The 14 references with contexts in paper John T. Barkoulas, Christopher F. Baum, Nickolaos Travlos (1996) “Long Memory in the Greek Stock Market” / RePEc:boc:bocoec:356

3
Cheung, Y. W., 1993, Tests for fractional integration: A Monte Carlo investigation, Journal of Time Series Analysis 14, 331-345.
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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).

5
Crato, N., 1994, Some international evidence regarding the stochastic behavior of stock returns, Applied Financial Economics 4, 33-39.
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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.

11
Hassler, U., 1993, Regression of spectral estimators with fractionally integrated time series, Journal of Time Series Analysis 14, 369-380. 16
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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.

12
Hosking, J. R. M., 1981, Fractional differencing, Biometrika 68, 165-176.
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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.

14
Lo, A. W., 1991, Long-term memory in stock market prices, Econometrica 59, 1279-1313.
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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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    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.

15
Mandelbrot, B. B., 1971, When can price be arbitraged efficiently? A limit to the validity of the random walk and martingale models, Review of Economics and Statistics 53, 225-236.
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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.

16
Panas, E., 1990, The Behavior of the Athens stock prices, Applied Economics 22, 1715-1727.
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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.

17
Papaioannou, G. J., 1982, Thinness and short-run price dependence in the Athens stock exchange, Greek Economic Review, 315-333.
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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.

18
Papaioannou, G. J., 1984, Informational efficiency tests in the Athens stock market, in G. A. Hawawini and P. A. Michel, eds., European Equity Markets: Risk, Return, and Efficiency (Garland Publishing, Inc., New York) 367-381.
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  1. In-text reference with the coordinate start=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.

19
Ray, B., 1993, Long range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model, International Journal of Forecasting 9, 255-269.
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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

20
Robinson, P., 1990, Time series with strong dependence, Advances in Econometrics, 6th World Congress (Cambridge University Press, Cambridge).
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  1. In-text reference with the coordinate start=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.

22
Travlos, N., 1992, Athens stock exchange: Creation of a stock market data bank and risk-returns characteristics for the period 1981-1990, University of 17 Piraeus, unpublished monograph.
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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.

23
Wright, J. H., 1995, Stochastic orders of magnitude associated with two-stage estimators of fractional ARIMA systems, Journal of Time Series Analysis 16, 119-125.
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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

24
Yajima, Y., 1985, Estimation of long memory time series models, Australian Journal of Statistics 27, 303-320. 18
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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.