal adjustment methods applied by official statistical agencies around the world. The present paper develops an unobserved components model that permits non-zero correlations between seasonal and non-seasonal shocks, hence allowing testing of the uncorrelated assumption that is traditionally imposed. Identification conditions for estimation of the parameters are discussed, while applications to observed time series illustrate the model and its implications for seasonal adjustment. |
| Keywords: | trend-cycle-seasonal decomposition; unobserved components; state-space models; seasonal adjustment; global real economic activity; unemployment |
| JEL: | C22 E24 E32 E37 F01 |
| Date: | 2014–03 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:417&r=ets |
| By: | Rasmus Søndergaard Pedersen (Department of Economics, Copenhagen University) |
| Abstract: | As an alternative to quasi-maximum likelihood, targeting estimation is a much applied estimation method for univariate and multivariate GARCH models. In terms of variance targeting estimation recent research has pointed out that at least finite fourth-order moments of the data generating process is required if one wants to perform inference in GARCH models relying on asymptotic normality of the estimator, see Pedersen and Rahbek (2014) and Francq et al. (2011). Such moment conditions may not be satisfied in practice for financial returns highlighting a large drawback of variance targeting estimation. In this paper we consider the large-sample properties of the variance targeting estimator for the multivariate extended constant conditional correlation GARCH model when the distribution of the data generating process has infinite fourth moments. Using non-standard limit theory we derive new results for the estimator stating that its limiting distribution is multivariate stable. The rate of consistency of the estimator is slower than squareroot T (as obtained by the quasi-maximum likelihood estimator) and depends on the tails of the data generating process |
| Keywords: | Targeting; variance targeting; multivariate GARCH; constant conditional correlation; asymptotic theory; time series, multivariate regular variation, stable distributions. |
| JEL: | C32 C51 C58 |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kuiedp:1404&r=ets |
| By: | Blöchl, Andreas |
| Abstract: | On purpose to extract trend and cycle from a time series many competing techniques have been developed. The probably most prevalent is the Hodrick Prescott filter. However this filter suffers from diverse shortcomings, especially the subjective choice of its penalization parameter. To this point penalized splines within a mixed model framework offer the advantage of a data driven derivation of the penalization parameter. Nevertheless the Hodrick-Prescott filter as well as penalized splines fail to estimate trend and cycle when one deals with times series that contain structural breaks. This paper extends the technique of splines within a mixed model framework to account for break points in the data. It explains how penalized splines as mixed models can be used to avoid distortions caused by breaks and finally provides an empirical application to German data which exhibit structural breaks due to the reunification in 1990. |
| Keywords: | penalized splines; mixed models; structural breaks; trends; flexible penalization |
| JEL: | C22 C52 |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:lmu:muenec:18446&r=ets |
| By: | David Ardia; Lukasz Gatarek; Lennart F. hoogerheide |
| Abstract: | A novel simulation-based methodology is proposed to test the validity of a set of marginal time series models, where the dependence structure between the time series is taken ‘directly’ from the observed data. The procedure is useful when one wants to summarize the test results for several time series in one joint test statistic and p-value. The proposed test method can have higher power than a test for a univariate time series, especially for short time series. Therefore our test for multiple time series is particularly useful if one wants to assess Value-at-Risk (or Expected Shortfall) predictions over a small time frame (e.g., a crisis period). We apply our method to test GARCH model specifications for a large panel data set of stock returns. |
| Keywords: | Bootstrap test, GARCH, Marginal models,Multiple time series, Value-at-Risk |
| JEL: | C1 C12 C22 C44 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:lvl:lacicr:1413&r=ets |
| By: | Smeekes S.; Urbain J.R.Y.J. (GSBE) |
| Abstract: | In this paper we consider several modified wild bootstrap methods that, additionally to heteroskedasticity, can take dependence into account. The modified wild bootstrap methods are shown to correctly replicate an invariance principle for multivariate time series that are characterized by general forms of unconditional heteroskedasticity, or nonstationary volatility, as well as dependence within and between different elements of the time series. The invariance principle is then applied to derive the asymptotic validity of the wild bootstrap methods for unit root testing in a multivariate setting. The resulting tests, which can also be interpreted as panel unit root tests, are valid under more general assumptions than most current tests used in the literature. A simulation study is performed to evaluate the small sample properties of the bootstrap unit root tests. |
| Keywords: | Statistical Simulation Methods: General; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; |
| JEL: | C15 C32 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:unm:umagsb:2014008&r=ets |
| By: | Yamin Ahmad (Department of Economics, University of Wisconsin - Whitewater); Ivan Paya (Department of Economics, Lancaster University Management School) |
| Abstract: | This paper examines the impact of time averaging and interval sampling data assuming that the data generating process for a given series follows a random walk with uncorrelated increments. We provide expressions for the corresponding variances, and covariances, for both the levels and differences of the aggregated series, demonstrating how the degree of temporal aggregation impacts these particular properties. Moreover, we analytically derive any differences that arise between the aggregated series and its disaggregated counterpart, and show that they can be decomposed into a distortionary and small sample effect. We also provide exact expressions for the variance and sharpe ratios, and correlation coefficients for any level of aggregation. We discuss our results in the context of asset prices, which have utilized these extensively. |
| Keywords: | Temporal Aggregation, Random Walk, Variance Ratio, Sharpe Ratio |
| JEL: | F47 C15 C32 |
| Date: | 2014–01 |
| URL: | http://d.repec.org/n?u=RePEc:uww:wpaper:14-01&r=ets |
| By: | Yamin Ahmad (Department of Economics, University of Wisconsin - Whitewater); Luiggi Donayre (Department of Economics, University of Minnesota - Duluth) |
| Abstract: | We conduct Monte Carlo simulations to investigate the effects of outlier observations on the properties of linearity tests against threshold autoregressive (TAR) processes. By considering different specifications and levels of persistence of the data generating processes, we find that outliers distort the size of the test and that the distortion increases with the level of persistence. However, contrary to what one might expect, we also find that larger outliers could help improve the power of the test in the case of persistent TAR processes. |
| Keywords: | Outliers, Persistence, Monte Carlo Simulations, Threshold Autoregressionn, Size, Power |
| JEL: | C15 C22 |
| Date: | 2014–03 |
| URL: | http://d.repec.org/n?u=RePEc:uww:wpaper:14-02&r=ets |
| By: | Tomasz Skoczylas (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | In this paper a new ARCH-type volatility model is proposed. The Range-based Heterogeneous Autoregressive Conditional Heteroskedasticity (RHARCH) model draws inspiration from Heterogeneous Autoregressive Conditional Heteroskedasticity presented by Muller et al. (1995), but employs more efficient, range-based volatility estimators instead of simple squared returns in conditional variance equation. In the first part of this research range-based volatility estimators (such as Parkinson, or Garman-Klass estimators) are reviewed, followed by derivation of the RHARCH model. In the second part of this research the RHARCH model is compared with selected ARCH-type models with particular emphasis on forecasting accuracy. All models are estimated using data containing EURPLN spot rate quotation. Results show that RHARCH model often outperforms return-based models in terms of predictive abilities in both in-sample and out-of-sample periods. Also properties of standardized residuals are very encouraging in case of the RHARCH model. |
| Keywords: | volatility modelling, volatility forecasting, ARCH, range-based volatility estimators, heterogeneity of volatility |
| JEL: | C13 C22 C53 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2014-06&r=ets |