Forecasting the Dynamic Correlation of Stock Indices Based on Deep Learning Method

[1]  Geert Bekaert,et al.  International Stock Return Comovements , 2005 .

[2]  Tansel Alp,et al.  Joint forecasts of Dow Jones stocks under general multivariate loss function , 2010, Comput. Stat. Data Anal..

[3]  Resul Aydemir,et al.  Volatility transmission among Latin American stock markets under structural breaks , 2016 .

[4]  Apostolos Serletis,et al.  Energy markets volatility modelling using GARCH , 2014 .

[5]  S. Bekiros Nonlinear causality testing with stepwise multivariate filtering: Evidence from stock and currency markets , 2014 .

[6]  Evrim Turgutlu,et al.  Is global diversification rational? Evidence from emerging equity markets through mixed copula approach , 2010 .

[7]  Ahmet Murat Ozbayoglu,et al.  Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 , 2019, Appl. Soft Comput..

[8]  Mariusz Kleć,et al.  Unsupervised Feature Pre-training of the Scattering Wavelet Transform for Musical Genre Recognition , 2014 .

[9]  Olalekan Aladesanmi,et al.  Stock market integration between the UK and the US: Evidence over eight decades , 2019, Global Finance Journal.

[10]  K. Wang Did Vietnam stock market avoid the “contagion risk” from China and the U.S.? The contagion effect test with dynamic correlation coefficients , 2013 .

[11]  Nadir Öcal,et al.  The effects of domestic and international news and volatility on integration of Chinese stock markets with international stock markets , 2016 .

[12]  Xin Zhang,et al.  Multifractal detrended cross-correlations between Chinese stock market and three stock markets in The Belt and Road Initiative , 2018, Physica A: Statistical Mechanics and its Applications.

[13]  R. Daigler,et al.  Is international diversification really beneficial , 2010 .

[14]  Liu Wen,et al.  A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction , 2020, Physica A: Statistical Mechanics and its Applications.

[15]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[16]  K. Deng Another Look at Large-Cap Stock Return Comovement: A Semi-Markov-Switching Approach , 2018 .

[17]  Nicolas Huck,et al.  Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 , 2017, Eur. J. Oper. Res..

[18]  Thomas Fischer,et al.  Deep learning with long short-term memory networks for financial market predictions , 2017, Eur. J. Oper. Res..

[19]  Chulwoo Han,et al.  Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies , 2017, Expert Syst. Appl..

[20]  Hsiang-Tai Lee Regime switching correlation hedging , 2010 .

[21]  Short-term and long-term Interconnectedness of stock returns in Western Europe and the global market , 2017 .

[22]  Wei Huang Financial Integration and the Price of World Covariance Risk: Large vs. Small-Cap Stocks , 2007 .

[23]  Jón Dańıelsson Blame the models , 2008 .

[24]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[25]  Ha Young Kim,et al.  ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module , 2018, Expert Syst. Appl..

[26]  Yanhui Chen,et al.  Forecasting Crude Oil Prices: a Deep Learning based Model , 2017, ITQM.

[27]  R. Engle Dynamic Conditional Correlation , 2002 .

[28]  Bart De Schutter,et al.  Forecasting spot electricity prices Deep learning approaches and empirical comparison of traditional algorithms , 2018 .

[29]  Perry Sadorsky,et al.  Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH ☆ , 2015 .

[30]  Efraim Turban,et al.  Decision Support and Business Intelligence Systems (8th Edition) , 2006 .

[31]  Leonardo Iania,et al.  Stock-bond return correlations: Moving away from “one-frequency-fits-all” by extending the DCC-MIDAS approach , 2020 .

[32]  Lumengo Bonga‐Bonga Uncovering equity market contagion among BRICS countries: An application of the multivariate GARCH model , 2017 .

[33]  Sibel Celik,et al.  The more contagion effect on emerging markets: The evidence of DCC-GARCH model , 2012 .

[34]  T. Chiang,et al.  Comovements between Chinese and global stock markets: evidence from aggregate and sectoral data , 2016 .

[35]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.

[36]  Ha Young Kim,et al.  Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models , 2018, Expert Syst. Appl..

[37]  Jianping Li,et al.  A deep learning ensemble approach for crude oil price forecasting , 2017 .

[38]  Yongheng Deng,et al.  Correlation and Volatility Dynamics in REIT Returns: Performance and Portfolio Considerations , 2010, The Journal of Portfolio Management.

[39]  Y. Shiferaw Time-varying correlation between agricultural commodity and energy price dynamics with Bayesian multivariate DCC-GARCH models , 2019, Physica A: Statistical Mechanics and its Applications.