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Is there a relationship between the time scaling property of asset returns and the outliers? Evidence from international financial markets

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  • González-Sánchez, Mariano

Abstract

Stylized facts are statistical properties present in high frequency returns of financial assets. While some of them supposes that returns are not Gaussian, another, called time scaling, involves that decreasing the frequency of observation, the returns converge to normal distribution. This paper find evidence that the existence of scaling and outliers entails other stylized facts. Also, a methodology for identifying outliers is proposed and applied to both simulated series and 1300 market assets. Results indicate that all market returns have time scaling (between 2 and 28 days) and, in 95% of cases, daily outliers represent less than 6% of observations.

Suggested Citation

  • González-Sánchez, Mariano, 2021. "Is there a relationship between the time scaling property of asset returns and the outliers? Evidence from international financial markets," Finance Research Letters, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319306774
    DOI: 10.1016/j.frl.2020.101510
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    1. Pena, Daniel, 1990. "Influential Observations in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 235-241, April.
    2. Amélie Charles, 2008. "Forecasting volatility with outliers in GARCH models," Post-Print hal-00765466, HAL.
    3. Kim, Yunmi & Kim, Tae-Hwan & Ergün, Tolga, 2015. "The instability of the Pearson correlation coefficient in the presence of coincidental outliers," Finance Research Letters, Elsevier, vol. 13(C), pages 243-257.
    4. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    7. Park, Beum-Jo, 2002. "An Outlier Robust GARCH Model and Forecasting Volatility of Exchange Rate Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(5), pages 381-393, August.
    8. Philip Hans Franses & Dick van Dijk & Andre Lucas, 2004. "Short patches of outliers, ARCH and volatility modelling," Applied Financial Economics, Taylor & Francis Journals, vol. 14(4), pages 221-231.
    9. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2016. "Empirical Likelihood for Outlier Detection and Estimation in Autoregressive Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 315-336, May.
    10. Shinichi Sakata & Halbert White, 1998. "High Breakdown Point Conditional Dispersion Estimation with Application to S&P 500 Daily Returns Volatility," Econometrica, Econometric Society, vol. 66(3), pages 529-568, May.
    11. Franses, Philip Hans & Ghijsels, Hendrik, 1999. "Additive outliers, GARCH and forecasting volatility," International Journal of Forecasting, Elsevier, vol. 15(1), pages 1-9, February.
    12. Ahmad Yamin & Donayre Luiggi, 2016. "Outliers and persistence in threshold autoregressive processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 37-56, February.
    13. Van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for Smooth Transition Nonlinearity in the Presence of Outliers," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(2), pages 217-235, April.
    14. Amélie Charles, 2008. "Forecasting volatility with outliers in GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 551-565.
    15. Michael R. Elliott & Nicolas Stettler, 2007. "Using a mixture model for multiple imputation in the presence of outliers: the ‘Healthy for life’ project," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(1), pages 63-78, January.
    16. Min-Hsien Chiang & Ray Yeutien Chou & Li-Min Wang, 2016. "Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 126-144, February.
    17. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    Keywords

    Stylized facts; Time scaling; Outlier; Heteroskedasticity; Leptokurtosis;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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