Modeling and predicting stock market volatility using neural network and conditional variance patterns
Subject Areas : Financial engineering
ali rastinfar
1
,
mahmood hematfar
2
1 - Department of Financial Management, Electronic Campus, Islamic Azad University, Tehran, Iran.
2 - Department of Financial Management, Boroujerd Branch, Islamic Azad University, Borujerd, Iran.
Keywords: Neural Networks, Stock market volatility, Conditional variance patterns,
Abstract :
abstractModeling and predicting stock market volatility using neural network and conditional variance patternsThe fluctuation forecast is one of the most important issues in the financial markets, which attracted the attention of many academic researchers and experts in the field over the past few decades. In this study, considering this necessity, we examine the modeling and prediction of stock market volatility using the combination of artificial neural networks and conditional variance patterns.In this research, multi-layer perceptron nerve networks (MLP), conditional variance heterogeneity models (ARCH) and self-regression model and conditional variance (GARCH) (P, Q) have been used. The statistical population of the study is the Tehran Stock Exchange index for the period of April 2008 to April 2018 . The research seeks to reject or confirm the hypothesis that "the use of an artificial neural network and conditional variance models increases the accuracy of the forecast of stock market fluctuations in the Tehran Stock Exchange relative to the conditional variance model" . The results, confirm the validity of the above hypothesis.
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نادر مهرگان پرویز محمدزاده و محمود حقانی، ،یونس سلمانی. (1391). بررسی الگوی چند رفتاری رشد اقتصادی در واکنش به نوسانات قیمت نفت خام: کاربردی از مدل های GARCH و رگرسیون چرخشی مارکف. تحقیقات مدل سازی اقتصادی تابستان 1392 شماره 12.
Balduzzi, P., & Reuter, J. (2012). Heterogeneity in Target-Date Funds: Optimal Risk-Taking or Risk Matching? (No. w17886). National Bureau of Economic Research.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Dash, R., & Dash, P. (2016). Efficient stock price prediction using a self evolving recurrent neuro-fuzzy inference system optimized through a modified differential harmony search technique. Expert Systems with Applications, 52, 75-90.
Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
Miao, K., Chen, F., & Zhao, Z. G. (2007). Stock price forecast based on bacterial colony RBF neural network [j]. Journal of Qingdao University (Natural Science Edition), 2(011).
همه
Hatami Nima, Mirzazadeh Hojat, Ebrahimpour Reza. (1389). "Combination of neural networks for forecasting stock prices", Macroeconomic Research Journal, 10.1(39), pp. 61-80.
Nader Mehrgan Parviz Mohammadzadeh and Mahmoud Haqqani, Younes Salmani. (2011). Investigating the multi-behavioral model of economic growth in response to crude oil price fluctuations: application of GARCH and Markov cyclic regression models. Economic modeling research, summer 2012, number 12.
Balduzzi, P., & Reuter, J. (2012). Heterogeneity in Target-Date Funds: Optimal Risk-Taking or Risk Matching? (No. w17886). National Bureau of Economic Research.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Dash, R., & Dash, P. (2016). Efficient stock price prediction using a self-evolving recurrent neuro-fuzzy inference system optimized through a modified differential harmony search technique. Expert Systems with Applications, 52, 75-90.
Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417.
Miao, K., Chen, F., & Zhao, Z. G. (2007). Stock price forecast based on bacterial colony RBF neural network [j]. Journal of Qingdao University (Natural Science Edition), 2(011).
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Balduzzi, P., & Reuter, J. (2012). Heterogeneity in Target-Date Funds: Optimal Risk-Taking or Risk Matching? (No. w17886). National Bureau of Economic Research.
Balduzzi, P., & Reuter, J. (2012). Heterogeneity in Target-Date Funds: Optimal Risk-Taking or Risk Matching? (No. w17886). National Bureau of Economic Research.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Dash, R., & Dash, P. (2016). Efficient stock price prediction using a self-evolving recurrent neuro-fuzzy inference system optimized through a modified differential harmony search technique. Expert Systems with Applications, 52, 75-90.
Dash, R., & Dash, P. (2016). Efficient stock price prediction using a self-evolving recurrent neuro-fuzzy inference system optimized through a modified differential harmony search technique. Expert Systems with Applications, 52, 75-90.
Hatami Nima, Mirzazadeh Hojat, Ebrahimpour Reza. (1389). "Combination of neural networks for forecasting stock prices", Macroeconomic Research Journal, 10.1(39), pp. 61-80.
Hatami Nima, Mirzazadeh Hojat, Ebrahimpour Reza. (1389). "Combination of neural networks for forecasting stock prices", Macroeconomic Research Journal, 10.1(39), pp. 61-80.
Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417.
Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417.
Miao, K., Chen, F., & Zhao, Z. G. (2007). Stock price forecast based on bacterial colony RBF neural network [j]. Journal of Qingdao University (Natural Science Edition), 2(011).
Miao, K., Chen, F., & Zhao, Z. G. (2007). Stock price forecast based on bacterial colony RBF neural network [j]. Journal of Qingdao University (Natural Science Edition), 2(011).
Nader Mehrgan Parviz Mohammadzadeh and Mahmoud Haqqani, Younes Salmani. (2011). Investigating the multi-behavioral model of economic growth in response to crude oil price fluctuations: application of GARCH and Markov cyclic regression models. Economic modeling research, summer 2012, number 12.
Nader Mehrgan Parviz Mohammadzadeh and Mahmoud Haqqani, Younes Salmani. (2011). Investigating the multi-behavioral model of economic growth in response to crude oil price fluctuations: application of GARCH and Markov cyclic regression models. Economic modeling research, summer 2012, number 12.
