Improving detectability of the indicator saturation approach through winsorization: an empirical study in the cryptocurrency market

Suleiman Dahir Mohamed; Mohd Tahir  Ismail; Majid Khan Bin Majahar Ali

doi:https://doi.org/10.59139/stattrans-2025-009

Improving detectability of the indicator saturation approach through winsorization: an empirical study in the cryptocurrency market

Suleiman Dahir Mohamed School of Mathematical Sciences, Universiti Sains Malaysia, Minden, Pulau Pinang, 11800, Malaysia ORCID:http://orcid.org/0000-0002-3368-204X , Mohd Tahir Ismail School of Mathematical Sciences, Universiti Sains Malaysia, Minden, Pulau Pinang, 11800, Malaysia ORCID:http://orcid.org/0000-0003-2747-054X , Majid Khan Bin Majahar Ali School of Mathematical Sciences, Universiti Sains Malaysia, Minden, Pulau Pinang, 11800, Malaysia ORCID:http://orcid.org/0000-0002-5558-5929 Statistics in Transition new series, vol. 26, 2025, 1, pages: 155-181 Published online: 10 March 2025 https://doi.org/10.59139/stattrans-2025-009 Citation: Mohamed S. D., Ismail M. T., Ali M. K. B. M., 2025. Improving detectability of the indicator saturation approach through winsorization: an empirical study in the cryptocurrency market. Statistics in Transition new series, 26(1), pp. 155-181. https://doi.org/10.59139/stattrans-2025-009

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ABSTRACT

Despite the introduction of several adjustments, mitigating data anomalies in financial datasets has proven challenging, particularly in the context of cryptocurrencies with extreme values and increased volatility. The progress in properly addressing these anomalies prior to testing remains restricted, highlighting the unique and complex nature of financial data in this domain. Thus, in this paper we propose a hybrid approach called the Win-IS strategy. It is meant to address the influence of extreme outliers in the tail and subsequently identify breaks, trend breaks and outliers in cryptocurrencies. This methodology uses the winsorization (Win) process to enhance the effectiveness of the indicator saturation (IS) approach. The study uses cryptocurrencies like Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Tether (USDT), and Ripple (XRP). The results of the research indicate that the winsorization strategy improved the detectability of the IS approach, with Win-IS outperforming the IS method in terms of the Bayesian Information Criterion. Furthermore, the Win-IS technique uncovered additional breaks, trend breaks and outliers that were previously unknown and repeated in some cases as detected by the IS strategy. The effect of winsorization is dependent on the chosen percentile and dataset attributes. Through detailed examination and comparison, the findings of this research contribute to the improvement of other detection approaches, providing a valuable perspective for researchers and practitioners in the field. Additionally, this hybrid approach can improve decision-making, risk management and model creation, benefiting investors, legislators and scholars.

KEYWORDS

breaks, outliers, winsorization, indicator saturation, cryptocurrency.

JEL

C22, C58, C61, G23, G32.

REFERENCES

Abakah, E. J. A., Gil-Alana, L. A., Madigu, G. and Romero-Rojo, F., (2020). Volatility persistence in cryptocurrency markets under structural breaks. International Review of Economics & Finance, 69, pp. 680–691. https://doi.org/10.1016/j.iref.2020.06.035.

Adams, J., Hayunga, D., Mansi, S., Reeb, D. and Verardi, V., (2019). Identifying and treating outliers in finance. Financial Management, 48(2), pp. 345–384. https://doi.org/ 10.1111/fima.12269.

Afanasyev, D. O., Fedorova, E. A., (2019). On the impact of outlier filtering on the electricity price forecasting accuracy. Applied Energy, 236, pp. 96–210. https://doi.org/10.1016/j.apenergy.2018.11.076.

Aharon, D. Y., Butt, H. A., Jaffri, A. and Nichols, B., (2023). Asymmetric volatility in the cryptocurrency market: New evidence from models with structural breaks. International Review of Financial Analysis, 87, pp. 102651. https://doi.org/10.1016/j.irfa.2023.102651.

Ané, T., Ureche-Rangau, L., Gambet, J. B. and Bouverot, J., (2008). Robust outlier detection for Asia–Pacific stock index returns. Journal of International Financial Markets, Institutions and Money, 18(4), pp. 326–343. https://doi.org/10.1016/j.intfin.2007. 03.001.

Bai, J., Perron, P., (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), pp. 47–78. http://doi/10.2307/2998540.

Bai, J., Perron, P., (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), pp. 1–22. https://doi.org/10.1002/jae.659.

Brooks, C., (2019). Introductory Econometrics for Finance, Fourth Edition. Cambridge University Press. https://doi.org/10.1017/9781108524872.

Brownen-Trinh, R., (2019). Effects of winsorization: The cases of forecasting non- GAAP and GAAP earnings. Journal of Business Finance & Accounting, 46(1–2), pp. 105–135. https://doi.org/10.1111/jbfa.12365.

Canh, N. P., Wongchoti, U., Thanh, S. D. and Thong, N. T., (2019). Systematic risk in cryptocurrency market: Evidence from DCC-MGARCH model. Finance Research Letters, 29, pp. 90–100. https://doi.org/10.1016/j.frl.2019.03.011.

Castle, J. L., Fawcett, N. W. and Hendry, D. F., (2011). Forecasting breaks and forecasting during breaks. The Oxford Handbook of Economic Forecasting. https://doi.org/ 10.1093/oxfordhb/9780195398649.013.0012.

Castle, J. L., Doornik, J. A. and Hendry, D. F., (2012). Model selection when there are multiple breaks. Journal of Econometrics, 169(2), pp. 239–246. https://doi.org/10.1016/j.jeconom.2012.01.026.

Castle, J. L., Doornik, J. A., Hendry, D. F. and Pretis, F., (2015). Detecting Location Shifts During Model Selection by Step-Indicator Saturation. Econometrics, 3(2), pp. 240–264. DOI: 10. 3390/econometrics3020240.

Castle, J. L., Doornik, J. A. and Hendry, D. F., (2021). Forecasting facing economic shifts, climate change and evolving pandemics. Econometrics, 10(1), 2. https://doi.org/ 10.3390/econometrics10010002.

Castle, J.L., Hendry, D.F., (2022). Econometrics for modelling climate change. Oxford Research Encyclopedia of Economics and Finance. https://doi.org/10.1093/acrefore/9780190625979.013.675.

Chatzikonstanti, V., (2017). Breaks and outliers when modelling the volatility of the US stock market. Applied Economics, 49(46), pp. 4704-4717.

Cheng, K., Young, D. S., (2023). An Approach for Specifying Trimming and Winsorization Cutoffs. Journal of Agricultural, Biological and Environmental Statistics, 28, pp. 299–323. https://doi.org/10.1007/s13253-023-00527-4.

Choi, S. W., (2009). The effect of outliers on regression analysis: regime type and foreign direct investment. Quarterly Journal of Political Science, 4(2), pp. 153–165.

Chow, G.C., (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), pp. 591–605. https://doi.org/10.2307/1910133.

Dixon, W. J., (1960). Simplified estimation from censored normal samples. The Annals of Mathematical Statistics, pp. 385–391.

Dutta, A., Bouri, E., (2022). Outliers and time-varying jumps in the cryptocurrency markets. Journal of Risk and Financial Management, 15(3), pp.128. https://doi.org/10.3390/jrfm15030128.

Fearnhead, P., Rigaill, G., (2019). Changepoint detection in the presence of outliers. Journal of the American Statistical Association, 114(525), pp. 169-183. https://doi.org/10.1080/01621459.2017.1385466.

Ghouse, G., Aslam, A. and Bhatti, M. I., (2021). Role of Islamic banking during COVID-19 on political and financial events: Application of impulse indicator saturation. Sustainability, 13(21), pp. 11619. https://doi.org/10.3390/su132111619.

Ghouse, G., Bhatti, M. I. and Shahid, M. H., (2022). Impact of COVID-19, political, and financial events on the performance of commercial banking sector. Journal of Risk and Financial Management, 15(4), pp. 186. https://doi.org/10.3390/jrfm15040186.

Hamadani, A., Ganai, N. A., (2023). Artificial intelligence algorithm comparison and ranking for weight prediction in sheep. Scientific Reports, 13(1), pp. 13242. https://doi.org/10.1038/s41598-023-40528-4.

Hawkins, D. M., (1980). Identification of outliers. London: Chapman and Hall. https://doi.org/10.1007/978-94-015-3994-4.

Hendry, D. F., (1999). An econometric analysis of US food expenditure, 1931–1989. In J. R. Magnus, & M. S. Morgan (Eds.). Methodology and tacit knowledge: two experiments in econometrics, pp. 341–361. Chichester: John Wiley and Sons.

Hendry, D. F., Johansen, S., Santos, C., (2008). Automatic Selection of Indicators in a Fully Saturated Regression. Computational Statistics, 23(2), pp. 317–335. doi:10.1007/s00180-007-0054-z.

Inclán, C., Tiao, G. C., (1994). Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association, 89(427), pp. 913–923. https://doi.org/10.1080/01621459.1994.10476824.

Ismail, M. T., Nasir, I. N. M., (2018). Structural breaks and outliers in ASEAN Shariah compliant indices: The impulse indicator saturation approach. In AIP Conference Proceedings 1 2013.

Jiang, Z., Mensi, W. and Yoon, S. M., (2023). Risks in Major Cryptocurrency Markets: Modeling the Dual Long Memory Property and Structural Breaks. Sustainability, 15(3), pp. 2193. https://doi.org/10.3390/su15032193.

Johansen, S., Nielsen, B., (2016). Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models. Scandinavian Journal of Statistics, 43(2), pp. 321–348. doi: 10.1111/sjos.12174.

Kaseke, F., Ramroop, S. and Mwambi, H., (2022). A comparative analysis of the volatility nature of cryptocurrency and JSE market. Investig. Manag. Financ. Innov, 19, pp. 23–39. http://dx.doi.org/10.21511/imfi.19(4).2022.03.

Li, L., Xie, X. and Yang, J., (2021). A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia. Medical & Biological Engineering & Computing, 59, pp. 2311–2324. https://doi.org/ 10.1007/s11517-021-02433-8.

Lien, D., Balakrishnan, N., (2005). On regression analysis with data cleaning via trimming, winsorization, and dichotomization. Communications in Statistics– Simulation and Computation, 34(4), pp. 839–849. https://doi.org/10.1080/03610910500307695.

Mandaci, P. E., Cagli, E. C., (2022). Herding intensity and volatility in cryptocurrency markets during the COVID-19, Finance Research Letters, 46, pp. 102382. https://doi.org/10.1016/j.frl.2021.102382.

Marczak, M., Proietti, T., (2016). Outlier detection in structural time series models: The indicator saturation approach. International Journal of Forecasting, 32(1), pp. 180– 202. https://doi.org/10.1016/j.ijforecast.2015.04.005.

Mohamed, S. D., Ismail, M. T. and Ali, M. K. M, (2023). Detecting Structural Breaks in Cryptocurrency Market, Jurnal Ekonomi Malaysia, 57(2), pp. 107–22. Jurnal Ekonomi Malaysia. http://dx.doi.org/10.17576/JEM-2023-5702-08.

Mohamed, S. D., Ismail, M. T. and Ali, M. K. B. M. (2024). Cryptocurrency Returns Over a Decade: Breaks, Trend Breaks and Outliers. Scientific Annals of Economics and Business, 71(1), pp. 1–20.

Moir, R., (1998). A Monte Carlo analysis of the Fisher randomization technique: reviving randomization for experimental economists. Experimental Economics, 1, pp. 87–100. https://doi.org/10.1023/A:1009961917752.

Muhammadullah, S., Urooj, A., Mengal, M. H., Khan, S. A. and Khalaj, F., (2022). Cross-Sectional Analysis of Impulse Indicator Saturation Method for Outlier Detection Estimated via Regularization Techniques with Application of COVID- 19 Data. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/ 2022/2588534.

Mulry, M. H., Oliver, B. E. and Kaputa, S. J., (2014). Detecting and treating verified influential values in a monthly retail trade survey. Journal of Official Statistics, 30(4), pp. 721–747. https://doi.org/10.2478/jos-2014-0045.

Narayan, P. K., Popp, S., (2010). A new unit root test with two structural breaks in level and slope at unknown time. Journal of Applied Statistics, 37(9), pp. 1425–1438. https://doi.org/10.1080/02664760903039883.

Ohara, H.I.,(1999). A unit root test with multiple trend breaks: A theory and an application to US and Japanese macroeconomic time-series. Japanese Economic Review, 50(3), pp. 266–290. https://doi.org/10.1111/1468-5876.00119.

Panday, A., (2015). Impact of monetary policy on exchange market pressure: The case of Nepal. Journal of Asian Economics, 37, pp. 59–71. https://doi.org/10.1016/j.asieco.2015.02.001.

Papell, D. H., Prodan, R., (2003). The Uncertain Unit Root in U.S. Real GDP: Evidence with Restricted and Unrestricted Structural Change. Journal of Money, Credit and Banking, 36(3). 10.1353/mcb.2004.0059.

Pretis, F., Mann, M. L. and Kaufmann, R. K., (2015). Testing Competing Models of the Temperature Hiatus: Assessing the Effects of Conditioning Variables and Temporal Uncertainties through Sample-Wide Break Detection. Climatic Change, 131(4), pp. 705–718. doi: 10.1007/s10584-015-1391-5.

Pretis, F., Schneider, L., Smerdon, J. E. and Hendry, D. F., (2017). Detecting volcanic eruptions in temperature reconstructions by designed break-indicator saturation. Environmental Economics and Sustainability, pp. 7–37. https://doi.org/10.1002/9781119328223.ch2.

Pretis, F., Reade, J. J., Sucarrat, G., (2018). Automated general-to-specific (GETS) regression modeling and indicator saturation for outliers and structural breaks. Journal of Statistical Software, 86 (3), pp. 1–44. 10.18637/jss.v086.i03.

Quandt, R. E., (1960). Tests of the Hypothesis that a Linear Regression System Obeys Two Separate Regimes Tests of the hypothesis that a linear regression system obeys two separate regimes. Journal of the American Statistical Association, 55(290), pp. 324–330, DOI: 10.1080/01621459.1960.10482067.

Rivest, L. P., (1994). Statistical properties of Winsorized means for skewed distributions. Biometrika, 81(2), pp. 373–383. https://doi.org/10.1093/biomet/81.2.373.

Rodrigues, P. M. M., Rubia, A., (2011). The effects of additive outliers and measurement errors when testing for structural breaks in variance. Oxford Bulletin of Economics and Statistics, 73, pp. 449–68.

Sahoo, P. K., (2021). COVID-19 pandemic and cryptocurrency markets: an empirical analysis from a linear and nonlinear causal relationship. Studies in Economics and Finance, 38(2), pp. 454–468. https://doi.org/10.1108/SEF-09-2020-0385.

Sharma, S., Chatterjee, S., (2021). Winsorization for robust Bayesian neural networks. Entropy, 23(11), pp. 1546. https://doi.org/10.3390/e23111546.

Thies, S., Molnár, P., (2018). Bayesian change point analysis of Bitcoin returns. Finance Research Letters, 27, pp. 223–227. https://doi.org/10.1016/j.frl.2018.03.018.

Tukey, J. W., (1962). The future of data analysis. The annals of mathematical statistics, 33(1), pp. 1–67.

Xao, O. G., Yahaya, S. S. S., Abdullah, S., Yusof, Z. M., (2014, July). H-statistic with winsorized modified one-step M-estimator for two independent groups design. AIP Conference Proceedings, Vol. 1605, No. 1, pp. 928–931. American Institute of Physics.

Yen, T. C., Beng, K. Y., Haur, N. K., Huat, N. K., (2022). Structural change analysis of active cryptocurrency market. Asian Academy of Management Journal of Accounting and Finance, 18(2), pp. 63–85. https://doi.org/10.21315/aamjaf2022.18.2.4.

Yuliyani, L., Kurnia, A., Indahwati, I., (2017, March). Winsorization on linear mixed model (Case study: National exam of senior high school in West Java). In AIP Conference Proceedings, Vol. 1827, No. 1. AIP Publishing.

Zivot, E., Andrews, D. W. K., (2002). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics 20(1), pp. 25–44. https://doi.org/10.1198/073500102753410372.