An evolutionary hybrid Fuzzy Computationally Efficient EGARCH model for volatility prediction

Dash, Rajashree; Dash, P.K.

doi:10.1016/j.asoc.2016.04.014

Cited by 30 publications

(28 citation statements)

References 26 publications

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“…al., 2015;Qiu et. al., 2016; Zhong and Enke, 2017; Chen and Hao, 2017).Starting from various financial models for volatility prediction, there also have been studies to combine volatility models such as GARCH model, EGARCH or GJR-GARCH and machine learning algorithms, see (Monfared and Enke, 2014;Dash and Dash, 2016;Peng et. al., 2018;Hurduzeu et.…”

Section: Jel Classification: C53 G17mentioning

confidence: 99%

Performance of Deep Learning in Prediction of Stock Market Volatility

Moon¹,

Kim²

2019

ECECSR

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Volatility forecasting is an important issue for investment analysis and risk management in finance. Based on the Long Short Term Memory (LSTM) deep learning algorithm, we propose an accurate algorithm for forecasting stock market index and its volatility. The proposed algorithm is tested on the data from 5 stock market indices including S&P500, NASDAQ, German DAX, Korean KOSPI200 and Mexico IPC over a 7-yearperiod from 2010 to 2016. The highest prediction performance is observed with hybrid momentum, the difference between the price and the moving average of the past prices, for the predictions of both market index and volatility. Unlike stock index, the prediction accuracy for the volatility does not show dependency on other financial variables such as open, low, high prices, volume, etc. except the volatility itself.

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Section: Jel Classification: C53 G17mentioning

confidence: 99%

Performance of Deep Learning in Prediction of Stock Market Volatility

Moon¹,

Kim²

2019

ECECSR

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“…The output of this Fuzzy-EGARCH-ANN model is the weighted average of each individual rule and is obtained by using FIS fundamental steps as in [11] and [12] as follows:…”

Section: Fuzzy-egarch-ann Modelmentioning

confidence: 99%

Modeling Exchange Rate Volatility Dynamics of the Great Britain Pound to Ethiopian Birr Using the Semi-Parametric Non-Linear Fuzzy-EGARCH-ANN Model

Mohammed¹,

Aduda²,

Kube³

2020

JMF

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In this paper, a robust analysis of volatility forecasting of the GBP-ETB exchange rate was provided using weekly data spanning the period June 30, 2003-January 24, 2020. To our knowledge, this was the first study that focuses on the GBP-ETB exchange rate using high-frequency data and the Fuzzy-EGARCH-ANN econometric model. The research finds that the best performing model in terms of one-step ahead forecasts based on realized volatility computed from the underlying daily data series is the Fuzzy-EGARCH-ANN(1, 2, 2, 1) with students t-distribution.

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“…Modeling and forecasting volatility of the stock market have gained great attention from researchers in academia as well as in financial markets due to its wide range of applications [1]. Investment decisions in financial markets powerfully depend on the forecast of expected returns and volatility of the assets.…”

Section: Introductionmentioning

confidence: 99%

“…Investment decisions in financial markets powerfully depend on the forecast of expected returns and volatility of the assets. Volatility delivers a measure of fluctuation in a financial security price around its expected value [1]. Expected market return is correlated to predictable stock market volatility [1].…”

Section: Introductionmentioning

confidence: 99%

Improving Forecasts of the EGARCH Model Using Artificial Neural Network and Fuzzy Inference System

Mohammed

Aduda

Kube

2020

Journal of Mathematics

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This paper proposes an innovative semiparametric nonlinear fuzzy-EGARCH-ANN model to solve the problem of accurate modeling for forecasting stock market volatility. This model has been developed by a combination of the FIS, ANN, and EGARCH models. Because the proposed model is highly nonlinear and gradient-based parameter estimation methods might not give global optimal parameters for highly nonlinear models, the study has decided to use evolutionary algorithms instead. In particular, a differential evolution (DE) algorithm is suggested to solve the parameter estimation problem of the proposed model. After this, the semiparametric nonlinear fuzzy-EGARCH-ANN model has been developed mathematically from the three models mentioned before, and the study has simulated data by it. After the simulation, parameter estimation of the proposed model using a differential evolution algorithm on the simulated data is done. Finally, it is seen that the proposed model is good in capturing the volatility clustering and leverage effects of highly nonlinear and complicated financial time series data that were overlooked by the EGARCH model.

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An evolutionary hybrid Fuzzy Computationally Efficient EGARCH model for volatility prediction

Cited by 30 publications

References 26 publications

Performance of Deep Learning in Prediction of Stock Market Volatility

Performance of Deep Learning in Prediction of Stock Market Volatility

Modeling Exchange Rate Volatility Dynamics of the Great Britain Pound to Ethiopian Birr Using the Semi-Parametric Non-Linear Fuzzy-EGARCH-ANN Model

Improving Forecasts of the EGARCH Model Using Artificial Neural Network and Fuzzy Inference System

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