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A volatility model based on adaptive expectations: An improvement on the rational expectations model

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  • Yao, Yuan
  • Zhao, Yang
  • Li, Yan

Abstract

Investment expectations affect stock price volatility, making asset pricing more difficult. Correctly capturing investment expectations can help alleviate this problem. In this paper, we analyze the rational expectations properties of existing volatility models. Second, we explore a volatility model based on adaptive expectations by using mathematical methods and the applicable conditions and continuity feature of the adaptive expectations volatility model. Third, under the assumption of adaptive expectations, we construct adaptive expectations GARCH (ADGARCH) and LSTM-ADGARCH models. Using daily trading data from the Shanghai stock index and SPX500 for the period 2015–2021, we find that the volatility model based on adaptive expectations has more explanatory power than one based on rational expectations.

Suggested Citation

  • Yao, Yuan & Zhao, Yang & Li, Yan, 2022. "A volatility model based on adaptive expectations: An improvement on the rational expectations model," International Review of Financial Analysis, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:finana:v:82:y:2022:i:c:s1057521922001636
    DOI: 10.1016/j.irfa.2022.102202
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    as
    1. Dahl, Christian M. & Iglesias, Emma M., 2009. "Volatility spill-overs in commodity spot prices: New empirical results," Economic Modelling, Elsevier, vol. 26(3), pages 601-607, May.
    2. Shih-Feng Huang & Hsin-Han Chiang & Yu-Jun Lin, 2021. "A network autoregressive model with GARCH effects and its applications," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-18, July.
    3. Giovanni Cespa & Xavier Vives, 2015. "The Beauty Contest and Short-Term Trading," Journal of Finance, American Finance Association, vol. 70(5), pages 2099-2154, October.
    4. Melino, Angelo & Turnbull, Stuart M., 1990. "Pricing foreign currency options with stochastic volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 239-265.
    5. Markku Lanne & Jani Luoto, 2017. "A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 969-995, August.
    6. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    7. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Beckmann, Joscha & Czudaj, Robert, 2017. "Exchange rate expectations and economic policy uncertainty," European Journal of Political Economy, Elsevier, vol. 47(C), pages 148-162.
    10. Pedersen, Rasmus Søndergaard & Rahbek, Anders, 2019. "Testing Garch-X Type Models," Econometric Theory, Cambridge University Press, vol. 35(5), pages 1012-1047, October.
    11. Goodell, John W. & Vähämaa, Sami, 2013. "US presidential elections and implied volatility: The role of political uncertainty," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1108-1117.
    12. Li, Dong & Zhang, Xingfa & Zhu, Ke & Ling, Shiqing, 2018. "The ZD-GARCH model: A new way to study heteroscedasticity," Journal of Econometrics, Elsevier, vol. 202(1), pages 1-17.
    13. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.
    14. Hamadi, Hassan & Bassil, Charbel & Nehme, Tamara, 2017. "News surprises and volatility spillover among agricultural commodities: The case of corn, wheat, soybean and soybean oil," Research in International Business and Finance, Elsevier, vol. 41(C), pages 148-157.
    15. Jiang, Cuixia & Ding, Xiaoyi & Xu, Qifa & Tong, Yongbo, 2020. "A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    16. Xu, Yingying & Chang, Hsu-Ling & Lobonţ, Oana-Ramona & Su, Chi-Wei, 2016. "Modeling heterogeneous inflation expectations: empirical evidence from demographic data?," Economic Modelling, Elsevier, vol. 57(C), pages 153-163.
    17. Wan†Chien Chiu & Juan Ignacio Peña & Chih†Wei Wang, 2015. "Measuring Systemic Risk: Common Factor Exposures and Tail Dependence Effects," European Financial Management, European Financial Management Association, vol. 21(5), pages 833-866, November.
    18. Ahmed El Ghini & Youssef Saidi, 2017. "Return and volatility spillovers in the Moroccan stock market during the financial crisis," Empirical Economics, Springer, vol. 52(4), pages 1481-1504, June.
    19. Arısoy, Yakup Eser & Altay-Salih, Aslıhan & Akdeniz, Levent, 2015. "Aggregate volatility expectations and threshold CAPM," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 231-253.
    20. Bali, Turan G. & Engle, Robert F., 2010. "The intertemporal capital asset pricing model with dynamic conditional correlations," Journal of Monetary Economics, Elsevier, vol. 57(4), pages 377-390, May.
    21. Katarzyna Czech & Michał Wielechowski & Pavel Kotyza & Irena Benešová & Adriana Laputková, 2020. "Shaking Stability: COVID-19 Impact on the Visegrad Group Countries’ Financial Markets," Sustainability, MDPI, vol. 12(15), pages 1-19, August.
    22. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    23. Hung, Jui-Cheng, 2015. "Evaluation of realized multi-power variations in minimum variance hedging," Economic Modelling, Elsevier, vol. 51(C), pages 672-679.
    24. Hao Sun & Bo Yu, 2020. "Forecasting Financial Returns Volatility: A GARCH-SVR Model," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 451-471, February.
    25. Zhou, Zhongbao & Fu, Zhangyan & Jiang, Yong & Zeng, Ximei & Lin, Ling, 2020. "Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 34(C).
    26. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    27. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    28. Lu Wang & Feng Ma & Guoshan Liu, 2020. "Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 797-810, August.
    29. N. Alemohammad & S. Rezakhah & S. H. Alizadeh, 2020. "Markov switching asymmetric GARCH model: stability and forecasting," Statistical Papers, Springer, vol. 61(3), pages 1309-1333, June.
    30. Gomez-Gonzalez, Jose E. & Rojas-Espinosa, Wilmer, 2019. "Detecting contagion in Asian exchange rate markets using asymmetric DCC-GARCH and R-vine copulas," Economic Systems, Elsevier, vol. 43(3).
    31. HaiYue Liu & Yile Wang & Dongmei He & Cangyu Wang, 2020. "Short term response of Chinese stock markets to the outbreak of COVID-19," Applied Economics, Taylor & Francis Journals, vol. 52(53), pages 5859-5872, November.
    32. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    33. Nelson, Daniel B., 1990. "Stationarity and Persistence in the GARCH(1,1) Model," Econometric Theory, Cambridge University Press, vol. 6(3), pages 318-334, September.
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    More about this item

    Keywords

    Rational expectations; Volatility models; GARCH model; LSTM; Stock market;
    All these keywords.

    JEL classification:

    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • E70 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - General

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