The 22 reference contexts in paper John T. Barkoulas, Christopher F. Baum, Nickolaos Travlos (1996) “Long Memory in the Greek Stock Market” / RePEc:boc:bocoec:356

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    2157
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    The presence of long memory in asset returns contradicts the weak form of the market efficiency hypothesis, which states that, conditioning on historical returns, future asset returns are unpredictable.1 A number of studies have tested the long-memory hypothesis for stock market returns. Using the rescaled-range (R/S) method,
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    Greene and Fielitz (1977)
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    report evidence of persistence in daily U.S. stock returns series. A problem with the classical R/S method is that the distribution of its test 1 The existence of long memory in asset returns calls into question linear modeling and invites the development of nonlinear pricing models at the theoretical level to account for long-memory behavior.
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    2580
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    A problem with the classical R/S method is that the distribution of its test 1 The existence of long memory in asset returns calls into question linear modeling and invites the development of nonlinear pricing models at the theoretical level to account for long-memory behavior.
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    Mandelbrot (1971)
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    observes that in the presence of long memory, the arrival of new market information cannot be fully arbitraged away and martingale models of asset prices cannot be obtained from arbitrage.
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    3165
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    In addition, pricing derivative securities with martingale methods may not be appropriate if the underlying continuous stochastic processes exhibit long memory. Statistical inferences concerning asset pricing models based on standard testing procedures may not be appropriate in the presence of long-memory series
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    (Yajima (1985)).
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    statistic is not well defined and is sensitive to short-term dependence and heterogeneities of the underlying data generating process. These dependencies bias the classical R/S test toward finding long memory too frequently.
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    3437
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    models based on standard testing procedures may not be appropriate in the presence of long-memory series (Yajima (1985)). statistic is not well defined and is sensitive to short-term dependence and heterogeneities of the underlying data generating process. These dependencies bias the classical R/S test toward finding long memory too frequently.
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    Lo (1991)
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    developed a modified R/S method which addresses these drawbacks of the classical R/S method. Using this variant of R/S analysis, Lo (1991) finds no evidence to support the presence of long memory in U.
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    3594
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    These dependencies bias the classical R/S test toward finding long memory too frequently. Lo (1991) developed a modified R/S method which addresses these drawbacks of the classical R/S method. Using this variant of R/S analysis,
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    Lo (1991)
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    finds no evidence to support the presence of long memory in U.S. stock returns. Using both the modified R/S method and the spectral regression method (described below), Cheung and Lai (1995) find no evidence of persistence in several international stock returns series.
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    3798
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    Using this variant of R/S analysis, Lo (1991) finds no evidence to support the presence of long memory in U.S. stock returns. Using both the modified R/S method and the spectral regression method (described below),
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    Cheung and Lai (1995)
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    find no evidence of persistence in several international stock returns series. Crato (1994) reports similar evidence for the stock returns series of the G-7 countries using exact maximum likelihood estimation.
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    3912
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    Using both the modified R/S method and the spectral regression method (described below), Cheung and Lai (1995) find no evidence of persistence in several international stock returns series.
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    Crato (1994)
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    reports similar evidence for the stock returns series of the G-7 countries using exact maximum likelihood estimation. The primary focus of these studies has been the stochastic long-memory behavior of stock returns in major capital markets.
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    8401
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    The introduction of new financial instruments, like warrants, options, commercial paper, etc. is currently under way. There is no capital gains tax in Greece. There has been limited research on the behavior of stocks traded on the ASE.
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    Papaioannou (1982, 1984)
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    reports price dependencies in stock returns for a period of at least six days. Panas (1990) provides evidence of weak-form efficiency for ten large Greek firms. Koutmos, Negakis, and Theodossiou (1993) find that an exponential generalized ARCH model is an adequate representation of volatility in weekly Greek stock returns.
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    8520
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    There has been limited research on the behavior of stocks traded on the ASE. Papaioannou (1982, 1984) reports price dependencies in stock returns for a period of at least six days.
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    Panas (1990)
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    provides evidence of weak-form efficiency for ten large Greek firms. Koutmos, Negakis, and Theodossiou (1993) find that an exponential generalized ARCH model is an adequate representation of volatility in weekly Greek stock returns.
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    8944
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    Koutmos, Negakis, and Theodossiou (1993) find that an exponential generalized ARCH model is an adequate representation of volatility in weekly Greek stock returns. The intertemporal relation between the U.S. and Greek stock markets is analyzed in Theodossiou, Koutmos, and Negakis (1993).
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    Barkoulas and Travlos (1996)
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    test whether Greek stock returns are characterized by deterministic nonlinear structure (chaos). In this paper, we test for the presence of fractional dynamics, or long memory, in the returns series for the Greek stock market.
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    11283
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    The stochastic process ty is both stationary and invertible if all roots of Φ(L) and Θ(L) lie outside the unit circle and d<0. 5. The process is nonstationary for d≥0. 5, as it possesses infinite variance, i.e. see
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    Granger and Joyeux (1980).
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    Assuming that d∈0, 0.5() and d≠0, Hosking (1981) showed that the correlation function, ρ(⋅), of an ARFIMA process is proportional to 2d−1k as k→∞. Consequently, the autocorrelations of the ARFIMA process decay hyperbolically to zero as k→∞ which is contrary to the faster, geometric decay of a stationary ARMA process.
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    11344
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    The stochastic process ty is both stationary and invertible if all roots of Φ(L) and Θ(L) lie outside the unit circle and d<0. 5. The process is nonstationary for d≥0. 5, as it possesses infinite variance, i.e. see Granger and Joyeux (1980). Assuming that d∈0, 0.5() and d≠0,
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    Hosking (1981)
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    showed that the correlation function, ρ(⋅), of an ARFIMA process is proportional to 2d−1k as k→∞. Consequently, the autocorrelations of the ARFIMA process decay hyperbolically to zero as k→∞ which is contrary to the faster, geometric decay of a stationary ARMA process.
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    12196
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    dependence.2 The process exhibits intermediate memory, or long-range negative dependence for d∈−0. 5, 0() and short memory for d=0, corresponding to a stationary and invertible ARMA model. For d∈0. 5, 1[) the process is mean reverting, even though it is not covariance stationary, as there is no long run impact of an innovation to future values of the process.
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    Geweke and Porter-Hudak (1983)
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    suggested a semi-parametric procedure to obtain an estimate of the fractional differencing parameter d based on the slope of the spectral density function around the angular frequency ξ=0.
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    13138
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    λξ= 2πλ T ()λ=0,...,T−1 denotes the Fourier frequencies of the sample, T is the number of observations, and ν = gT() << T is the number of Fourier frequencies included in the spectral regression. Assuming that T→∞ limgT()=∞, T→∞ lim gT() T       =0, and T→∞ lim lnT()2 gT() =0, the negative of the slope coefficient in (4) provides an estimate of d.
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    Geweke and Porter-Hudak (1983)
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    prove consistency and asymptotic normality for d<0, while Robinson (1990) proves consistency for d∈0, 0.5(). Hassler (1993) proves consistency and asymptotic normality in the case of Gaussian innovations in (1).
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    13235
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    Assuming that T→∞ limgT()=∞, T→∞ lim gT() T       =0, and T→∞ lim lnT()2 gT() =0, the negative of the slope coefficient in (4) provides an estimate of d. Geweke and Porter-Hudak (1983) prove consistency and asymptotic normality for d<0, while
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    Robinson (1990)
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    proves consistency for d∈0, 0.5(). Hassler (1993) proves consistency and asymptotic normality in the case of Gaussian innovations in (1). The spectral regression estimator is not 1/ 2T consistent as it converges at a slower rate.
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    13292
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    Assuming that T→∞ limgT()=∞, T→∞ lim gT() T       =0, and T→∞ lim lnT()2 gT() =0, the negative of the slope coefficient in (4) provides an estimate of d. Geweke and Porter-Hudak (1983) prove consistency and asymptotic normality for d<0, while Robinson (1990) proves consistency for d∈0, 0.5().
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    Hassler (1993)
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    proves consistency and asymptotic normality in the case of Gaussian innovations in (1). The spectral regression estimator is not 1/ 2T consistent as it converges at a slower rate. The theoretical variance of the error term in the spectral regression is known to be π2 6 . 3.
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    13885
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    Data and Empirical Estimates The data set consists of weekly stock returns based on the closing prices of a value-weighted index comprised of the thirty most heavily traded stocks (during the period 1988-1990) on the Athens Stock Exchange (ASE30) developed by
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    Travlos (1992).
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    The sample period spans 01/07/1981 to 12/27/1990 for a total of 521 weekly observations. The period 01/07/1981 to 10/11/1989 is used for in-sample estimation with the remaining observations used for out-of-sample forecasting.
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    20404
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    The nonlinear model construction suggested is that of an ARFIMA process, which represents a flexible and parsimonious way to model both the short- and long-term dynamic properties of the series. Granger and 4 Through extensive Monte Carlo simulations,
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    Cheung (1993) and
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    Agiakloglou, Newbold, and Wohar (1993) found the spectral regression test to be biased toward finding long memory ()d>0 in the presence of infrequent shifts in the mean of the process and large AR parameters (0.7 and higher).
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    21009
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    We investigated the potential presence of these bias-inducing data features in the ASE30 returns series and found that neither a shift in mean nor strong short-term dynamics are responsible for detecting long memory in the Greek stock market. 10 Joyeux (1980) have discussed the forecasting potential of such nonlinear models and
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    Geweke and Porter-Hudak (1983)
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    have confirmed this by showing that ARFIMA models provide more reliable out-of-sample forecasts than do traditional procedures. The possibility of speculative profits due to superior long-memory forecasts would cast serious doubt on the basic tenet of market efficiency: unpredictability of future returns.
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    22462
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    selected on the basis of statistical significance of the coefficient estimates and Q statistics for serial dependence (the AR order chosen in each case is given in subsequent tables). A question arises as to the asymptotic properties of the AR parameter estimates in the second stage. Conditioning on the d estimate obtained in the first stage,
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    Wright (1995)
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    shows that the ARp() fitted by the Yule-Walker procedure to the d- differenced series inherit the δT-consistency of the semiparametric estimate of d. The Greek stock returns series is forecast by casting the fitted fractional-AR model in infinite autoregressive form, truncating the infinite autoregression at the beginning of the sample, and applying Wol
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    22931
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    The Greek stock returns series is forecast by casting the fitted fractional-AR model in infinite autoregressive form, truncating the infinite autoregression at the beginning of the sample, and applying Wold's chain rule. A similar procedure was followed by
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    Ray (1993) to
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    forecast IBM product revenues and Diebold and Lindner (1996) to forecast the real interest rate. The 11 long-memory forecasts are compared to those obtained by estimating two standard linear models: a random walk (RW) model, as suggested by the market efficiency hypothesis in its weak form, and an autoregressive (AR) model fit to the ASE30 returns
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    22985
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    The Greek stock returns series is forecast by casting the fitted fractional-AR model in infinite autoregressive form, truncating the infinite autoregression at the beginning of the sample, and applying Wold's chain rule. A similar procedure was followed by Ray (1993) to forecast IBM product revenues and
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    Diebold and Lindner (1996) to
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    forecast the real interest rate. The 11 long-memory forecasts are compared to those obtained by estimating two standard linear models: a random walk (RW) model, as suggested by the market efficiency hypothesis in its weak form, and an autoregressive (AR) model fit to the ASE30 returns series according to the Akaike information criterion (AIC).
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