IDEAS home Printed from https://ideas.repec.org/e/c/pgr194.html
   My authors  Follow this author

Nikola Gradojevic

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Deniz Erdemlioglu & Nikola Gradojevic, 2020. "Heterogeneous investment horizons, risk regimes, and realized jumps," Post-Print hal-02995997, HAL.

    Cited by:

    1. Tong, Yuan & Wan, Ning & Dai, Xingyu & Bi, Xiaoyi & Wang, Qunwei, 2022. "China's energy stock market jumps: To what extent does the COVID-19 pandemic play a part?," Energy Economics, Elsevier, vol. 109(C).

  2. Nikola Gradojevic & Deniz Erdemlioglu & Ramazan Gençay, 2020. "A new wavelet-based ultra-high-frequency analysis of triangular currency arbitrage," Post-Print hal-02512423, HAL.

    Cited by:

    1. Massimiliano Caporin & Riccardo (Jack) Lucchetti & Giulio Palomba, 2020. "Analytical Gradients of Dynamic Conditional Correlation Models," JRFM, MDPI, vol. 13(3), pages 1-21, March.
    2. Fu, Hsuan & Luger, Richard, 2022. "Multiple testing of the forward rate unbiasedness hypothesis across currencies," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 232-245.

  3. Nikola Gradojevic & Deniz Erdemlioglu & Ramazan Gençay, 2017. "Informativeness of trade size in foreign exchange markets," Post-Print hal-01745281, HAL.

    Cited by:

    1. Cui, Zhenyu & Taylor, Stephen, 2020. "Arbitrage detection using max plus product iteration on foreign exchange rate graphs," Finance Research Letters, Elsevier, vol. 35(C).
    2. Nihad Aliyev, 2019. "Financial Markets with Multidimensional Uncertainty," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2019.
    3. Donald Lien & Pi-Hsia Hung, 2023. "Whose trades contribute more to price discovery? Evidence from the Taiwan stock exchange," Review of Quantitative Finance and Accounting, Springer, vol. 61(1), pages 213-263, July.
    4. Tahara, Hiroki, 2020. "On the Applicability of the Black-Scholes Model to the Inverse Quantity of Price (Under Peer-Review)," OSF Preprints fgnca, Center for Open Science.
    5. Sensoy, Ahmet & Serdengeçti, Süleyman, 2019. "Intraday volume-volatility nexus in the FX markets: Evidence from an emerging market," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 1-12.
    6. Nihad Aliyev & Xue-Zhong He, 2017. "Ambiguous Market Making," Research Paper Series 383, Quantitative Finance Research Centre, University of Technology, Sydney.

  4. Nikola Gradojevic & Camillo Lento, 2015. "Multiscale analysis of foreign exchange order flows and technical trading profitability," Post-Print hal-01563053, HAL.

    Cited by:

    1. Jin, Xiaoye, 2021. "What do we know about the popularity of technical analysis in foreign exchange markets? A skewness preference perspective," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    2. Rangan Gupta & Patrick Kanda & Mark E. Wohar, 2021. "Predicting Stock Market Movements in the United States: The Role of Presidential Approval Ratings," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 324-335, March.
    3. Cheema, Muhammad A. & Nartea, Gilbert V & Man, Yimei, 2017. "Cross-Sectional and Time-Series Momentum Returns and Market States," MPRA Paper 78989, University Library of Munich, Germany.
    4. Fotini Economou & Konstantinos Gavriilidis & Bartosz Gebka & Vasileios Kallinterakis, 2022. "Feedback trading: a review of theory and empirical evidence," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 15(4), pages 429-476, February.
    5. Day, Min-Yuh & Ni, Yensen & Huang, Paoyu, 2019. "Trading as sharp movements in oil prices and technical trading signals emitted with big data concerns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 349-372.
    6. Konstandinos Chourmouziadis & Dimitra K. Chourmouziadou & Prodromos D. Chatzoglou, 2021. "Embedding Four Medium-Term Technical Indicators to an Intelligent Stock Trading Fuzzy System for Predicting: A Portfolio Management Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1183-1216, April.
    7. Osman Kilic & Joseph M. Marks & Kiseok Nam, 2022. "Predictable asset price dynamics, risk-return tradeoff, and investor behavior," Review of Quantitative Finance and Accounting, Springer, vol. 59(2), pages 749-791, August.
    8. Syed Jawad Hussain Shahzad & Jose Arreola‐Hernandez & Md Lutfur Rahman & Gazi Salah Uddin & Muhammad Yahya, 2021. "Asymmetric interdependence between currency markets' volatilities across frequencies and time scales," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2436-2457, April.
    9. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Does investor attention matter? The attention-return relationships in FX markets," Economic Modelling, Elsevier, vol. 68(C), pages 644-660.
    10. Bouoiyour, Jamal & Selmi, Refk, 2015. "Is the Internet Search Driving Oil Market? A Revisit through Time-Frequency approaches," MPRA Paper 66214, University Library of Munich, Germany.
    11. Tzu‐Pu Chang, 2021. "Buy Low and Sell High: The 52‐Week Price Range and Predictability of Returns," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 336-344, March.
    12. Ni, Yensen & Liao, Yi-Ching & Huang, Paoyu, 2015. "MA trading rules, herding behaviors, and stock market overreaction," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 253-265.

  5. Nikola Gradojevic & Marko Caric, 2015. "Predicting Systemic Risk with Entropic Indicators," Working Paper series 15-14, Rimini Centre for Economic Analysis.

    Cited by:

    1. Nikola Gradojevic, 2021. "Brexit and foreign exchange market expectations: Could it have been predicted?," Annals of Operations Research, Springer, vol. 297(1), pages 167-189, February.
    2. Shternshis, Andrey & Mazzarisi, Piero & Marmi, Stefano, 2022. "Measuring market efficiency: The Shannon entropy of high-frequency financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    3. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    4. Barbagli, Matteo & François, Pascal & Gauthier, Geneviève & Vrins, Frédéric, 2024. "The role of CDS spreads in explaining bond recovery rates," LIDAM Discussion Papers LFIN 2024002, Université catholique de Louvain, Louvain Finance (LFIN).
    5. Casarin, Roberto & Costola, Michele, 2019. "Structural changes in large economic datasets: A nonparametric homogeneity test," Economics Letters, Elsevier, vol. 176(C), pages 55-59.
    6. Radhika Prosad Datta, 2023. "Leveraging Sample Entropy for Enhanced Volatility Measurement and Prediction in International Oil Price Returns," Papers 2312.12788, arXiv.org.
    7. Li, Chuchu & Lin, Qin & Huang, Dong & Grifoll, Manel & Yang, Dong & Feng, Hongxiang, 2023. "Is entropy an indicator of port traffic predictability? The evidence from Chinese ports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).

  6. Nikola Gradojevic, 2015. "Multi-criteria Classification for Pricing European Options," Working Paper series 15-13, Rimini Centre for Economic Analysis.

    Cited by:

    1. Nikola Gradojevic, 2021. "Brexit and foreign exchange market expectations: Could it have been predicted?," Annals of Operations Research, Springer, vol. 297(1), pages 167-189, February.

  7. Ramazan Gençay & Nikola Gradojevic & Richard Olsen & Faruk Selçuk, 2015. "Informed traders' arrival in foreign exchange markets: Does geography matter?," Post-Print hal-01563055, HAL.

    Cited by:

    1. Meifen Qian & Bin Yu & Qianyu Zhu, 2018. "Noise traders, firm-specific uncertainty and technical trading effectiveness," Applied Economics Letters, Taylor & Francis Journals, vol. 25(13), pages 918-923, July.
    2. Gradojevic, Nikola & Erdemlioglu, Deniz & Gençay, Ramazan, 2017. "Informativeness of trade size in foreign exchange markets," Economics Letters, Elsevier, vol. 150(C), pages 27-33.
    3. Gençay, Ramazan & Gradojevic, Nikola, 2013. "Private information and its origins in an electronic foreign exchange market," Economic Modelling, Elsevier, vol. 33(C), pages 86-93.
    4. Sandra Ferreruela & Daniel Martín, 2022. "Market Quality and Short-Selling Ban during the COVID-19 Pandemic: A High-Frequency Data Approach," JRFM, MDPI, vol. 15(7), pages 1-29, July.

  8. Nikola Gradojevic, 2013. "Foreign exchange customers and dealers: Who’s driving whom?," Working Papers 2013-FIN-03, IESEG School of Management.

    Cited by:

    1. Batten, Jonathan A. & Lucey, Brian M. & Peat, Maurice, 2016. "Gold and silver manipulation: What can be empirically verified?," Economic Modelling, Elsevier, vol. 56(C), pages 168-176.

  9. Dragan Kukolj & Nikola Gradojevic & Camillo Lento, 2012. "Improving Non-Parametric Option Pricing during the Financial Crisis," Working Paper series 35_12, Rimini Centre for Economic Analysis.

    Cited by:

    1. Nikola Gradojevic, 2015. "Multi-criteria Classification for Pricing European Options," Working Paper series 15-13, Rimini Centre for Economic Analysis.

  10. Ramazan Gencay & Nikola Gradojevic & Faruk Selcuk, 2009. "Profitability in an Electronic Foreign Exchange Market: Informed Trading or Differences in Valuation?," Working Paper series 25_09, Rimini Centre for Economic Analysis.

    Cited by:

    1. Sirimon Treepongkaruna & Robert Brooks & Stephen Gray, 2012. "Do trading hours affect volatility links in the foreign exchange market?," Australian Journal of Management, Australian School of Business, vol. 37(1), pages 7-27, April.
    2. Bekiros, Stelios D., 2015. "Heuristic learning in intraday trading under uncertainty," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 34-49.

  11. Nikola Gradojevic & Ramazan Gencay & Dragan Kukolj, 2009. "Option Pricing with Modular Neural Networks," Working Paper series 32_09, Rimini Centre for Economic Analysis.

    Cited by:

    1. Yan Liu & Xiong Zhang, 2023. "Option Pricing Using LSTM: A Perspective of Realized Skewness," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    2. Fei Chen & Charles Sutcliffe, 2012. "Pricing And Hedging Short Sterling Options Using Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 128-149, April.
    3. Nikola Gradojevic, 2015. "Multi-criteria Classification for Pricing European Options," Working Paper series 15-13, Rimini Centre for Economic Analysis.
    4. Nikola Gradojevic, 2021. "Brexit and foreign exchange market expectations: Could it have been predicted?," Annals of Operations Research, Springer, vol. 297(1), pages 167-189, February.
    5. Cao, Yi & Liu, Xiaoquan & Zhai, Jia, 2021. "Option valuation under no-arbitrage constraints with neural networks," European Journal of Operational Research, Elsevier, vol. 293(1), pages 361-374.
    6. Raquel M. Gaspar & Sara D. Lopes & Bernardo Sequeira, 2020. "Neural Network pricing of American put options," Working Papers REM 2020/0122, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    7. Ke Nian & Thomas F. Coleman & Yuying Li, 2018. "Learning minimum variance discrete hedging directly from the market," Quantitative Finance, Taylor & Francis Journals, vol. 18(7), pages 1115-1128, July.
    8. Yongxin Yang & Yu Zheng & Timothy M. Hospedales, 2016. "Gated Neural Networks for Option Pricing: Rationality by Design," Papers 1609.07472, arXiv.org, revised Mar 2020.
    9. Nikola Gradojevic & Dragan Kukolj & Ramazan Gencay, 2011. "Clustering and Classification in Option Pricing," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 3(2), pages 109-128, October.
    10. Muyang Ge & Shen Zhou & Shijun Luo & Boping Tian, 2021. "3D Tensor-based Deep Learning Models for Predicting Option Price," Papers 2106.02916, arXiv.org, revised Sep 2021.
    11. Dragan Kukolj & Nikola Gradojevic & Camillo Lento, 2012. "Improving Non-Parametric Option Pricing during the Financial Crisis," Working Paper series 35_12, Rimini Centre for Economic Analysis.
    12. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    13. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    14. Efe Arin & A. Murat Ozbayoglu, 2022. "Deep Learning Based Hybrid Computational Intelligence Models for Options Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 39-58, January.
    15. Liu, Xiaoquan & Cao, Yi & Ma, Chenghu & Shen, Liya, 2019. "Wavelet-based option pricing: An empirical study," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1132-1142.
    16. Hammad Siddiqi & Sajid Anwar, 2020. "The Pricing Kernel Puzzle: A Real Phenomenon or a Statistical Artifact?," International Review of Finance, International Review of Finance Ltd., vol. 20(2), pages 485-491, June.
    17. Joseph L. Breeden & Eugenia Leonova, 2021. "Creating Unbiased Machine Learning Models by Design," JRFM, MDPI, vol. 14(11), pages 1-15, November.
    18. Anindya Goswami & Sharan Rajani & Atharva Tanksale, 2020. "Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning," Papers 2008.00462, arXiv.org, revised Dec 2020.
    19. Ortíz Arango Francisco & Cabrera Llanos Agustín Ignacio & López Herrera Francisco, 2013. "Pronóstico de los índices accionarios DAX y S&P 500 con redes neuronales diferenciales," Contaduría y Administración, Accounting and Management, vol. 58(3), pages 203-225, julio-sep.
    20. Dbouk, Wassim & Jamali, Ibrahim, 2018. "Predicting daily oil prices: Linear and non-linear models," Research in International Business and Finance, Elsevier, vol. 46(C), pages 149-165.
    21. Yang Qu & Ming-Xi Wang, 2019. "The option pricing model based on time values: an application of the universal approximation theory on unbounded domains," Papers 1910.01490, arXiv.org, revised Apr 2021.
    22. Adamu Abubakar & Haruna Chiroma & Abdullah Khan & Mukhtar Fatihu Hamza & Ali Baba Dauda & Mahmood Nadeem & Shah Asadullah & Jaafar Zubairu Maitama & Tutut Herawan, 2016. "Utilizing Modular Neural Network for Prediction of Possible Emergencies Locations within Point of Interest of Hajj Pilgrimage," Modern Applied Science, Canadian Center of Science and Education, vol. 10(2), pages 1-34, February.

  12. Ramazan Gencay & Nikola Gradojevic, 2009. "Informed Trading in an Electronic Foreign Exchange Market," Working Paper series 24_09, Rimini Centre for Economic Analysis.

    Cited by:

    1. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    2. Sirimon Treepongkaruna & Robert Brooks & Stephen Gray, 2012. "Do trading hours affect volatility links in the foreign exchange market?," Australian Journal of Management, Australian School of Business, vol. 37(1), pages 7-27, April.

  13. Ramazan Gencay & Nikola Gradojevic, 2009. "Crash of '87 - Was it Expected? Aggregate Market Fears and Long Range Dependence," Working Paper series 28_09, Rimini Centre for Economic Analysis.

    Cited by:

    1. Loretta Mastroeni & Pierluigi Vellucci, 2016. "“Butterfly Effect" vs Chaos in Energy Futures Markets," Departmental Working Papers of Economics - University 'Roma Tre' 0209, Department of Economics - University Roma Tre.
    2. Nikola Gradojevic, 2021. "Brexit and foreign exchange market expectations: Could it have been predicted?," Annals of Operations Research, Springer, vol. 297(1), pages 167-189, February.
    3. Lutz, Chandler, 2015. "The impact of conventional and unconventional monetary policy on investor sentiment," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 89-105.
    4. Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    5. Bekiros, Stelios & Jlassi, Mouna & Lucey, Brian & Naoui, Kamel & Uddin, Gazi Salah, 2017. "Herding behavior, market sentiment and volatility: Will the bubble resume?," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 107-131.
    6. Nikola Gradojevic & Marko Caric, 2015. "Predicting Systemic Risk with Entropic Indicators," Working Paper series 15-14, Rimini Centre for Economic Analysis.
    7. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Espinosa-Paredes, Gilberto, 2012. "Is the US stock market becoming weakly efficient over time? Evidence from 80-year-long data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5643-5647.
    8. Paulo Ferreira, 2020. "Dynamic long-range dependences in the Swiss stock market," Empirical Economics, Springer, vol. 58(4), pages 1541-1573, April.
    9. Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Chaos" in energy and commodity markets: a controversial matter," Papers 1611.07432, arXiv.org, revised Mar 2017.
    10. Namaki, A. & Koohi Lai, Z. & Jafari, G.R. & Raei, R. & Tehrani, R., 2013. "Comparing emerging and mature markets during times of crises: A non-extensive statistical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3039-3044.
    11. Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Butterfly Effect" vs Chaos in Energy Futures Markets," Papers 1610.05697, arXiv.org.
    12. Alvarez-Ramirez, J. & Rodriguez, E. & Espinosa-Paredes, G., 2012. "A partisan effect in the efficiency of the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4923-4932.
    13. Bekiros, Stelios D., 2015. "Heuristic learning in intraday trading under uncertainty," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 34-49.

  14. Nikola Gradojevic & Ramazan Gençay, 2009. "Overnight Interest Rates and Aggregate Market Expectations," Working Paper series 26_09, Rimini Centre for Economic Analysis.

    Cited by:

    1. Loretta Mastroeni & Pierluigi Vellucci, 2016. "“Butterfly Effect" vs Chaos in Energy Futures Markets," Departmental Working Papers of Economics - University 'Roma Tre' 0209, Department of Economics - University Roma Tre.
    2. Kuzubaş, Tolga Umut & Ömercikoğlu, Inci & Saltoğlu, Burak, 2014. "Network centrality measures and systemic risk: An application to the Turkish financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 203-215.
    3. Bekiros, Stelios & Jlassi, Mouna & Lucey, Brian & Naoui, Kamel & Uddin, Gazi Salah, 2017. "Herding behavior, market sentiment and volatility: Will the bubble resume?," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 107-131.
    4. Nikola Gradojevic & Marko Caric, 2015. "Predicting Systemic Risk with Entropic Indicators," Working Paper series 15-14, Rimini Centre for Economic Analysis.
    5. Ramazan Gencay & Nikola Gradojevic, 2009. "Crash of '87 - Was it Expected? Aggregate Market Fears and Long Range Dependence," Working Paper series 28_09, Rimini Centre for Economic Analysis.
    6. Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Chaos" in energy and commodity markets: a controversial matter," Papers 1611.07432, arXiv.org, revised Mar 2017.
    7. Namaki, A. & Koohi Lai, Z. & Jafari, G.R. & Raei, R. & Tehrani, R., 2013. "Comparing emerging and mature markets during times of crises: A non-extensive statistical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3039-3044.
    8. Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Butterfly Effect" vs Chaos in Energy Futures Markets," Papers 1610.05697, arXiv.org.
    9. Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.
    10. Ahmad Hajihasani & Ali Namaki & Nazanin Asadi & Reza Tehrani, 2020. "Non-Extensive Value-at-Risk Estimation During Times of Crisis," Papers 2005.09036, arXiv.org, revised Jan 2021.
    11. Sensoy, A., 2013. "Effects of monetary policy on the long memory in interest rates: Evidence from an emerging market," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 85-88.
    12. Raja Mazhar Hameed & Abdul Rafae Mazhar Raja & Nida Zahid, 2023. "Herding Spillover among the Stock Markets: Pakistan & China Covering Covid-19 and Its Repercussions," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 9(2), pages 257-267.

  15. Ramazan Gencay & Nikola Gradojevic, 2009. "Errors-in-Variables Estimation with No Instruments," Working Paper series 30_09, Rimini Centre for Economic Analysis.

    Cited by:

    1. Huijun Guo & Youming Liu, 2017. "Strong consistency of wavelet estimators for errors-in-variables regression model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 121-144, February.
    2. Stelios Bekiros & Duc Khuong Nguyen & Gazi Salah Uddin & Bo Sjö, 2014. "Business Cycle (De)Synchronization in the Aftermath of the Global Financial Crisis: Implications for the Euro Area," Working Papers 2014-437, Department of Research, Ipag Business School.
    3. Reese, Simon & Li, Yushu, 2013. "Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements," Working Papers 2013:36, Lund University, Department of Economics.
    4. Chen Yi-Ting & Sun Edward W. & Yu Min-Teh, 2015. "Improving model performance with the integrated wavelet denoising method," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(4), pages 445-467, September.
    5. Yi-Ting Chen & Edward W. Sun & Min-Teh Yu, 2018. "Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 653-684, August.
    6. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    7. Yazgan, M. Ege & Özkan, Harun, 2015. "Detecting structural changes using wavelets," Finance Research Letters, Elsevier, vol. 12(C), pages 23-37.
    8. Bruzda Joanna, 2015. "Amplitude and phase synchronization of European business cycles: a wavelet approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 625-655, December.
    9. Gallegati, Marco & Ramsey, James B., 2013. "Bond vs stock market's Q: Testing for stability across frequencies and over time," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 138-150.

  16. Ramazan Gencay & Nikola Gradojevic & Faruk Selcuk & Brandon Whitcher, 2009. "Asymmetry of Information Flow Between Volatilities Across Time Scales," Working Paper series 27_09, Rimini Centre for Economic Analysis.

    Cited by:

    1. Khalfaoui, Rabeh, 2018. "Oil–gold time varying nexus: A time–frequency analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 86-104.
    2. François Benhmad, 2011. "A wavelet analysis of oil price volatility dynamic," Economics Bulletin, AccessEcon, vol. 31(1), pages 792-806.
    3. Hafner, Christian, 2012. "Cross-correlating wavelet coefficients with applications to high-frequency financial time series," LIDAM Reprints ISBA 2012027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Hasan, Mudassar & Arif, Muhammad & Naeem, Muhammad Abubakr & Ngo, Quang-Thanh & Taghizadeh–Hesary, Farhad, 2021. "Time-frequency connectedness between Asian electricity sectors," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 208-224.
    5. Roger Bowden & Jennifer Zhu, 2010. "Multi-scale variation, path risk and long-term portfolio management," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 783-796.
    6. Pata, Ugur Korkut & Kartal, Mustafa Tevfik & Erdogan, Sinan & Sarkodie, Samuel Asumadu, 2023. "The role of renewable and nuclear energy R&D expenditures and income on environmental quality in Germany: Scrutinizing the EKC and LCC hypotheses with smooth structural changes," Applied Energy, Elsevier, vol. 342(C).
    7. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2020. "Dynamic frequency connectedness between oil and natural gas volatilities," Economic Modelling, Elsevier, vol. 84(C), pages 181-189.
    8. Swastika, Purti & Dewandaru, Ginanjar & Masih, Mansur, 2013. "The Impact of Debt on Economic Growth: A Case Study of Indonesia," MPRA Paper 58837, University Library of Munich, Germany.
    9. Vaclav Broz & Evzen Kocenda, 2019. "Mortgage-Related Bank Penalties and Systemic Risk Among U.S. Banks," Working Papers IES 2019/25, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2019.
    10. Fratianni, Michele & Gallegati, Marco & Giri, Federico, 2022. "The medium-run Phillips curve: A time–frequency investigation for the UK," Journal of Macroeconomics, Elsevier, vol. 73(C).
    11. Gallegati, Marco & Delli Gatti, Domenico, 2018. "Macrofinancial imbalances in historical perspective: A global crisis index," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 190-205.
    12. Michis, Antonis A., 2014. "Investing in gold: Individual asset risk in the long run," Finance Research Letters, Elsevier, vol. 11(4), pages 369-374.
    13. Erdost Torun & Afife Duygu Ayhan Akdeniz & Erhan Demireli & Simon Grima, 2022. "Long-Term US Economic Growth and the Carbon Dioxide Emissions Nexus: A Wavelet-Based Approach," Sustainability, MDPI, vol. 14(17), pages 1-16, August.
    14. Arif, Muhammad & Hasan, Mudassar & Alawi, Suha M. & Naeem, Muhammad Abubakr, 2021. "COVID-19 and time-frequency connectedness between green and conventional financial markets," Global Finance Journal, Elsevier, vol. 49(C).
    15. Deniz Erdemlioglu & Nikola Gradojevic, 2020. "Heterogeneous investment horizons, risk regimes, and realized jumps," Post-Print hal-02995997, HAL.
    16. Theo Berger & Ramazan Gençay, 2020. "Short‐run wavelet‐based covariance regimes for applied portfolio management," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 642-660, July.
    17. Pata, Ugur Korkut & Yilanci, Veli & Zhang, Qianxiao & Shah, Syed Ale Raza, 2022. "Does financial development promote renewable energy consumption in the USA? Evidence from the Fourier-wavelet quantile causality test," Renewable Energy, Elsevier, vol. 196(C), pages 432-443.
    18. Thomas Conlon & John Cotter, 2012. "Downside risk and the energy hedger's horizon," Working Papers 201219, Geary Institute, University College Dublin.
    19. Olivier Habimana, 2019. "Wavelet Multiresolution Analysis of the Liquidity Effect and Monetary Neutrality," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 85-110, January.
    20. Uluyol, Burhan & Hui Pu, Suan & Shaturaev, Jakhongir & Kanaparan, Geetha, 2023. "Cracking the Code of Market Secrets: A Deep Dive into Financial Anomalies," MPRA Paper 119039, University Library of Munich, Germany, revised 05 Oct 2023.
    21. Saâdaoui, Foued & Naifar, Nader & Aldohaiman, Mohamed S., 2017. "Predictability and co-movement relationships between conventional and Islamic stock market indexes: A multiscale exploration using wavelets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 552-568.
    22. Caporin, Massimiliano & Naeem, Muhammad Abubakr & Arif, Muhammad & Hasan, Mudassar & Vo, Xuan Vinh & Hussain Shahzad, Syed Jawad, 2021. "Asymmetric and time-frequency spillovers among commodities using high-frequency data," Resources Policy, Elsevier, vol. 70(C).
    23. Jozef Baruník & Evžen Kocenda, 2019. "Total, Asymmetric and Frequency Connectedness Between Oil and Forex Markets," CESifo Working Paper Series 7756, CESifo.
    24. Dieter Hendricks & Tim Gebbie & Diane Wilcox, 2015. "Detecting intraday financial market states using temporal clustering," Papers 1508.04900, arXiv.org, revised Feb 2017.
    25. Muhammad Abubakr Naeem & Mudassar Hasan & Abraham Agyemang & Md Iftekhar Hasan Chowdhury & Faruk Balli, 2023. "Time‐frequency dynamics between fear connectedness of stocks and alternative assets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2188-2201, April.
    26. Naeem, Muhammad Abubakr & Karim, Sitara & Hasan, Mudassar & Lucey, Brian M. & Kang, Sang Hoon, 2022. "Nexus between oil shocks and agriculture commodities: Evidence from time and frequency domain," Energy Economics, Elsevier, vol. 112(C).
    27. Liu, Haiying & Pata, Ugur Korkut & Zafar, Muhammad Wasif & Kartal, Mustafa Tevfik & Karlilar, Selin & Caglar, Abdullah Emre, 2023. "Do oil and natural gas prices affect carbon efficiency? Daily evidence from China by wavelet transform-based approaches," Resources Policy, Elsevier, vol. 85(PB).
    28. Kaijian He & Kin Keung Lai & Guocheng Xiang, 2012. "Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach," Energies, MDPI, vol. 5(4), pages 1-26, April.
    29. Jozef Barunik & Lukas Vacha, 2015. "Realized wavelet-based estimation of integrated variance and jumps in the presence of noise," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1347-1364, August.
    30. Al Rababa’a, Abdel Razzaq & Alomari, Mohammad & McMillan, David, 2021. "Multiscale stock-bond correlation: Implications for risk management," Research in International Business and Finance, Elsevier, vol. 58(C).
    31. Gradojevic, Nikola & Tsiakas, Ilias, 2021. "Volatility cascades in cryptocurrency trading," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 252-265.
    32. Yi-Ting Chen & Edward W. Sun & Min-Teh Yu, 2018. "Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 653-684, August.
    33. Haiyun Xu, 2016. "Economic policy uncertainty and housing returns in Germany: Evidence from a bootstrap rolling window," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 34(2), pages 309-332.
    34. Marco Gallegati & Mauro Gallegati & James B. Ramsey & Willi Semmler, 2017. "Long waves in prices: new evidence from wavelet analysis," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 11(1), pages 127-151, January.
    35. Bhatia, Vaneet & Das, Debojyoti & Kumar, Surya Bhushan, 2020. "Hedging effectiveness of precious metals across frequencies: Evidence from Wavelet based Dynamic Conditional Correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    36. UÄŸur UrsavaÅŸ & Veli Yilanci, 2023. "Convergence analysis of ecological footprint at different time scales: Evidence from Southern Common Market countries," Energy & Environment, , vol. 34(2), pages 429-442, March.
    37. Juliana Malagon & David Moreno & Rosa Rodr�guez, 2015. "Time horizon trading and the idiosyncratic risk puzzle," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 327-343, February.
    38. Bera, Anil Kumar & Uyar, Umut & Kangalli Uyar, Sinem Guler, 2020. "Analysis of the five-factor asset pricing model with wavelet multiscaling approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 414-423.
    39. Benhmad, François, 2013. "Bull or bear markets: A wavelet dynamic correlation perspective," Economic Modelling, Elsevier, vol. 32(C), pages 576-591.
    40. Muhammed Sehid Gorus & Veli Yilanci & Maxwell Kongkuah, 2023. "FDI Inflows-Economic Globalization Nexus in ASEAN Countries: The Panel Bootstrap Causality Test Based on Wavelet Decomposition," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(2), pages 339-362, June.
    41. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    42. Jozef Barunik & Evzen Kocenda & Lukas Vacha, 2013. "Gold, Oil, and Stocks," Papers 1308.0210, arXiv.org, revised Mar 2014.
    43. Suleman, Muhammad Tahir & Rehman, Mobeen Ur & Sheikh, Umaid A. & Kang, Sang Hoon, 2023. "Dynamic time-frequency connectedness between European emissions trading system and sustainability markets," Energy Economics, Elsevier, vol. 123(C).
    44. Francis In & Sangbae Kim, 2012. "An Introduction to Wavelet Theory in Finance:A Wavelet Multiscale Approach," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8431, January.
    45. Takaki Hayashi & Yuta Koike, 2017. "Multi-scale analysis of lead-lag relationships in high-frequency financial markets," Papers 1708.03992, arXiv.org, revised May 2020.
    46. Sun, Edward W. & Chen, Yi-Ting & Yu, Min-Teh, 2015. "Generalized optimal wavelet decomposing algorithm for big financial data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 194-214.
    47. Das, Debojyoti & Bhowmik, Puja & Jana, R.K., 2018. "A multiscale analysis of stock return co-movements and spillovers: Evidence from Pacific developed markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 379-393.
    48. Bekiros, Stelios D., 2015. "Heuristic learning in intraday trading under uncertainty," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 34-49.
    49. Vincent Charvin & Jonathan Fullwood & Jessica James, 2014. "The fair value of FX options. Do you get what you pay for?," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 15-23, January.
    50. Mudassar Hasan & Muhammad Abubakr Naeem & Muhammad Arif & Syed Jawad Hussain Shahzad & Xuan Vinh Vo, 2022. "Liquidity connectedness in cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    51. Benhmad, François, 2012. "Modeling nonlinear Granger causality between the oil price and U.S. dollar: A wavelet based approach," Economic Modelling, Elsevier, vol. 29(4), pages 1505-1514.
    52. Long Hai Vo & Duc Hong Vo, 2020. "Modelling Australian Dollar Volatility at Multiple Horizons with High-Frequency Data," Risks, MDPI, vol. 8(3), pages 1-16, August.
    53. Mudassar Hasan & Muhammad Abubakr Naeem & Muhammad Arif & Syed Jawad Hussain Shahzad & Safwan Mohd Nor, 2020. "Geopolitical Risk and Tourism Stocks of Emerging Economies," Sustainability, MDPI, vol. 12(21), pages 1-21, November.
    54. BEN ABDALLAH Mohamed & TALBI Omar, 2024. "A Wavelet Analysis of Bitcoin Price Volatility Dynamic," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(1), pages 951-964, January.
    55. Alessandro Cardinali, 2009. "A Generalized Multiscale Analysis Of The Predictive Content Of Eurodollar Implied Volatilities," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 1-18.
    56. Gallegati, Marco & Ramsey, James B., 2013. "Bond vs stock market's Q: Testing for stability across frequencies and over time," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 138-150.

  17. Nikola Gradojevic & Christopher J. Neely, 2008. "The dynamic interaction of order flows and the CAD/USD exchange rate," Working Papers 2008-006, Federal Reserve Bank of St. Louis.

    Cited by:

    1. King, Michael & Sarno, Lucio & Sojli, Elvira, 2010. "Timing exchange rates using order flow: The case of the Loonie," Journal of Banking & Finance, Elsevier, vol. 34(12), pages 2917-2928, December.
    2. Jacob Gyntelberg & Mico Loretan & Tientip Subhanij & Eric Chan, 2009. "Private information, stock markets, and exchange rates," Working Papers 2009-07, Monetary Policy Group, Bank of Thailand.

  18. Nikola Gradojevic & Jing Yang, 2000. "The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure Variables," Staff Working Papers 00-23, Bank of Canada.

    Cited by:

    1. Cem Kadilar & Muammer Simsek & Cagdas Hakan Aladag, 2009. "Forecasting The Exchange Rate Series With Ann: The Case Of Turkey," Istanbul University Econometrics and Statistics e-Journal, Department of Econometrics, Faculty of Economics, Istanbul University, vol. 9(1), pages 17-29, May.
    2. Martha A. Misas A. & Enrique López E. & Carlos A. Arango A. & uan Nicolás Hernández A., 2004. "No-linealidades en la demanda de efectivo en Colombia: las redes neuronales como herramienta de pronóstico," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 22(45), pages 10-57, June.
    3. Martha Misas Arango & Enrique López Enciso & Pablo Querubín Borrero, 2002. "La Inflación en Colombia: Una Aproximación desde las Redes Neuronales," Borradores de Economia 3029, Banco de la Republica.
    4. Ghaffari, Ali & Zare, Samaneh, 2009. "A novel algorithm for prediction of crude oil price variation based on soft computing," Energy Economics, Elsevier, vol. 31(4), pages 531-536, July.
    5. Martha Misas Arango & Enrique López Enciso & Pablo Querubín Borrero, 2002. "La inflación en Colombia: una aproximación desde las redes neuronales," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 20(41-42), pages 143-214, June.
    6. Koffi Dumor & Komlan Gbongli, 2021. "Trade impacts of the New Silk Road in Africa: Insight from Neural Networks Analysis," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 17(02), pages 13-26.
    7. Koffi Dumor & Li Yao, 2019. "Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis," Sustainability, MDPI, vol. 11(5), pages 1-22, March.
    8. Martha Misas A. & Enrique López E. & Carlos A. Arango A. & Juan Nicolás Hernández A., 2003. "La Demanda de Efectivo en Colombia: Una Caja Nagra a la Luz de las Redes Neuronales," Borradores de Economia 2963, Banco de la Republica.
    9. Koffi Dumor & Li Yao & Jean-Paul Ainam & Edem Koffi Amouzou & Williams Ayivi, 2021. "Quantitative Dynamics Effects of Belt and Road Economies Trade Using Structural Gravity and Neural Networks," SAGE Open, , vol. 11(3), pages 21582440211, July.

Articles

  1. Gradojevic, Nikola & Tsiakas, Ilias, 2021. "Volatility cascades in cryptocurrency trading," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 252-265.

    Cited by:

    1. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    2. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    3. Katsiampa, Paraskevi & Yarovaya, Larisa & Zięba, Damian, 2022. "High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    4. Jiri Kukacka & Ladislav Kristoufek, 2023. "Fundamental and speculative components of the cryptocurrency pricing dynamics," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.

  2. Deniz Erdemlioglu & Nikola Gradojevic, 2021. "Heterogeneous investment horizons, risk regimes, and realized jumps," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 617-643, January.
    See citations under working paper version above.
  3. Camillo Lento & Nikola Gradojevic, 2021. "S&P 500 Index Price Spillovers around the COVID-19 Market Meltdown," JRFM, MDPI, vol. 14(7), pages 1-13, July.

    Cited by:

    1. Constantin Anghelache & Mădălina-Gabriela Anghel & Ștefan Virgil Iacob & Mirela Panait & Irina Gabriela Rădulescu & Alina Gabriela Brezoi & Adrian Miron, 2022. "The Effects of Health Crisis on Economic Growth, Health and Movement of Population," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    2. Hakan Yilmazkuday, 2021. "COVID-19 Effects on the S&P 500 Index," Working Papers 2117, Florida International University, Department of Economics.
    3. Chen, Song Xi & Guo, Bin & Qiu, Yumou, 2023. "Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding," Journal of Econometrics, Elsevier, vol. 235(2), pages 1337-1354.
    4. Camillo Lento & Nikola Gradojevic, 2022. "The Profitability of Technical Analysis during the COVID-19 Market Meltdown," JRFM, MDPI, vol. 15(5), pages 1-19, April.
    5. Federico Mecchia & Marcellino Gaudenzi, 2022. "The dynamics of the prices of the companies of the STOXX Europe 600 Index through the logit model and neural network," Papers 2206.09899, arXiv.org.

  4. Ivana Brkić & Nikola Gradojević & Svetlana Ignjatijević, 2020. "The Impact of Economic Freedom on Economic Growth? New European Dynamic Panel Evidence," JRFM, MDPI, vol. 13(2), pages 1-13, February.

    Cited by:

    1. Anand Sharma & Vipin Sharma & Shekhar Tokas, 2022. "Institutional quality and health outcomes: evidence from the EU countries," Economics and Business Letters, Oviedo University Press, vol. 11(2), pages 70-78.
    2. Gouider, Abdessalem & Nouira, Ridha & Saafi, Sami, 2022. "Re-Exploring the Nexus Between Economic Freedom and Growth: Is There a Threshold Effect?," Journal of Economic Development, The Economic Research Institute, Chung-Ang University, vol. 47(3), pages 147-167, September.
    3. Mohammad Tariq Al Fozaie, 2022. "Behavior and Socio-Economic Development: An Interdisciplinary Perspective," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 11, November.
    4. Manuel Carlos Nogueira & Mara Madaleno, 2021. "Is the Aurora Borealis an Inspiration to the Performance of Nordic Economic Sustainability?," Sustainability, MDPI, vol. 13(17), pages 1-22, September.
    5. Tolcha, Tassew Dufera & Tchouamou Njoya, Eric & Bråthen, Svein & Holmgren, Johan, 2021. "Effects of African aviation liberalisation on economic freedom, air connectivity and related economic consequences," Transport Policy, Elsevier, vol. 110(C), pages 204-214.
    6. Jonas Rapsikevicius & Jurgita Bruneckiene & Mantas Lukauskas & Sarunas Mikalonis, 2021. "The Impact of Economic Freedom on Economic and Environmental Performance: Evidence from European Countries," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    7. Jinghui Duan & Yinuo Liu, 2022. "The Decomposition of Information and Communication Technology Products Trading: A Case Study of China," Economies, MDPI, vol. 10(6), pages 1-15, May.
    8. Gamze Sart & Yilmaz Bayar & Marina Danilina & Funda Hatice Sezgin, 2022. "Economic Freedom, Education and CO 2 Emissions: A Causality Analysis for EU Member States," IJERPH, MDPI, vol. 19(13), pages 1-14, June.
    9. Hosein Mohammadi & Samira Shayanmehr & Juan D. Borrero, 2022. "Does Freedom Matter for Sustainable Economic Development? New Evidence from Spatial Econometric Analysis," Mathematics, MDPI, vol. 11(1), pages 1-19, December.

  5. Gradojevic, Nikola & Erdemlioglu, Deniz & Gençay, Ramazan, 2020. "A new wavelet-based ultra-high-frequency analysis of triangular currency arbitrage," Economic Modelling, Elsevier, vol. 85(C), pages 57-73.
    See citations under working paper version above.
  6. Adcock, Robert & Gradojevic, Nikola, 2019. "Non-fundamental, non-parametric Bitcoin forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).

    Cited by:

    1. Akyildirim, Erdinc & Cepni, Oguzhan & Corbet, Shaen & Uddin, Gazi Salah, 2020. "Forecasting Mid-price Movement of Bitcoin Futures Using Machine Learning," Working Papers 20-2020, Copenhagen Business School, Department of Economics.
    2. Nicolás Magner & Nicolás Hardy, 2022. "Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle," Mathematics, MDPI, vol. 10(13), pages 1-27, July.
    3. Paweł Jakubowski & Robert Ślepaczuk & Franciszek Windorbski, 2023. "REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market," Working Papers 2023-20, Faculty of Economic Sciences, University of Warsaw.
    4. Sofiane Aboura, 2022. "A note on the Bitcoin and Fed Funds rate," Empirical Economics, Springer, vol. 63(5), pages 2577-2603, November.
    5. Hakan Pabuccu & Serdar Ongan & Ayse Ongan, 2023. "Forecasting the movements of Bitcoin prices: an application of machine learning algorithms," Papers 2303.04642, arXiv.org.
    6. Chen, Rui & Ren, Jinjuan, 2022. "Do AI-powered mutual funds perform better?," Finance Research Letters, Elsevier, vol. 47(PA).
    7. Gradojevic, Nikola & Tsiakas, Ilias, 2021. "Volatility cascades in cryptocurrency trading," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 252-265.
    8. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "How well do investor sentiment and ensemble learning predict Bitcoin prices?," Research in International Business and Finance, Elsevier, vol. 64(C).
    9. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    10. Jiri Kukacka & Ladislav Kristoufek, 2023. "Fundamental and speculative components of the cryptocurrency pricing dynamics," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    11. Akanksha Jalan & Roman Matkovskyy & Andrew Urquhart & Larisa Yarovaya, 2023. "The role of interpersonal trust in cryptocurrency adoption," Post-Print hal-03946536, HAL.
    12. Qiutong Guo & Shun Lei & Qing Ye & Zhiyang Fang, 2021. "MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price," Papers 2105.00707, arXiv.org.
    13. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    14. do Nascimento, José Cláudio, 2021. "The personal wealth importance to the intertemporal choice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).

  7. Nikola Gradojevic & Marko Caric, 2017. "Predicting Systemic Risk with Entropic Indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(1), pages 16-25, January.
    See citations under working paper version above.
  8. Gradojevic, Nikola & Erdemlioglu, Deniz & Gençay, Ramazan, 2017. "Informativeness of trade size in foreign exchange markets," Economics Letters, Elsevier, vol. 150(C), pages 27-33.
    See citations under working paper version above.
  9. Gradojevic Nikola, 2016. "Multi-criteria classification for pricing European options," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 123-139, April.
    See citations under working paper version above.
  10. Ramazan Gençay & Nikola Gradojevic & Richard Olsen & Faruk Selçuk, 2015. "Informed traders’ arrival in foreign exchange markets: Does geography matter?," Empirical Economics, Springer, vol. 49(4), pages 1431-1462, December.
    See citations under working paper version above.
  11. Gradojevic, Nikola & Lento, Camillo, 2015. "Multiscale analysis of foreign exchange order flows and technical trading profitability," Economic Modelling, Elsevier, vol. 47(C), pages 156-165.
    See citations under working paper version above.
  12. Gradojevic, Nikola, 2014. "Foreign exchange customers and dealers: Who’s driving whom?," Finance Research Letters, Elsevier, vol. 11(3), pages 213-218.
    See citations under working paper version above.
  13. Gradojevic, Nikola & Gençay, Ramazan, 2013. "Fuzzy logic, trading uncertainty and technical trading," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 578-586.

    Cited by:

    1. Hossein Mombeini & Abdolreza Yazdani-Chamzini & Dalia Streimikiene & Edmundas Kazimieras Zavadskas, 2018. "New fuzzy logic approach for the capability assessment of renewable energy technologies: Case of Iran," Energy & Environment, , vol. 29(4), pages 511-532, June.
    2. Cheema, Muhammad A. & Nartea, Gilbert V & Man, Yimei, 2017. "Cross-Sectional and Time-Series Momentum Returns and Market States," MPRA Paper 78989, University Library of Munich, Germany.
    3. Ülkü, Numan & Prodan, Eugeniu, 2013. "Drivers of technical trend-following rules' profitability in world stock markets," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 214-229.
    4. Day, Min-Yuh & Ni, Yensen & Huang, Paoyu, 2019. "Trading as sharp movements in oil prices and technical trading signals emitted with big data concerns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 349-372.
    5. Konstandinos Chourmouziadis & Dimitra K. Chourmouziadou & Prodromos D. Chatzoglou, 2021. "Embedding Four Medium-Term Technical Indicators to an Intelligent Stock Trading Fuzzy System for Predicting: A Portfolio Management Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1183-1216, April.
    6. Thorsten Hens & Terje Lensberg & Klaus Reiner Schenk‐Hoppé, 2018. "Front‐Running and Market Quality: An Evolutionary Perspective on High Frequency Trading," International Review of Finance, International Review of Finance Ltd., vol. 18(4), pages 727-741, December.
    7. Chen, Kuan-Hau & Su, Xuan-Qi & Lin, Li-Feng & Shih, Yi-Cheng, 2021. "Profitability of moving-average technical analysis over the firm life cycle: Evidence from Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 69(C).
    8. Vince Vella & Wing Lon Ng, 2015. "A Dynamic Fuzzy Money Management Approach for Controlling the Intraday Risk‐Adjusted Performance of AI Trading Algorithms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(2), pages 153-178, April.
    9. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos & Verousis, Thanos, 2020. "A conditional fuzzy inference approach in forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 196-216.
    10. Massimiliano Caporin & Angelo Ranaldo, 2011. "On the Predictability of Stock Prices: a Case for High and Low Prices," Working Papers 2011-11, Swiss National Bank.
    11. Chen, Rui & Ren, Jinjuan, 2022. "Do AI-powered mutual funds perform better?," Finance Research Letters, Elsevier, vol. 47(PA).
    12. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
    13. Ghandar, Adam & Michalewicz, Zbigniew & Zurbruegg, Ralf, 2016. "The relationship between model complexity and forecasting performance for computer intelligence optimization in finance," International Journal of Forecasting, Elsevier, vol. 32(3), pages 598-613.
    14. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    15. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    16. Tzu‐Pu Chang, 2021. "Buy Low and Sell High: The 52‐Week Price Range and Predictability of Returns," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 336-344, March.
    17. Ni, Yensen & Liao, Yi-Ching & Huang, Paoyu, 2015. "MA trading rules, herding behaviors, and stock market overreaction," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 253-265.
    18. Yung-Ho Chang, 2019. "Cross-market information spillover and the performance of technical trading in the foreign exchange market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(2), pages 211-227, April.
    19. Liu, Xiaojia & An, Haizhong & Wang, Lijun & Guan, Qing, 2017. "Quantified moving average strategy of crude oil futures market based on fuzzy logic rules and genetic algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 444-457.

  14. Gençay, Ramazan & Gradojevic, Nikola, 2013. "Private information and its origins in an electronic foreign exchange market," Economic Modelling, Elsevier, vol. 33(C), pages 86-93.

    Cited by:

    1. Nikola Gradojevic, 2014. "Informativeness of the Trade Size in an Electronic Foreign Exchange Market," Working Papers 2014-ACF-02, IESEG School of Management.
    2. Meifen Qian & Bin Yu & Qianyu Zhu, 2018. "Noise traders, firm-specific uncertainty and technical trading effectiveness," Applied Economics Letters, Taylor & Francis Journals, vol. 25(13), pages 918-923, July.
    3. Kitamura, Yoshihiro, 2016. "The probability of informed trading measured with price impact, price reversal, and volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 42(C), pages 77-90.
    4. Gradojevic, Nikola & Erdemlioglu, Deniz & Gençay, Ramazan, 2017. "Informativeness of trade size in foreign exchange markets," Economics Letters, Elsevier, vol. 150(C), pages 27-33.
    5. Ramazan Gençay & Nikola Gradojevic & Richard Olsen & Faruk Selçuk, 2015. "Informed traders' arrival in foreign exchange markets: Does geography matter?," Post-Print hal-01563055, HAL.
    6. Eun, Cheol S. & Kim, Soo-Hyun & Lee, Kyuseok, 2015. "Currency competition between the dollar and euro: Evidence from exchange rate behaviors," Finance Research Letters, Elsevier, vol. 12(C), pages 100-108.
    7. Gradojevic, Nikola & Erdemlioglu, Deniz & Gençay, Ramazan, 2020. "A new wavelet-based ultra-high-frequency analysis of triangular currency arbitrage," Economic Modelling, Elsevier, vol. 85(C), pages 57-73.
    8. Sheng-Ping Yang & Thanh Nguyen, 2019. "Skewness Preference and Asset Pricing: Evidence from the Japanese Stock Market," JRFM, MDPI, vol. 12(3), pages 1-10, September.

  15. Gradojevic, Nikola, 2012. "Frequency domain analysis of foreign exchange order flows," Economics Letters, Elsevier, vol. 115(1), pages 73-76.

    Cited by:

    1. Cui, Zhenyu & Taylor, Stephen, 2020. "Arbitrage detection using max plus product iteration on foreign exchange rate graphs," Finance Research Letters, Elsevier, vol. 35(C).
    2. Fotini Economou & Konstantinos Gavriilidis & Bartosz Gebka & Vasileios Kallinterakis, 2022. "Feedback trading: a review of theory and empirical evidence," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 15(4), pages 429-476, February.
    3. Nikola Gradojevic & Camillo Lento, 2012. "Multiscale Analysis of Foreign Exchange Order Flows and Technical Trading Profitability," Working Paper series 31_12, Rimini Centre for Economic Analysis.

  16. Gençay, Ramazan & Gradojevic, Nikola, 2010. "Crash of '87 -- Was it expected?: Aggregate market fears and long-range dependence," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 270-282, March.
    See citations under working paper version above.
  17. Ramazan Gencay & Nikola Gradojevic & Faruk Selcuk & Brandon Whitcher, 2010. "Asymmetry of information flow between volatilities across time scales," Quantitative Finance, Taylor & Francis Journals, vol. 10(8), pages 895-915.
    See citations under working paper version above.
  18. Gradojevic, Nikola & Gencay, Ramazan, 2008. "Overnight interest rates and aggregate market expectations," Economics Letters, Elsevier, vol. 100(1), pages 27-30, July.
    See citations under working paper version above.
  19. Gradojevic, Nikola, 2007. "The microstructure of the Canada/U.S. dollar exchange rate: A robustness test," Economics Letters, Elsevier, vol. 94(3), pages 426-432, March.

    Cited by:

    1. King, Michael & Sarno, Lucio & Sojli, Elvira, 2010. "Timing exchange rates using order flow: The case of the Loonie," Journal of Banking & Finance, Elsevier, vol. 34(12), pages 2917-2928, December.
    2. Ramazan Gençay & Nikola Gradojevic & Richard Olsen & Faruk Selçuk, 2015. "Informed traders' arrival in foreign exchange markets: Does geography matter?," Post-Print hal-01563055, HAL.
    3. Gradojevic, Nikola, 2012. "Frequency domain analysis of foreign exchange order flows," Economics Letters, Elsevier, vol. 115(1), pages 73-76.
    4. Nikola Gradojevic & Camillo Lento, 2012. "Multiscale Analysis of Foreign Exchange Order Flows and Technical Trading Profitability," Working Paper series 31_12, Rimini Centre for Economic Analysis.
    5. Chen, Pei-wen & Huang, Han-ching & Su, Yong-chern, 2014. "The central bank in market efficiency: The case of Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 29(C), pages 239-260.
    6. Nikola Gradojevic & Christopher J. Neely, 2008. "The dynamic interaction of order flows and the CAD/USD exchange rate," Working Papers 2008-006, Federal Reserve Bank of St. Louis.

  20. Gradojevic, Nikola, 2007. "Non-linear, hybrid exchange rate modeling and trading profitability in the foreign exchange market," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 557-574, February.

    Cited by:

    1. Panopoulou, Ekaterini & Souropanis, Ioannis, 2019. "The role of technical indicators in exchange rate forecasting," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 197-221.
    2. Gradojevic, Nikola & Gençay, Ramazan, 2013. "Fuzzy logic, trading uncertainty and technical trading," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 578-586.
    3. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
    4. Ghandar, Adam & Michalewicz, Zbigniew & Zurbruegg, Ralf, 2016. "The relationship between model complexity and forecasting performance for computer intelligence optimization in finance," International Journal of Forecasting, Elsevier, vol. 32(3), pages 598-613.
    5. Todea, Alexandru & Zoicas Ienciu, Adrian, 2011. "Technical Analysis and Stochastic Properties of Exchange Rate Movements: Empirical Evidence from the Romanian Currency Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 175-192, March.
    6. Dockery, Everton & Todorov, Ivan, 2023. "Further evidence on the returns to technical trading rules: Insights from fourteen currencies," Journal of Multinational Financial Management, Elsevier, vol. 69(C).
    7. Bekiros, Stelios D., 2010. "Heterogeneous trading strategies with adaptive fuzzy Actor-Critic reinforcement learning: A behavioral approach," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1153-1170, June.
    8. Taufiq Choudhry & Frank McGroarty & Ke Peng & Shiyun Wang, 2012. "High‐Frequency Exchange‐Rate Prediction With An Artificial Neural Network," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(3), pages 170-178, July.
    9. Tabak, Benjamin M. & Lima, Eduardo J.A., 2009. "Market efficiency of Brazilian exchange rate: Evidence from variance ratio statistics and technical trading rules," European Journal of Operational Research, Elsevier, vol. 194(3), pages 814-820, May.

  21. Jing Yang & Nikola Gradojevic, 2006. "Non-linear, non-parametric, non-fundamental exchange rate forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(4), pages 227-245.

    Cited by:

    1. Olcay Erdogan & Ali Goksu, 2014. "Forecasting Euro and Turkish Lira Exchange Rates with Artificial Neural Networks (ANN)," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(4), pages 307-316, October.
    2. Emekter, Riza & Jirasakuldech, Benjamas & Snaith, Sean M., 2009. "Nonlinear dynamics in foreign exchange excess returns: Tests of asymmetry," Journal of Multinational Financial Management, Elsevier, vol. 19(3), pages 179-192, July.
    3. David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2015. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Tinbergen Institute Discussion Papers 15-125/III, Tinbergen Institute.
    4. Gradojevic, Nikola, 2007. "Non-linear, hybrid exchange rate modeling and trading profitability in the foreign exchange market," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 557-574, February.
    5. Gradojevic, Nikola, 2007. "The microstructure of the Canada/U.S. dollar exchange rate: A robustness test," Economics Letters, Elsevier, vol. 94(3), pages 426-432, March.
    6. Angela He & Alan Wan, 2009. "Predicting daily highs and lows of exchange rates: a cointegration analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1191-1204.
    7. Mario Cerrato & Hyunsok Kim & Ronald MacDonald, 2010. "Microstructure order flow: statistical and economic evaluation of nonlinear forecasts," Working Papers 2010_30, Business School - Economics, University of Glasgow.
    8. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    9. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. "“An application of deep learning for exchange rate forecasting”," AQR Working Papers 202201, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2022.
    10. Oliver Blaskowitz & Helmut Herwartz, 2008. "Testing directional forecast value in the presence of serial correlation," SFB 649 Discussion Papers SFB649DP2008-073, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    11. Tasadduq Imam & Kevin Tickle & Abdullahi Ahmed & William Guo, 2012. "Linear Relationship Between The Aud/Usd Exchange Rate And The Respective Stock Market Indices: A Computational Finance Perspective," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 19-42, January.
    12. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    13. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    14. Blaskowitz, Oliver & Herwartz, Helmut, 2014. "Testing the value of directional forecasts in the presence of serial correlation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 30-42.
    15. Chen, Pei-wen & Huang, Han-ching & Su, Yong-chern, 2014. "The central bank in market efficiency: The case of Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 29(C), pages 239-260.
    16. 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.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.