Deep Learning Stock Volatility with Google Domestic Trends
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- repec:bla:jfinan:v:59:y:2004:i:2:p:755-793 is not listed on IDEAS
- Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
- Hamid, Shaikh A. & Iqbal, Zahid, 2004. "Using neural networks for forecasting volatility of S&P 500 Index futures prices," Journal of Business Research, Elsevier, vol. 57(10), pages 1116-1125, October.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Andrea Bucci, 2020.
"Realized Volatility Forecasting with Neural Networks,"
Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
- Andrea Bucci, 0. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
- Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.
- Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
- Yuping Song & Xiaolong Tang & Hemin Wang & Zhiren Ma, 2023. "Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH‐MIDAS and deep learning models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 51-59, January.
- Andrea Bucci, 2020.
"Cholesky–ANN models for predicting multivariate realized volatility,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 865-876, September.
- Bucci, Andrea, 2019. "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper 95137, University Library of Munich, Germany.
- Milan Cibuľa & Michal Tkáč, 2023. "Porovnanie algoritmov strojového učenia pre tvorbu predikčného modelu ceny bitcoinu [Comparison of Machine Learning Algorithms for Creation of a Bitcoin Price Prediction Model]," Politická ekonomie, Prague University of Economics and Business, vol. 2023(5), pages 496-517.
- Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.
- Milan Cibuľa & Michal Tkáč, . "Porovnanie algoritmov strojového učenia pre tvorbu predikčného modelu ceny bitcoinu [Comparison of Machine Learning Algorithms for Creation of a Bitcoin Price Prediction Model]," Politická ekonomie, Prague University of Economics and Business, vol. 0.
- Shujian Liao & Jian Chen & Hao Ni, 2021. "Forex Trading Volatility Prediction using Neural Network Models," Papers 2112.01166, arXiv.org, revised Dec 2021.
- Zhengyong Jiang & Jeyan Thiayagalingam & Jionglong Su & Jinjun Liang, 2023. "CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy," Papers 2310.01319, arXiv.org.
- Yuping Song & Bolin Lei & Xiaolong Tang & Chen Li, 2024. "Volatility forecasting for stock market index based on complex network and hybrid deep learning model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 544-566, April.
- Nikita Medvedev & Zhiguang Wang, 2022. "Multistep forecast of the implied volatility surface using deep learning," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 645-667, April.
- Theodoros Zafeiriou & Dimitris Kalles, 2024. "Comparative analysis of neural network architectures for short-term FOREX forecasting," Papers 2405.08045, arXiv.org.
- Manuel Nunes & Enrico Gerding & Frank McGroarty & Mahesan Niranjan, 2020. "Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box," Papers 2005.02217, arXiv.org.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Seiler, Volker, 2024.
"The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain,"
The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 160-179.
- Volker Seiler, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," Post-Print hal-04549980, HAL.
- Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
- Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
- Guillermo Llorente & Jiang Wang, 2020. "Trading and information in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(8), pages 1231-1263, August.
- Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012.
"Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range,"
International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
- Chen, C.W.S. & Gerlach, R. & Hwang, B.B.K. & McAleer, M.J., 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range," Econometric Institute Research Papers EI 2011-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," KIER Working Papers 775, Kyoto University, Institute of Economic Research.
- Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," Documentos de Trabajo del ICAE 2011-16, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
- Cathy W. S. Chen & Richard Gerlach & Bruce B. K. Hwang & Michael McAleer, 2011. "Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range," Working Papers in Economics 11/22, University of Canterbury, Department of Economics and Finance.
- Jonathan Donier & Jean-Philippe Bouchaud, 2015. "Why Do Markets Crash? Bitcoin Data Offers Unprecedented Insights," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-11, October.
- Gustavo Peralta, 2016. "The Nature of Volatility Spillovers across the International Capital Markets," CNMV Working Papers CNMV Working Papers no. 6, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
- 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.
- Eser Arisoy & Aslihan Altay-Salih & Levent Akdeniz, 2015. "Aggregate Volatility Expectations and Threshold CAPM," Post-Print hal-01634175, HAL.
- Costola, Michele & Lorusso, Marco, 2022.
"Spillovers among energy commodities and the Russian stock market,"
Journal of Commodity Markets, Elsevier, vol. 28(C).
- Costola, Michele & Lorusso, Marco, 2021. "Spillovers among Energy Commodities and the Russian Stock Market," MPRA Paper 108990, University Library of Munich, Germany.
- Claudiu Vinte & Marcel Ausloos, 2022. "The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator," Papers 2205.00104, arXiv.org.
- Igor Kliakhandler, 2007. "Execution edge of pit traders and intraday price ranges of soft commodities," Applied Financial Economics, Taylor & Francis Journals, vol. 17(5), pages 343-350.
- Lovcha, Yuliya & Perez-Laborda, Alejandro, 2020. "Dynamic frequency connectedness between oil and natural gas volatilities," Economic Modelling, Elsevier, vol. 84(C), pages 181-189.
- Lafuente, Juan A. & Novales, Alfonso, 2003.
"Optimal hedging under departures from the cost-of-carry valuation: Evidence from the Spanish stock index futures market,"
Journal of Banking & Finance, Elsevier, vol. 27(6), pages 1053-1078, June.
- Lafuente Luengo, Juan Ángel, 2000. "Optimal hedging under departures from the cost of carry valuation: evidence from the spanish stock index futures market," DEE - Working Papers. Business Economics. WB 9853, Universidad Carlos III de Madrid. Departamento de EconomÃa de la Empresa.
- Alfonso Novales & J.A. Lafuente, 2002. "Optimal hedging under departures from the cost-of-carry valuation: evidence from the Spanish stock index futures market," Documentos de Trabajo del ICAE 0223, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
- Sapkota, Niranjan, 2022. "News-based sentiment and bitcoin volatility," International Review of Financial Analysis, Elsevier, vol. 82(C).
- Olivier Ledoit & Michael Wolf, 2022. "Markowitz portfolios under transaction costs," ECON - Working Papers 420, Department of Economics - University of Zurich, revised Sep 2024.
- Kenneth Yung & Yen-Chih Liu, 2009. "Implications of futures trading volume: Hedgers versus speculators," Journal of Asset Management, Palgrave Macmillan, vol. 10(5), pages 318-337, December.
- Aslanidis, Nektarios & Bariviera, Aurelio F. & Perez-Laborda, Alejandro, 2021.
"Are cryptocurrencies becoming more interconnected?,"
Economics Letters, Elsevier, vol. 199(C).
- Aslanidis, Nektarios & Fernández Bariviera, Aurelio & Pérez Laborda, Àlex, 2020. "Are cryptocurrencies becoming more interconnected?," Working Papers 2072/417679, Universitat Rovira i Virgili, Department of Economics.
- Nektarios Aslanidis & Aurelio F. Bariviera & Alejandro Perez-Laborda, 2020. "Are cryptocurrencies becoming more interconnected?," Papers 2009.14561, arXiv.org.
- Baur, Dirk G. & Smales, Lee A., 2020. "Hedging geopolitical risk with precious metals," Journal of Banking & Finance, Elsevier, vol. 117(C).
- Alexandre Aidov & Olesya Lobanova, 2021. "Volatility and Depth in Commodity and FX Futures Markets," JRFM, MDPI, vol. 14(11), pages 1-16, November.
- Francis X. Diebold & Kamil Yilmaz, 2016. "Trans-Atlantic Equity Volatility Connectedness: U.S. and European Financial Institutions, 2004–2014," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 81-127.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-CMP-2015-12-20 (Computational Economics)
- NEP-MAC-2015-12-20 (Macroeconomics)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1512.04916. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.