Forecasting the Return and Volatility of Financial Instruments with Fuzzy Inference Systems and ARMA-GARCH Models

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Resumo

A modification of the GARCH model based on fuzzy inference system (FIS) is applied to the problem of returns and volatility forecasting of financial instruments. The proposed modification allows for the skewness and multiple clusters of volatility. It also performs a fuzzy switching between clusters and dynamically adapts their structure. Such an approach gives the opportunity to account for different volatility behavior under diverse conditions. Furthermore, with FIS-type models it is possible to incorporate large number of affecting factors without explicitly adding them to the model. These models also allow for estimation and dynamic adjustments of the affecting degree of these factors. First, for a series of instrument returns an ARMA model is built, then its residuals are used to calibrate the FIS-GARCH model which is further used to forecast volatility. More than 35 samples are examined, all of which are daily quotes of Russian financial market instruments. The stock, bond and money markets were studied. Calculations showed that for some time series, especially for stock market instruments, the fuzzy approach offers a result that exceeds the original autoregressive — conditional heteroskedasticity model. For several time series fuzzy system helps improve the forecast accuracy (it is also supported by statistical criteria). However, in most cases forecast errors of models with fuzziness and without such are comparable. One may state that due to their features fuzzy systems have a potential in forecasting returns and volatility on stock markets.

Sobre autores

V. Sviyazov

Market Risk Department, Sberbank

Email: v.sviyazov.96@gmail.com
Moscow, Russia

Bibliografia

  1. Пегат А. (2013). Нечеткое моделирование и управление. 2-е изд. М.: БИНОМ. Лаборатория знаний. [Пегат А. (2013). Fuzzy modeling and control. 2nd ed. Moscow: BINOM. Laboratorija znanii (in Russian).]
  2. Свиязов В. А. (2023). Существует ли эффект выходного дня: исследование российского фондового рынка с помощью нечетких систем // Экономический журнал Высшей школы экономики. Т. 27. № 3. С. 412–434. doi: 10.17323/1813-8691-2023-27-3-412-434 [Sviyazov V. A. (2023). Is there a weekend effect? Russian stock market research based on fuzzy systems. The HSE Economic Journal, 27, 3, 412–434. doi: 10.17323/1813-8691-2023-27-3-412-434 (in Russian).]
  3. Шведов А. С. (2018). Аппроксимация функций с помощью нейронных сетей и нечетких систем // Проблемы управления. № 1. С. 21–29. ISSN: 1819–3161. [Shvedov A. S. (2018). Functions approximation with neural networks and fuzzy systems. Control Sciences, 1, 21–29. ISSN: 1819–3161 (in Russian).]
  4. Angelov P. (2010). Evolving Takagi–Sugeno fuzzy systems from streaming data (eTS+). Evolving Intelligent Systems: Methodology and Applications, 21–50. doi: 10.1002/9780470569962.CH2
  5. Angelov P., Filev D. (2004). An approach to online identification of Takagi–Sugeno fuzzy models. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34, 1, 484–498. doi: 10.1109/TSMCB.2003.817053
  6. Baczyński M., Jayaram B. (2008). Fuzzy implications. Berlin, Heidelberg: Springer.
  7. Bollerslev T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 3, 307–327. ISSN: 0304-4076. doi: 10.1016/0304-4076 (86)90063-1
  8. Diebold F. X., Mariano R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13, 3, 253–263. ISSN: 15372707. doi: 10.1080/07350015.1995.10524599
  9. Engle R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 4, 987–1007.
  10. Engle R. F., Ng V. K. (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48, 5, 1749–1778. ISSN: 00221082, 15406261.
  11. Hung J. C. (2009). A fuzzy asymmetric GARCH model applied to stock markets. Information Sciences, 179, 22, 3930– 3943. ISSN: 0020-0255. doi: 10.1016/J.INS.2009.07.009
  12. Jagannathan R., Glosten L., Runkle D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48, 1779–1801. doi: 10.1111/j.1540-6261.1993.tb05128.x
  13. Kostenetskiy P., Chulkevich R., Kozyrev V. (2021). HPC resources of the Higher School of Economics. Journal of Physics: Conference Series, 1740, 1, 012050. doi: 10.1088/1742-6596/1740/1/012050
  14. Luna I., Ballini R. (2012). Adaptive fuzzy system to forecast financial time series volatility. Journal of Intelligent and Fuzzy Systems, 23, 27–38. doi: 10.3233/IFS-2012–0491
  15. Maciel L., Gomide F., Ballini R. (2013). Enhanced evolving participatory learning fuzzy modeling: An application for asset returns volatility forecasting. Evolving Systems, 5, 1–14. doi: 10.1007/s12530-013-9099-0
  16. Sentana E. (1995). Quadratic ARCH Models. The Review of Economic Studies, 62, 4, 639–661. ISSN: 00346527, 1467937X.
  17. Sugeno M., Kang G. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28, 1, 15–33. ISSN: 0165-0114. doi: 10.1016/0165-0114 (88)90113-3
  18. Takagi T., Sugeno M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15, 1, 116–132. doi: 10.1109/TSMC.1985.6313399
  19. Tan L., Wang S., Wang K. (2017). A new adaptive network-based fuzzy inference system with adaptive adjustment rules for stock market volatility forecasting. Information Processing Letters, 127, 32–36. doi: 10.1016/j.ipl.2017.06.012
  20. Thavaneswaran A., Liang Y., Zhu Z., Thulasiram Ruppa K. (2020). Novel data-driven fuzzy algorithmic volatility forecasting models with applications to algorithmic trading. 2020 IEEE International Conference on Fuzzy Systems (FUZZ–IEEE), 1–8. doi: 10.1109/FUZZ48607.2020.9177735
  21. Troiano L., Mejuto E., Kriplani P. (2017). An alternative estimation of market volatility based on fuzzy transform. IFSA-SCIS2017 — Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, 1–6. doi: 10.1109/IFSA-SCIS.2017.8023316
  22. Tsay R. S. (2010). Analysis of Financial Time Series. 3rd ed. Hoboken (New Jersey): John Wiley & Sons.
  23. Zadeh L. (1965). Fuzzy sets. Information and Control, 8, 3, 338–353. ISSN: 0019-9958. doi: 10.1016/S0019-9958 (65)90241-X
  24. Zakoian J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18, 5, 931–955. ISSN: 0165-1889. doi: 10.1016/0165-1889 (94)90039-6

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