Forecasting Realized Volatility of Bitcoin: The Role of the Trade War
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DOI: 10.1007/s10614-020-10022-4
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- Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Working Papers 202003, University of Pretoria, Department of Economics.
References listed on IDEAS
- Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
- Wu, Shan & Tong, Mu & Yang, Zhongyi & Derbali, Abdelkader, 2019. "Does gold or Bitcoin hedge economic policy uncertainty?," Finance Research Letters, Elsevier, vol. 31(C), pages 171-178.
- Fang, Libing & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2019.
"Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin?,"
International Review of Financial Analysis, Elsevier, vol. 61(C), pages 29-36.
- Libing Fang & Elie Bouri & Rangan Gupta & David Roubaud, 2018. "Does Global Economic Uncertainty Matter for the Volatility and Hedging Effectiveness of Bitcoin?," Working Papers 201858, University of Pretoria, Department of Economics.
- Baur, Dirk G. & Dimpfl, Thomas, 2016. "Googling gold and mining bad news," Resources Policy, Elsevier, vol. 50(C), pages 306-311.
- Gupta, Rangan & Pierdzioch, Christian & Vivian, Andrew J. & Wohar, Mark E., 2019.
"The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests,"
Finance Research Letters, Elsevier, vol. 29(C), pages 315-322.
- Rangan Gupta & Christian Pierdzioch & Andrew J. Vivian & Mark E. Wohar, 2018. "The Predictive Value of Inequality Measures for Stock Returns: An Analysis of Long-Span UK Data Using Quantile Random Forests," Working Papers 201809, University of Pretoria, Department of Economics.
- Caldara, Dario & Iacoviello, Matteo & Molligo, Patrick & Prestipino, Andrea & Raffo, Andrea, 2020.
"The economic effects of trade policy uncertainty,"
Journal of Monetary Economics, Elsevier, vol. 109(C), pages 38-59.
- Dario Caldara & Matteo Iacoviello & Patrick Molligo & Andrea Prestipino & Andrea Raffo, 2019. "The Economic Effects of Trade Policy Uncertainty," International Finance Discussion Papers 1256, Board of Governors of the Federal Reserve System (U.S.).
- Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2022.
"Risk aversion and the predictability of crude oil market volatility: A forecasting experiment with random forests,"
Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(8), pages 1755-1767, August.
- Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Risk Aversion and the Predictability of Crude Oil Market Volatility: A Forecasting Experiment with Random Forests," Working Papers 201972, University of Pretoria, Department of Economics.
- Ole E. Barndorff-Nielsen & Neil Shephard, 2006.
"Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation,"
Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 1-30.
- Neil Shephard & Ole Barndorff-Nielsen, 2003. "Econometrics of testing for jumps in financial economics using bipower variation," Economics Series Working Papers 2004-FE-01, University of Oxford, Department of Economics.
- Ole E. Barndorff-Nielsen & Neil Shephard, 2004. "Econometrics of testing for jumps in financial economics using bipower variationÂ," OFRC Working Papers Series 2004fe01, Oxford Financial Research Centre.
- Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Econometrics of testing for jumps in financial economics using bipower variation," Economics Papers 2003-W21, Economics Group, Nuffield College, University of Oxford.
- Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2018.
"On the determinants of bitcoin returns: A LASSO approach,"
Finance Research Letters, Elsevier, vol. 27(C), pages 235-240.
- Theodore Panagiotidis & Thanasis Stengos & Orestis Vravosinos, 2018. "On the determinants of bitcoin returns: a LASSO approach," Working Paper series 18-14, Rimini Centre for Economic Analysis.
- Tetsuya Takaishi, 2017. "Statistical properties and multifractality of Bitcoin," Papers 1707.07618, arXiv.org, revised May 2018.
- Thomas Walther & Tony Klein, 2018. "Exogenous Drivers of Cryptocurrency Volatility - A Mixed Data Sampling Approach To Forecasting," Working Papers on Finance 1815, University of St. Gallen, School of Finance.
- Walther, Thomas & Klein, Tony & Bouri, Elie, 2019.
"Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting,"
Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
- Walther, Thomas & Klein, Tony & Bouri, Elie, 2018. "Exogenous Drivers of Bitcoin and Cryptocurrency Volatility – A Mixed Data Sampling Approach to Forecasting," QBS Working Paper Series 2018/02, Queen's University Belfast, Queen's Business School.
- Hyndman, Rob J. & Khandakar, Yeasmin, 2008.
"Automatic Time Series Forecasting: The forecast Package for R,"
Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
- Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
- Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007.
"Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility,"
The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2005. "Roughing it Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," NBER Working Papers 11775, National Bureau of Economic Research, Inc.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," CREATES Research Papers 2007-18, Department of Economics and Business Economics, Aarhus University.
- Bollerslev, Tim & Ghysels, Eric, 1996.
"Periodic Autoregressive Conditional Heteroscedasticity,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-151, April.
- Bollerslev, T. & Ghysels, E., 1994. "Periodic Autoregressive Conditional Heteroskedasticity," Cahiers de recherche 9408, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Bollerslev, T. & Ghysels, E., 1994. "Periodic Autoregressive Conditional Heteroskedasticity," Cahiers de recherche 9408, Universite de Montreal, Departement de sciences economiques.
- Michael McAleer & Marcelo Medeiros, 2008.
"Realized Volatility: A Review,"
Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
- Michael McAleer & Marcelo Cunha Medeiros, 2006. "Realized volatility: a review," Textos para discussão 531 Publication status: F, Department of Economics PUC-Rio (Brazil).
- Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012.
"Jump-robust volatility estimation using nearest neighbor truncation,"
Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
- Torben G. Andersen & Dobrislav Dobrev & Ernst Schaumburg, 2009. "Jump-Robust Volatility Estimation using Nearest Neighbor Truncation," CREATES Research Papers 2009-52, Department of Economics and Business Economics, Aarhus University.
- Torben G. Andersen & Dobrislav Dobrev & Ernst Schaumburg, 2010. "Jump-robust volatility estimation using nearest neighbor truncation," Staff Reports 465, Federal Reserve Bank of New York.
- Torben G. Andersen & Dobrislav Dobrev & Ernst Schaumburg, 2009. "Jump-Robust Volatility Estimation using Nearest Neighbor Truncation," NBER Working Papers 15533, National Bureau of Economic Research, Inc.
- Bouri, Elie & Gupta, Rangan & Tiwari, Aviral Kumar & Roubaud, David, 2017.
"Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions,"
Finance Research Letters, Elsevier, vol. 23(C), pages 87-95.
- Elie Bouri & Rangan Gupta & Aviral Kumar Tiwari & David Roubaud, 2016. "Does Bitcoin Hedge Global Uncertainty? Evidence from Wavelet-Based Quantile-in-Quantile Regressions," Working Papers 201690, University of Pretoria, Department of Economics.
- Elie Bouri & Rangan Gupta & Aviral Kumar Tiwari & David Roubaud, 2017. "Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions," Post-Print hal-02008552, HAL.
- Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020.
"Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss,"
Journal of International Money and Finance, Elsevier, vol. 104(C).
- Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
- Bouri, Elie & Gupta, Rangan, 2021.
"Predicting Bitcoin returns: Comparing the roles of newspaper- and internet search-based measures of uncertainty,"
Finance Research Letters, Elsevier, vol. 38(C).
- Elie Bouri & Rangan Gupta, 2019. "Predicting Bitcoin Returns: Comparing the Roles of Newspaper- and Internet Search-Based Measures of Uncertainty," Working Papers 201955, University of Pretoria, Department of Economics.
- Ardia, David & Bluteau, Keven & Rüede, Maxime, 2019. "Regime changes in Bitcoin GARCH volatility dynamics," Finance Research Letters, Elsevier, vol. 29(C), pages 266-271.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Arouri, Mohamed El Hédi & Lahiani, Amine & Lévy, Aldo & Nguyen, Duc Khuong, 2012.
"Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models,"
Energy Economics, Elsevier, vol. 34(1), pages 283-293.
- Mohamed El Hedi Arouri & Amine Lahiani & Khuong Nguyen Duc, 2010. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Working Papers 13, Development and Policies Research Center (DEPOCEN), Vietnam.
- Mohamed AROURI & Amine LAHIANI & D.-K. NGUYEN, 2010. "Forecasting the Conditional Volatility of Oil Spot andFutures Prices with Structural Breaksand Long Memory Models," LEO Working Papers / DR LEO 661, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
- Mohamed El Hedi Arouri & Duc Khuong Nguyen & Amine Lahiani, 2010. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Working Papers hal-00507831, HAL.
- Aldo Levy & M.H. Arouri & Amine Lahiani & Duc Khuong Nguyen, 2012. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Post-Print halshs-01279906, HAL.
- Elie Bouri & Konstantinos Gkillas & Rangan Gupta, 2020.
"Trade uncertainties and the hedging abilities of Bitcoin,"
Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 49(3), September.
- Elie Bouri & Konstantinos Gkillas & Rangan Gupta, 2019. "Trade Uncertainties and the Hedging Abilities of Bitcoin," Working Papers 201948, University of Pretoria, Department of Economics.
- Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015.
"Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes,"
Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
- Kevin Sheppard & Lily Liu & Andrew J. Patton, 2013. "Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes," Economics Series Working Papers 645, University of Oxford, Department of Economics.
- Amaya, Diego & Christoffersen, Peter & Jacobs, Kris & Vasquez, Aurelio, 2015.
"Does realized skewness predict the cross-section of equity returns?,"
Journal of Financial Economics, Elsevier, vol. 118(1), pages 135-167.
- Diego Amaya & Peter Christoffersen & Kris Jacobs & Aurelio Vasquez, 2013. "Does Realized Skewness Predict the Cross-Section of Equity Returns?," CREATES Research Papers 2013-41, Department of Economics and Business Economics, Aarhus University.
- Xin Huang & George Tauchen, 2005. "The Relative Contribution of Jumps to Total Price Variance," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 456-499.
- Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
- Elie Bouri & Luis A. Gil‐Alana & Rangan Gupta & David Roubaud, 2019.
"Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks,"
International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 412-426, January.
- Elie Bouri & Luis A. Gil-Alana & Rangan Gupta & David Roubaud, 2016. "Modelling Long Memory Volatility in the Bitcoin Market: Evidence of Persistence and Structural Breaks," Working Papers 201654, University of Pretoria, Department of Economics.
- Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
- Aysan, Ahmet Faruk & Demir, Ender & Gozgor, Giray & Lau, Chi Keung Marco, 2019. "Effects of the geopolitical risks on Bitcoin returns and volatility," Research in International Business and Finance, Elsevier, vol. 47(C), pages 511-518.
- Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- Clark, Todd E. & West, Kenneth D., 2007.
"Approximately normal tests for equal predictive accuracy in nested models,"
Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
- Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
- Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
- Chaim, Pedro & Laurini, Márcio P., 2018. "Volatility and return jumps in bitcoin," Economics Letters, Elsevier, vol. 173(C), pages 158-163.
- Diep Duong & Norman R. Swanson, 2011. "Volatility in Discrete and Continuous Time Models: A Survey with New Evidence on Large and Small Jumps," Departmental Working Papers 201117, Rutgers University, Department of Economics.
- Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
- Christian Conrad & Anessa Custovic & Eric Ghysels, 2018. "Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis," JRFM, MDPI, vol. 11(2), pages 1-12, May.
- Baur, Dirk G. & Dimpfl, Thomas, 2018. "Asymmetric volatility in cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 148-151.
- Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021.
"Forecasting realized volatility of bitcoin returns: tail events and asymmetric loss,"
The European Journal of Finance, Taylor & Francis Journals, vol. 27(16), pages 1626-1644, November.
- Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Volatility of Bitcoin Returns: Tail Events and Asymmetric Loss," Working Papers 201905, University of Pretoria, Department of Economics.
- Muhammad Ali Nasir & Toan Luu Duc Huynh & Sang Phu Nguyen & Duy Duong, 2019. "Forecasting cryptocurrency returns and volume using search engines," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-13, December.
- Bouri, Elie & Gupta, Rangan & Lau, Chi Keung Marco & Roubaud, David & Wang, Shixuan, 2018.
"Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles,"
The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 297-307.
- Elie Bouri & Rangan Gupta & Chi Keung Marco Lau & David Roubaud & Shixuan Wang, 2017. "Bitcoin and Global Financial Stress: A Copula-Based Approach to Dependence and Causality-in-Quantiles," Working Papers 201750, University of Pretoria, Department of Economics.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003.
"Modeling and Forecasting Realized Volatility,"
Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," Center for Financial Institutions Working Papers 01-01, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Anderson, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Labys, Paul, 2002. "Modeling and Forecasting Realized Volatility," Working Papers 02-12, Duke University, Department of Economics.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," NBER Working Papers 8160, National Bureau of Economic Research, Inc.
- Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
- Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019.
"The effects of markets, uncertainty and search intensity on bitcoin returns,"
International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
- Theodore Panagiotidis & Thanasis Stengos & Orestis Vravosinos, 2018. "The effects of markets, uncertainty and search intensity on bitcoin returns," Working Paper series 18-39, Rimini Centre for Economic Analysis.
- Acharya, Viral V. & Lochstoer, Lars A. & Ramadorai, Tarun, 2013.
"Limits to arbitrage and hedging: Evidence from commodity markets,"
Journal of Financial Economics, Elsevier, vol. 109(2), pages 441-465.
- Acharya, Viral & Lochstoer, Lars, 2009. "Limits to Arbitrage and Hedging: Evidence from Commodity Markets," CEPR Discussion Papers 7327, C.E.P.R. Discussion Papers.
- Viral V. Acharya & Lars A. Lochstoer & Tarun Ramadorai, 2011. "Limits to Arbitrage and Hedging: Evidence from Commodity Markets," NBER Working Papers 16875, National Bureau of Economic Research, Inc.
- Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," JRFM, MDPI, vol. 10(4), pages 1-15, October.
- Takaishi, Tetsuya, 2018. "Statistical properties and multifractality of Bitcoin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 507-519.
- Duong, Diep & Swanson, Norman R., 2015.
"Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction,"
Journal of Econometrics, Elsevier, vol. 187(2), pages 606-621.
- Diep Duong & Norman Swanson, 2013. "Empirical Evidence on the Importance of Aggregation, Asymmetry, and Jumps for Volatility Prediction," Departmental Working Papers 201321, Rutgers University, Department of Economics.
- Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
- Gozgor, Giray & Tiwari, Aviral Kumar & Demir, Ender & Akron, Sagi, 2019. "The relationship between Bitcoin returns and trade policy uncertainty," Finance Research Letters, Elsevier, vol. 29(C), pages 75-82.
- Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
- Sowmya Subramaniam & Madhumita Chakraborty, 2020. "Investor Attention and Cryptocurrency Returns: Evidence from Quantile Causality Approach," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(1), pages 103-115, January.
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- Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
- repec:ipg:wpaper:2014-053 is not listed on IDEAS
- Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
- Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019.
"Time-varying risk aversion and realized gold volatility,"
The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
- Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2018. "Time-Varying Risk Aversion and Realized Gold Volatility," Working Papers 201881, University of Pretoria, Department of Economics.
- Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024.
"Business applications and state‐level stock market realized volatility: A forecasting experiment,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 456-472, March.
- Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2022. "Business Applications and State-Level Stock Market Realized Volatility: A Forecasting Experiment," Working Papers 202247, University of Pretoria, Department of Economics.
- Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023.
"The contribution of jump signs and activity to forecasting stock price volatility,"
Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
- , 2019. "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers 1902, Federal Reserve Bank of Dallas, revised 17 Dec 2022.
- Ruijun Bu & Rodrigo Hizmeri & Marwan Izzeldin & Anthony Murphy & Mike G. Tsionas, 2021. "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers 202109, University of Liverpool, Department of Economics.
- Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
- Alizadeh, Amir H. & Huang, Chih-Yueh & Marsh, Ian W., 2021. "Modelling the volatility of TOCOM energy futures: A regime switching realised volatility approach," Energy Economics, Elsevier, vol. 93(C).
- Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017.
"The contribution of jumps to forecasting the density of returns,"
Documents de travail du Centre d'Economie de la Sorbonne
17006, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
- Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01442618, HAL.
- Liu, Yi & Liu, Huifang & Zhang, Lei, 2019. "Modeling and forecasting return jumps using realized variation measures," Economic Modelling, Elsevier, vol. 76(C), pages 63-80.
- Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022.
"Forecasting oil and gold volatilities with sentiment indicators under structural breaks,"
Energy Economics, Elsevier, vol. 105(C).
- Jiawen Luo & Riza Demirer & Rangan Gupta & Qiang Ji, 2021. "Forecasting Oil and Gold Volatilities with Sentiment Indicators Under Structural Breaks," Working Papers 202130, University of Pretoria, Department of Economics.
- Lyócsa, Štefan & Molnár, Peter & Plíhal, Tomáš & Širaňová, Mária, 2020. "Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
- Degiannakis, Stavros & Filis, George, 2022.
"Oil price volatility forecasts: What do investors need to know?,"
Journal of International Money and Finance, Elsevier, vol. 123(C).
- Degiannakis, Stavros & Filis, George, 2019. "Oil price volatility forecasts: What do investors need to know?," MPRA Paper 94445, University Library of Munich, Germany.
- Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02505861, HAL.
- Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Post-Print halshs-02505861, HAL.
- Degiannakis, Stavros & Filis, George, 2017.
"Forecasting oil price realized volatility using information channels from other asset classes,"
Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
- Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," MPRA Paper 96276, University Library of Munich, Germany.
- Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
More about this item
Keywords
Bitcoin; Realized volatility; Trade war; Random forests;All these keywords.
JEL classification:
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
- Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
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