nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒02‒21
thirteen papers chosen by
Yong Yin
SUNY at Buffalo

  1. Tests of Financial Market Contagion- Evolutionary Cospectral Analysis V.S. Wavelet Analysis By Zied Ftiti; Aviral Tiwari; Amél Belanès
  2. Conditional Correlations and Volatility Spillovers between Oil Price and OECD Stock index: a Multivariate Analysis By Anna Creti; Khaled Guesmi; Ilyes Abid
  3. Semiparametric Generalized Long Memory Modelling of GCC Stock Market Returns: A Wavelet Approach By Heni Boubaker; Nadia Sghaier
  4. Shock and Volatility Transmissions between Bank Stock Returns in Romania: Evidence from a VARGARCH Approach By Anissa Chaibi; Maria Ulici
  5. Cycles, Syllogisms and Semantics: Examining the Idea of Spurious Cycles By Stephen Pollock
  6. Trends Cycles and Seasons: Econometric Methods of Signal Extraction By Stephen Pollock
  7. Econometric Time Series Specification Testing in a Class of Multiplicative Error Models By Patrick W Saart; Jiti Gao; Nam Hyun Kim
  8. Specification Testing in Structural Nonparametric Cointegration By Chaohua Dong; Jiti Gao
  9. On The Theory and Practice of Singular Spectrum Analysis Forecasting By M. Atikur Rahman Khan; D.S. Poskitt
  10. Measurement of causality change between multiple time series By Ryo Kinoshita; Kosuke Oya
  12. Consistent estimation of breakpoints in time series, with application to wavelet analysis of Citigroup returns By Roberts, Leigh
  13. Testing for near I(2) trends when the signal to noise ratio is small By Juselius, Katarina

  1. By: Zied Ftiti; Aviral Tiwari; Amél Belanès
    Abstract: This paper examines the co-movements dynamics between OCDE countries with the US and Europe. The core focus is to suggest advantageous techniques allowing the investigation with respect to time and frequency, namely evolutionary co-spectral analysis and wavelet analysis. Our study puts in evidence the existence of both long run and short-run co-movements. Both interdependence and contagion are well identified across markets; but with slight differences. Both investors and policymakers can derive worthwhile information from this research. Recognizing countries sensitivity to permanent and transitory shocks enables investors to select rational investment strategies. Similarly, policymakers can make safe crisis management policies.
    Keywords: contagion, interdependence, stock markets index, evolutionary co-spectral analysis, wavelet analysis.
    Date: 2014–01–06
  2. By: Anna Creti; Khaled Guesmi; Ilyes Abid
    Abstract: This paper aims to explore the links between Brent crude oil index and stock markets index in OECD countries. We estimate time-varying conditional correlation relationships among these variables by employing a Multivariate Fractionally Integrated Asymmetric, Power ARCH model with dynamic corrected conditional correlations of Engle (1982) M-FIAPARCH-c-DCCE with a Student-t distribution. This process detects eventual volatility spillovers, asymmetries and persistence, which are typically observed in stock markets and oil prices. Our sample consists of monthly frequency stock indexes and oil price, covering 17 OECD countries for the period January, 1990- September, 2012. We find that at the beginning of our sample, oil has offered diversification opportunities with respect to the stock market, but this trend has been reversed in the last decade. We regroup the countries sample in 5 groups which present quite similar patterns of dynamic correlation between oil and their stock market and corroborate our geographical clustering by multivariate correlations among stock markets.
    Keywords: Multivariate Fractional Cointegration, Oil Prices, stock markets, M-FIAPARCH-c-DCCE.
    JEL: C10 E44 G15
    Date: 2014–01–06
  3. By: Heni Boubaker; Nadia Sghaier
    Abstract: This paper proposes a new class of semiparametric generalized long memory model with FIA- PARCH errors (SEMIGARMA-FIAPARCH model) that extends the conventionnel GARMA model to incorporate nonlinear deterministic trend, in the mean equation, and to allow for time varying volatility, in the conditional variance equation. The parameters of this model are estimated in a wavelet domain. We provide an empirical application of this model to examine the dynamic of the stock market returns in six GCC countries. The empirical results show that the model proposed o¤ers an interesting framework to describe the seasonal long range dependence and the nonlinear deterministic trend in the return as well as persistence to shocks in the conditional volatiliy. We also compare its performance predictive to the traditional long memory model with FIAPARCH errors (FARMA-FIAPARCH model). The predictive results indicate that the model proposed out performs the FARMA-FIAPARCH model.
    Keywords: semiparametric generalized long memory process, FIAPARCH errors, wavelet do- main, stock market returns.
    JEL: C13 C22 C32 G15
    Date: 2014–01–06
  4. By: Anissa Chaibi; Maria Ulici
    Abstract: We develop a VAR-GRACH approach to invesigate shock and volatility transmissions between bank stock returns in Romania during the 2007-2009 international financial crisis.Our findings provide eveidence of significant shock and volatility transmissions between Romanian bank returns.We also show how our empirical results can be used to build effective diversification and hedging strategies.
    Keywords: Shock and volatility transmission, financial crisis, Romanian banks.
    JEL: G1 G2 P2
    Date: 2014–02–12
  5. By: Stephen Pollock
    Abstract: The claim that linear filters are liable to induce spurious fluctuations has been repeated many times of late. However, there are good reasons for asserting that this cannot be the case for the filters that, nowadays, are commonly employed by econometricians. If these filters cannot have the effects that have been attributed to them, then one must ask what effects the filters do have that could have led to the aspersions that have been made against them.
    Keywords: business cycles, linear filters, spurious fluctuations
    JEL: C22 E32
    Date: 2014–02
  6. By: Stephen Pollock
    Abstract: Alternative methods of trend extraction and of seasonal adjustment are described that operate in the time domain and in the frequency domain. The time-domain methods that are implemented in the TRAMO–SEATS and the STAMP programs are described and compared. An abbreviated time-domain method of seasonal adjustment that is implemented in the IDEOLOG program is also described. Finite-sample versions of the Wiener–Kolmogorov filter are described that can be used to implement the methods in a common way. The frequency-domain method, which is also implemented in the IDEOLOG program, employs a ideal frequency selective filter that depends on identifying the ordinates of the Fourier transform of a detrended data sequence that should lie in the pass band of the filter and those that should lie in its stop band. Filters of this nature can be used both for extracting a low-frequency cyclical component of the data and for extracting the seasonal component.
    Keywords: Signal extraction, Linear filtering, Frequency-domain analysis. Seasonal Adjustment
    JEL: E32 C22
    Date: 2014–02
  7. By: Patrick W Saart; Jiti Gao; Nam Hyun Kim
    Abstract: In recent years, analysis of financial time series has focused largely on data related to market trading activity. Apart from modelling the conditional variance of returns within the GARCH family of models, presently attention has also been devoted to other market variables, especially volumes, number of trades and durations. The financial econometrics literature has focused on Multiplicative Error Models (MEMs), which are considered particularly suited for modelling certain financial variables. The paper establishes an econometric specification approach for MEMs. In the literature, several procedures are available to perform specification testing for MEMs, but the proposed specification testing method is particularly useful within the context of the MEMs of financial duration. The paper makes a number of important theoretical contributions. Both the proposed specification testing method and the associated theory are established and evaluated through simulations and real data examples.
    Keywords: Financial duration process; Nonnegative time series; Nonparametric kernel estimation; Semiparametric mixture model
    Date: 2014
  8. By: Chaohua Dong; Jiti Gao
    Abstract: This paper proposes two simple and new specification tests based on the use of an orthogonal series for a considerable class of cointegrated time series models with endogeneity and nonsta-tionarity. The paper then establishes an asymptotic theory for each of the proposed tests. The first test is initially proposed for the case where the regression function involved is integrable, which fills a gap in the literature, and the second test is an extended version of the first test for covering a class of non-integrable functions. Endogeneity in two general forms is allowed in the models to be tested. A potential global departure in the alternative hypothesis, which is being overlooked by the literature, is investigated. The finite sample performance of the proposed tests is examined through using several simulated examples. Meanwhile, the second test is naturally applicable to the case where there is a type of endogeneity inherited in the relationship between the United States aggregate consumers' consumption expenditure and disposable income over the period of 1960-2009. Our experience generally shows that the proposed tests are easily implementable and also have stable sizes and good power properties even when the 'distance' between the null hypothesis and a sequence of local alternatives is asymptotically negligible.
    Keywords: Consumption-income model; Endogeneity; Integrated time series; Linear process; Orthogonal series estimation; Parametric specification
    Date: 2014
  9. By: M. Atikur Rahman Khan; D.S. Poskitt
    Abstract: Theoretical results on the properties of forecasts obtained using singular spectrum analysis are presented in this paper. The mean squared forecast error is derived under broad regularity conditions, and it is shown that the forecasts obtained in practice will converge to their population ensemble counterparts. The theoretical results are illustrated by examining the performance of singular spectrum analysis forecasts when applied to autoregressive processes and a random walk process. Simulation experiments suggest that the asymptotic properties developed are reflected in observed finite sample behaviour. Empirical applications using real world data sets indicate that forecasts based on singular spectrum analysis are competitive with other methods currently in vogue.
    Keywords: Linear recurrent formula, Mean squared forecast error, Signal dimension, Window length.
    Date: 2014
  10. By: Ryo Kinoshita (Graduate School of Economics, Osaka University); Kosuke Oya (Graduate School of Economics & Center for the Study of Finance and Insurance, Osaka University)
    Abstract: Structural change is gauged with the change of parameters in the model. In the case of multiple time series model, the causality between the time series also changes when there is a structural change. However the magnitude of change in causality is not clear in the case of structural change. We explore the measure of causality change between the time series and propose the test statistic whether there is any significance change in the causal relationship using frequency domain causality measure given by Geweke (1982) and Hosoya (1991). These procedures can be applied to error correction model which is non-stationary time series. The properties of the measure and test statistic are examined through the Monte Carlo simulation. As an example of application, the change in causality between United states and Japanese stock indexes is tested.
    Keywords: Causality, Frequency domain, Error correction model, Structural breaks
    JEL: C01 C19
    Date: 2014–02
  11. By: Yasumasa Matsuda
    Abstract: This paper aims to provide a wavelet analysis for spatio-temporal data which are observed on irregularly spaced stations at discrete time points, where the spatial covariances show serious non-stationarity caused by local dependency. A specific example that is used for the demonstration is US precipitation data observed on about ten thousand stations in every month. By a reinterpretation of Whittle likelihood function for stationary time series, we propose a kind of Bayesian regression model for spatial data whose regressors are given by modified Haar wavelets and try a spatio-temporal extension by a state space approach. We also propose an empirical Bayes estimation for the parameters, which is regarded as a spatio-temporal extension of Whittle likelihood estimation originally defined for stationary time series. We conduct the extended Whittle estimate and compare mean square errors of the forecasts with those of some benchmarks to evaluate its goodness for the US precipitation data in August from 1987-1997.
    Date: 2014–01
  12. By: Roberts, Leigh
    Abstract: Simple and intuitive non-parametric methods are provided for estimating variance change points for time series data. Only slight alterations to existing open-source computer code applying CUSUM methods for estimating breakpoints are required to apply our proposed techniques. Our approach, apparently new in this context, is first to define two artificial time series of double the length of the original by reflective continuations of the original. We then search for breakpoints forwards and backwards through each of these symmetric extensions to the original time series. A novel feature of this paper is that we are able to identify common breakpoints for multiple time series, even when they collect data at different frequencies. In particular, our methods facilitate the reconciliation of breakpoint outputs from the two standard wavelet filters. Simulation results in this paper indicate that our methods produce accurate results for time series exhibiting both long and short term correlation; and we illustrate by an application to Citigroup stock returns for the last thirty years.
    Keywords: Breakpoint, Variance change point;, Model-free, Non-parametric, R programming suite, R package waveslim, Wavelets, DWT (discrete wavelet transform), MODWT (maximal overlap discrete wavelet transform), MRA (multiresolution analysis), CUSUM (cumulative sum of squares), Cluster analysis, Change point,
    Date: 2014
  13. By: Juselius, Katarina
    Abstract: Researchers seldom find evidence of I(2) in exchange rates, prices, and other macroeconomics time series when they test the order of integration using univariate Dickey-Fuller tests. In contrast, when using the multivariate ML trace test we frequently find double unit roots in the data. Our paper demonstrates by simulations that this often happens when the signal-to-noise-ratio is small. --
    Keywords: univariate and multivariate unit root tests,double unit roots,near I(2)
    JEL: C1 C18 C22 C32 C52
    Date: 2014

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