We present a multifarious view on the presence of long memory across 33 countries, subsampled as developed, emerging and frontier economies for the period 2000–2018. We employ the classical rescaled range test, and two semi-parametric tests proposed by Geweke Porter-Hudak (1983) and the frequency domain test proposed by Robinson (1995) to decipher the presence of long memory. The results confirm that while there exists no long-range dependence for developed countries, the return series in the emerging and the frontier countries display long memory characteristics. The rationale for such result emerges from market quality parameters such as size, liquidity, trading and settlement mechanism or sound regulatory framework. These results are of importance for the global investor community for asset allocation, risk management and portfolio diversification purposes.
AbbrittiM., Gil-AlanaL. A., LovchaY., & MorenoA. (2016). Term structure persistence. Journal of Financial Econometrics, 14(2), 331–352.
2.
AliS., ShahzadS. J. H., RazaN., & Al-YahyaeeK. H. (2018). Stock market efficiency: A comparative analysis of Islamic and conventional stock markets. Physica A: Statistical Mechanics and Its Applications, 503, 139–153.
3.
AssafA. (2008). Nonstationarity in real exchange rates using unit root tests with a level shift at unknown time. International Review of Economics & Finance, 17(2), 269–278.
4.
BaillieR. T. (1996). Long memory processes and fractional integration in econometrics. Journal of Econometrics, 73(1), 5–59.
5.
BarivieraA. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1–4.
6.
BarkoulasJ. T., & BaumC. F. (1996). Long-term dependence in stock returns. Economics Letters, 53(3), 253–259.
7.
BarkoulasJ. T., LabysW. C., & OnochieJ. I. (1999). Long memory in futures prices. Financial Review, 34(1), 91–100.
8.
BattenJ. A., HoganW. P., & JacobyG. (2005). Measuring credit spreads: Evidence from Australian Eurobonds. Applied Financial Economics, 15(9), 651–666.
9.
BennettV. M., & GartenbergC. M. (2016). Changes in persistence of performance over time(Research Paper, 2016-41). Duke I&E.
10.
BergL., & LyhagenJ. (1998). Short and long-run dependence in Swedish stock returns. Applied Financial Economics, 8(4), 435–443.
11.
BouriE., Gil-AlanaL. A., GuptaR., & RoubaudD. (2019). Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks. International Journal of Finance & Economics, 24(1), 412–426.
CoakleyJ., KellardN., & WangJ. (2016). Commodity futures returns: More memory than you might think. The European Journal of Finance, 22(14), 1457–1483.
14.
CorazzaM., & MalliarisA. T. G. (2002). Multi-fractality in foreign currency markets. Multinational Finance Journal, 6(2), 65–98.
15.
CratoN., & RayB. K. (2000). Memory in returns and volatilities of futures’ contracts. Journal of Futures Markets, 20(6), 525–543.
16.
DusakK. (1973). Futures trading and investor returns: An investigation of commodity market risk premiums. Journal of Political Economy, 81(6), 1387–1406.
17.
ElderJ., & JinH. J. (2009). Fractional integration in commodity futures returns. Financial Review, 44(4), 583–602.
18.
FamaE. F., & FrenchK. R. (1988). Permanent and temporary components of stock prices. Journal of Political Economy, 96(2), 246–273.
19.
FamaE. F., & FrenchK. R. (1988). Permanent and temporary components of stock prices. Journal of Political Economy, 96(2), 246–273.
20.
FernandezV. (2010). Commodity futures and market efficiency: A fractional integrated approach. Resources Policy, 35(4), 276–282.
21.
GewekeJ., & Porter-HudakS. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4(4), 221–238.
22.
Gil-AlanaL. A. (2006). Fractional integration in daily stock market indexes. Review of Financial Economics, 15(1), 28–48.
23.
GrangerC. W., & DingZ. (1996). Varieties of long memory models. Journal of Econometrics, 73(1), 61–77.
24.
GrangerC. W., & JoyeuxR. (1980). An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis, 1(1), 15–29.
25.
GrechD., & MazurZ. (2005). Statistical properties of old and new techniques in detrended analysis of time series. Acta Physica Polonica Series B, 36(8), 2403.
26.
HoskingJ. R. M. (1981). Lagrange-multiplier tests of multivariate time-series models. Journal of the Royal Statistical Society: Series B (Methodological), 43(2), 219–230.
27.
HurstH. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineering, 116, 770–799.
28.
HurstH. E. (1957). A suggested statistical model of some time series which occur in nature. Nature, 180(4584), 494–494.
29.
JacobsenB. (1996). Long term dependence in stock returns. Journal of Empirical Finance, 3(4), 393–417.
30.
KimM. J., NelsonC. R., & StartzR. (1991). Mean reversion in stock prices? A reappraisal of the empirical evidence. The Review of Economic Studies, 58(3), 515–528.
31.
KristjanpollerW., & BouriE. (2019). Asymmetric multifractal cross-correlations between the main world currencies and the main cryptocurrencies. Physica A: Statistical Mechanics and Its Applications, 523, 1057–1071.
32.
KristjanpollerW., BouriE., & TakaishiT. (2020). Cryptocurrencies and equity funds: Evidence from an asymmetric multifractal analysis. Physica A: Statistical Mechanics and Its Applications, 545, 123711.
33.
KrištoufekL. (2010). Rescaled range analysis and detrended fluctuation analysis: Finite sample properties and confidence intervals. Czech Economic Review, 4(3), 315–329.
34.
LarsonA. B. (1960 August). Measurement of a random process in futures prices. In Proceedings of the Annual Meeting (Western Farm Economics Association) (Vol. 33, pp. 101–112). Western Agricultural Economics Association.
35.
LeRoyS. F. (1989). Efficient capital markets and martingales. Journal of Economic Literature, 27(4), 1583–1621.
36.
LoA. W. (1991). Long term memory in stock market prices. Econometrica, 59(5), 1279–1313.
37.
LuY. K., & PerronP. (2010). Modeling and forecasting stock return volatility using a random level shift model. Journal of Empirical Finance, 17(1), 138–156.
38.
MaheswaranS. (1990). Predictable short-term variation in asset prices: Theory and evidence (Working Paper). Carlson School of Management, University of Minnesota.
39.
MandelbrotB. (1972). Statistical methodology for nonperiodic cycles: From the covariance to R/S analysis. Annals of Economic and Social Measurement, 1(3), 259–290.
40.
MandelbrotB. B., & WallisJ. R. (1968). Noah, Joseph, and operational hydrology. Water Resources Research, 4(5), 909–918.
41.
McLeodA. I., & HipelK. W. (1978). Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon. Water Resources Research, 14(3), 491–508.
42.
MensiW., HammoudehS., ShahzadS. J. H., & ShahbazM. (2017). Modeling systemic risk and dependence structure between oil and stock markets using a variational mode decomposition-based copula method. Journal of Banking & Finance, 75, 258–279.
43.
MynhardtH. R., PlastunA., & MakarenkoI. (2014). Behavior of financial markets efficiency during the financial market crisis: 2007–2009. Corporate Ownership and Control, 11(2F), 531–546.
44.
NiuH., & WangJ. (2014). Financial time series prediction by a random data-time effective RBF neural network. Soft Computing, 18(3), 497–508.
45.
NystrupP., MadsenH., & LindströmE. (2017). Long memory of financial time series and hidden Markov models with time-varying parameters. Journal of Forecasting, 36(8), 989–1002.
46.
OdonkorA. A., AbabioE. N., DarkwahE. A., & AndohR. (2019). Stock returns and long-range dependence. Global Business Review, https://doi.org/10.1177/0972150919866966
47.
OdonkorA. A., AbabioE. N., DarkwahE. A., & AndohR. (2019). Stock returns and long-range dependence. Global Business Review, https://doi.org/10.1177/0972150919866966
48.
PaganA. (1996). The econometrics of financial markets. Journal of Empirical Finance, 3(1), 15–102.
49.
PalmaW. (2007). Long-memory time series: Theory and methods (Vol. 662). John Wiley & Sons.
50.
PhillipA., ChanJ., & PeirisS. (2019). On long memory effects in the volatility measure of cryptocurrencies. Finance Research Letters, 28, 95–100.
51.
PoterbaJ. M., & SummersL. H. (1988). Mean reversion in stock prices: Evidence and implications. Journal of Financial Economics, 22(1), 27–59.
52.
RichardsonM. (1993). Temporary components of stock prices: A skeptic’s view. Journal of Business & Economic Statistics, 11(2), 199–207.
53.
RichardsonM., & StockJ. H. (1989). Drawing inferences from statistics based on multiyear asset returns. Journal of Financial Economics, 25(2), 323–348.
54.
RobinsonP. M. (1995). Gaussian semiparametric estimation of long range dependence. The Annals of Statistics, 23(5), 1630–1661.
55.
SerletisA., & RosenbergA. A. (2007). The Hurst exponent in energy futures prices. Physica A: Statistical Mechanics and Its Applications, 380, 325–332.
56.
ShahzadS. J. H., BouriE., KayaniG. M., NasirR. M., & KristoufekL. (2020). Are clean energy stocks efficient? Asymmetric multifractal scaling behaviour. Physica A: Statistical Mechanics and Its Applications, 550, 124519.
57.
SinghA., & ChakrabortyM. (2017). Examining efficiencies of Indian ADRs and their underlying stocks. Global Business Review, 18(1), 144–162.
58.
StevensonR. A., & BearR. M. (1970). Commodity futures: Trends or random walks. The Journal of Finance, 25(1), 65–81.
59.
WeronR. (2002). Estimating long-range dependence: Finite sample properties and confidence intervals. Physica A: Statistical Mechanics and Its Applications, 312(1–2), 285–299.
60.
WoldH. (1938). A study in the analysis of stationary time series(Doctoral dissertation). Almqvist & Wiksell.
