IDEAS home Printed from https://ideas.repec.org/f/pam312.html

Apostolos Ampountolas

Personal Details

First Name:Apostolos
Middle Name:
Last Name:Ampountolas
Suffix:
In ASCII letters:
RePEc Short-ID:pam312
[This author has chosen not to make the email address public]

Research output

as
Jump to: Working papers Articles

Working papers

  1. Apostolos Ampountolas, 2023. "The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility -- A Two-Stage DCC-EGARCH Model Analysis," Papers 2307.09137, arXiv.org.
  2. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.

Articles

  1. Lo Mascolo, Giuseppina & Ampountolas, Apostolos & Chiodi, Marcello & Mocciaro Li Destri, Arabella & Levanti, Gabriella, 2026. "Climate-induced tourism breaks: Segmented-GAM analysis," Annals of Tourism Research, Elsevier, vol. 118(C).
  2. Ampountolas, Apostolos & Saglam, Yagmur, 2026. "Green finance transmission mechanisms and renewable energy deployment: Threshold effects in EU carbon markets," Research in International Business and Finance, Elsevier, vol. 89(C).
  3. Mark Legg & Apostolos Ampountolas & Asit Bandyopadhayay, 2026. "How personality traits influence perceptions of casino loyalty incentives," Journal of Marketing Analytics, Palgrave Macmillan, vol. 14(2), pages 517-534, June.
  4. Ampountolas, Apostolos, 2025. "Election-induced volatility and cross-asset spillovers: The impact of political uncertainty on cryptocurrencies, stocks, and oil," Research in International Business and Finance, Elsevier, vol. 80(C).
  5. Ampountolas, Apostolos, 2025. "Political uncertainty and market regimes: Clustering evidence from the 2024 U.S. election cycle," Finance Research Letters, Elsevier, vol. 86(PB).
  6. Apostolos Ampountolas, 2025. "Predicting hotel booking cancellations: a comprehensive machine learning approach," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(6), pages 539-550, December.
  7. Apostolos Ampountolas, 2025. "Addressing complex seasonal patterns in hotel forecasting: a comparative study," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(2), pages 143-152, April.
  8. Apostolos Ampountolas & Mark Legg, 2024. "Predicting daily hotel occupancy: a practical application for independent hotels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(3), pages 197-205, June.
  9. Apostolos Ampountolas, 2024. "Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models," IJFS, MDPI, vol. 12(3), pages 1-20, June.
  10. Apostolos Ampountolas, 2024. "Forecasting Orange Juice Futures: LSTM, ConvLSTM, and Traditional Models Across Trading Horizons," JRFM, MDPI, vol. 17(11), pages 1-18, October.
  11. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.
  12. Apostolos Ampountolas, 2023. "The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility: A Two-Stage DCC-EGARCH Model Analysis," JRFM, MDPI, vol. 16(1), pages 1-17, January.
  13. Apostolos Ampountolas, 2023. "A review of: Revenue Management in the Lodging Industry Origins to the Last Frontier, by Ben Vinod, Springer Management for Professionals, p. 412, ISBN 978-3-031-14301-4 ISBN 978-3-031-14302-1 (eBook)," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(5), pages 427-428, October.
  14. Apostolos Ampountolas, 2022. "Cryptocurrencies Intraday High-Frequency Volatility Spillover Effects Using Univariate and Multivariate GARCH Models," IJFS, MDPI, vol. 10(3), pages 1-22, July.
  15. Apostolos Ampountolas, 2021. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models," Forecasting, MDPI, vol. 3(3), pages 1-16, August.

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. Apostolos Ampountolas, 2023. "The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility -- A Two-Stage DCC-EGARCH Model Analysis," Papers 2307.09137, arXiv.org.

    Cited by:

    1. Otabek Sattarov & Fazliddin Makhmudov, 2025. "Risk-Aware Crypto Price Prediction Using DQN with Volatility-Adjusted Rewards Across Multi-Period State Representations," Mathematics, MDPI, vol. 13(18), pages 1-29, September.
    2. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.
    3. Anas Eisa Abdelkreem Mohammed & Henry Mwambi & Bernard Omolo, 2024. "Time-Varying Correlations between JSE.JO Stock Market and Its Partners Using Symmetric and Asymmetric Dynamic Conditional Correlation Models," Stats, MDPI, vol. 7(3), pages 1-16, July.
    4. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.
    5. Mohamed M. Sraieb & Shahnawaz Muhammed & Vladimir Dženopoljac & Samet Gunay, 2025. "Determinants of Russia’s probability of default: evidence from domestic and global indicators," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 49(3), pages 854-882, September.
    6. Mukul Bhatnagar & Sanjay Taneja & Ramona Rupeika-Apoga, 2023. "Demystifying the Effect of the News (Shocks) on Crypto Market Volatility," JRFM, MDPI, vol. 16(2), pages 1-16, February.

  2. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.

    Cited by:

    1. Moiz Qureshi & Hasnain Iftikhar & Paulo Canas Rodrigues & Mohd Ziaur Rehman & S. A. Atif Salar, 2024. "Statistical Modeling to Improve Time Series Forecasting Using Machine Learning, Time Series, and Hybrid Models: A Case Study of Bitcoin Price Forecasting," Mathematics, MDPI, vol. 12(23), pages 1-15, November.
    2. Geng, Ru & Zhang, Hong-Kun & Gao, Yixian & Yuan, Gangnan, 2025. "Decoding global economic dynamic: A graph-based examination of contemporary ETF markets," Chaos, Solitons & Fractals, Elsevier, vol. 201(P3).
    3. Yaquelin Verenice Pantoja-Pacheco & Javier Yáñez-Mendiola, 2024. "Method for the Statistical Analysis of the Signals Generated by an Acquisition Card for Pulse Measurement," Mathematics, MDPI, vol. 12(6), pages 1-24, March.

Articles

  1. Ampountolas, Apostolos, 2025. "Election-induced volatility and cross-asset spillovers: The impact of political uncertainty on cryptocurrencies, stocks, and oil," Research in International Business and Finance, Elsevier, vol. 80(C).

    Cited by:

    1. Ampountolas, Apostolos, 2025. "Political uncertainty and market regimes: Clustering evidence from the 2024 U.S. election cycle," Finance Research Letters, Elsevier, vol. 86(PB).
    2. de Almeida, Israel Nunes & Palazzi, Rafael Baptista & Klotzle, Marcelo Cabus, 2026. "Breaking from the herd: Evidence from the 2024 U.S. election," Economics Letters, Elsevier, vol. 259(C).

  2. Apostolos Ampountolas, 2025. "Predicting hotel booking cancellations: a comprehensive machine learning approach," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(6), pages 539-550, December.

    Cited by:

    1. Harrison E. Katz & Jess Needleman & Liz Medina, 2026. "Distributional Fitting and Tail Analysis of Lead-Time Compositions: Nights vs. Revenue on Airbnb," Papers 2601.12175, arXiv.org, revised Feb 2026.
    2. Ian Yeoman, 2025. "Expanding the frontiers of revenue and pricing management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(6), pages 503-505, December.

  3. Apostolos Ampountolas & Mark Legg, 2024. "Predicting daily hotel occupancy: a practical application for independent hotels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(3), pages 197-205, June.

    Cited by:

    1. Anastasia Arabadzhyan & Paolo Figini & Laura Vici, 2026. "Have you ever priced the rain? Unravelling how the weather forecasts affect prices in the hospitality industry," Tourism Economics, , vol. 32(2), pages 367-386, March.

  4. Apostolos Ampountolas, 2024. "Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models," IJFS, MDPI, vol. 12(3), pages 1-20, June.

    Cited by:

    1. Li, Shuyue & Yarovaya, Larisa & Mishra, Tapas, 2025. "Machine learning, memory and efficiency in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 105(C).
    2. Ampountolas, Apostolos, 2025. "Election-induced volatility and cross-asset spillovers: The impact of political uncertainty on cryptocurrencies, stocks, and oil," Research in International Business and Finance, Elsevier, vol. 80(C).

  5. Apostolos Ampountolas, 2024. "Forecasting Orange Juice Futures: LSTM, ConvLSTM, and Traditional Models Across Trading Horizons," JRFM, MDPI, vol. 17(11), pages 1-18, October.

    Cited by:

    1. Jeffrey Vitale & John Robinson, 2025. "In-Season Price Forecasting in Cotton Futures Markets Using ARIMA, Neural Network, and LSTM Machine Learning Models," JRFM, MDPI, vol. 18(2), pages 1-19, February.

  6. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.

    Cited by:

    1. Moiz Qureshi & Hasnain Iftikhar & Paulo Canas Rodrigues & Mohd Ziaur Rehman & S. A. Atif Salar, 2024. "Statistical Modeling to Improve Time Series Forecasting Using Machine Learning, Time Series, and Hybrid Models: A Case Study of Bitcoin Price Forecasting," Mathematics, MDPI, vol. 12(23), pages 1-15, November.
    2. Geng, Ru & Zhang, Hong-Kun & Gao, Yixian & Yuan, Gangnan, 2025. "Decoding global economic dynamic: A graph-based examination of contemporary ETF markets," Chaos, Solitons & Fractals, Elsevier, vol. 201(P3).
    3. Yaquelin Verenice Pantoja-Pacheco & Javier Yáñez-Mendiola, 2024. "Method for the Statistical Analysis of the Signals Generated by an Acquisition Card for Pulse Measurement," Mathematics, MDPI, vol. 12(6), pages 1-24, March.

  7. Apostolos Ampountolas, 2023. "The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility: A Two-Stage DCC-EGARCH Model Analysis," JRFM, MDPI, vol. 16(1), pages 1-17, January.

    Cited by:

    1. Otabek Sattarov & Fazliddin Makhmudov, 2025. "Risk-Aware Crypto Price Prediction Using DQN with Volatility-Adjusted Rewards Across Multi-Period State Representations," Mathematics, MDPI, vol. 13(18), pages 1-29, September.
    2. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.
    3. Alberto Manelli & Roberta Pace & Maria Leone, 2023. "The Financial Derivatives Market and the Pandemic: BioNTech and Moderna Volatility," JRFM, MDPI, vol. 16(10), pages 1-13, September.
    4. Anas Eisa Abdelkreem Mohammed & Henry Mwambi & Bernard Omolo, 2024. "Time-Varying Correlations between JSE.JO Stock Market and Its Partners Using Symmetric and Asymmetric Dynamic Conditional Correlation Models," Stats, MDPI, vol. 7(3), pages 1-16, July.
    5. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.
    6. Mohamed M. Sraieb & Shahnawaz Muhammed & Vladimir Dženopoljac & Samet Gunay, 2025. "Determinants of Russia’s probability of default: evidence from domestic and global indicators," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 49(3), pages 854-882, September.
    7. Mukul Bhatnagar & Sanjay Taneja & Ramona Rupeika-Apoga, 2023. "Demystifying the Effect of the News (Shocks) on Crypto Market Volatility," JRFM, MDPI, vol. 16(2), pages 1-16, February.

  8. Apostolos Ampountolas, 2022. "Cryptocurrencies Intraday High-Frequency Volatility Spillover Effects Using Univariate and Multivariate GARCH Models," IJFS, MDPI, vol. 10(3), pages 1-22, July.

    Cited by:

    1. Palomba, Giulio & Tedeschi, Marco, 2024. "Contagion among European financial indices, evidence from a quantile VAR approach," Economic Systems, Elsevier, vol. 48(2).
    2. Parthajit Kayal & Sumanjay Dutta, 2024. "Regime switching and causal network analysis of cryptocurrency volatility: evidence from pre-COVID and post-COVID analysis," Digital Finance, Springer, vol. 6(2), pages 319-340, June.
    3. Franco, João Pedro M. & Laurini, Márcio P., 2025. "Quantifying systemic risk in cryptocurrency markets: A high-frequency approach," International Review of Economics & Finance, Elsevier, vol. 102(C).
    4. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.
    5. Queiroz, R.G.S. & Kristoufek, L. & David, S.A., 2024. "A combined framework to explore cryptocurrency volatility and dependence using multivariate GARCH and Copula modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 652(C).
    6. Galati, Luca & Capalbo, Francesco, 2024. "Silicon Valley Bank bankruptcy and Stablecoins stability," International Review of Financial Analysis, Elsevier, vol. 91(C).
    7. Kudbeddin Şeker & Ethem Kiliç, 2026. "Bitcoin, U.S. stock markets, and volatility: the interaction of digital assets with traditional markets," Digital Finance, Springer, vol. 8(1), pages 1-25, March.
    8. Galati, Luca & Webb, Alexander & Webb, Robert I., 2024. "Financial contagion in cryptocurrency exchanges: Evidence from the FTT collapse," Finance Research Letters, Elsevier, vol. 67(PA).
    9. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.
    10. Apostolos Ampountolas, 2023. "The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility -- A Two-Stage DCC-EGARCH Model Analysis," Papers 2307.09137, arXiv.org.
    11. Riccardo Blasis & Luca Galati & Alexander Webb & Robert I. Webb, 2023. "Intelligent design: stablecoins (in)stability and collateral during market turbulence," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    12. Alessio Brini & Jimmie Lenz, 2024. "A comparison of cryptocurrency volatility-benchmarking new and mature asset classes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-38, December.
    13. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.
    14. Suleiman Dahir Mohamed & Mohd Tahir Ismail & Majid Khan Bin Majahar Ali, 2025. "Improving and evaluating GARCH-type models for Bitcoin volatility prediction," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 15(4), pages 1219-1260, December.

  9. Apostolos Ampountolas, 2021. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models," Forecasting, MDPI, vol. 3(3), pages 1-16, August.

    Cited by:

    1. Apostolos Ampountolas, 2025. "Predicting hotel booking cancellations: a comprehensive machine learning approach," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(6), pages 539-550, December.
    2. Badri Toppur & T. C. Thomas, 2023. "Forecasting Commercial Vehicle Production Using Quantitative Techniques," Contemporary Economics, Vizja University, vol. 17(1), March.
    3. Keerti Manisha & Inderpal Singh, 2024. "Forecasting of Indian and foreign tourist arrivals to Himachal Pradesh using Decomposition, Box–Jenkins, and Holt–Winters exponential smoothing methods," Asia-Pacific Journal of Regional Science, Springer, vol. 8(3), pages 879-909, September.
    4. Juan Gabriel Brida & Martín Olivera & Manuela Pulina, 2026. "Determinants of visitors’ flow in Uruguay: A SARIMAX approach," Tourism Economics, , vol. 32(1), pages 105-128, February.
    5. Jorge V Pérez-Rodríguez & Juan M Hernández & Julián Andrada-Félix, 2024. "Modelling prices and volatilities in the sharing economy," Tourism Economics, , vol. 30(5), pages 1189-1215, August.
    6. Apostolos Ampountolas & Mark Legg, 2024. "Predicting daily hotel occupancy: a practical application for independent hotels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(3), pages 197-205, June.
    7. Jordi Grau-Escolano & Salvador Anton Clavé & Joan Borràs, 2026. "Daily tourism demand forecasting via card transactions: a multi-source, interpretable, framework for diverse destinations and markets," Information Technology & Tourism, Springer, vol. 28(1), pages 1-29, June.
    8. Apostolos Ampountolas, 2025. "Addressing complex seasonal patterns in hotel forecasting: a comparative study," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(2), pages 143-152, April.
    9. Günal Bilek, 2025. "Modeling Tourism Demand in Turkey (2008–2024): Time-Series Approaches for Sustainable Growth," Sustainability, MDPI, vol. 17(4), pages 1-19, February.
    10. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    11. Dong Zhang & Chong Wu, 2023. "What online review features really matter? An explainable deep learning approach for hotel demand forecasting," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(9), pages 1100-1117, September.
    12. Mokhtar Jlidi & Oscar Barambones & Faiçal Hamidi & Mohamed Aoun, 2024. "ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC," Energies, MDPI, vol. 17(12), pages 1-21, June.
    13. Apostolos Ampountolas, 2023. "The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility -- A Two-Stage DCC-EGARCH Model Analysis," Papers 2307.09137, arXiv.org.
    14. Apostolos Ampountolas & Mark Legg, 2026. "A comprehensive approach to enhancing short-term hotel cancellation forecasts through dynamic machine learning models," Tourism Economics, , vol. 32(2), pages 321-341, March.
    15. Ivanka Vasenska, 2025. "Comparative Analysis of Machine Learning and Deep Learning Models for Tourism Demand Forecasting with Economic Indicators," FinTech, MDPI, vol. 4(3), pages 1-22, September.
    16. Vyom Shah & Nishil Patel & Dhruvin Shah & Debabrata Swain & Manorama Mohanty & Biswaranjan Acharya & Vassilis C. Gerogiannis & Andreas Kanavos, 2024. "Forecasting Maximum Temperature Trends with SARIMAX: A Case Study from Ahmedabad, India," Sustainability, MDPI, vol. 16(16), pages 1-21, August.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BIG: Big Data (1) 2023-08-21. Author is listed
  2. NEP-CMP: Computational Economics (1) 2023-08-21. Author is listed
  3. NEP-FMK: Financial Markets (1) 2023-08-21. Author is listed
  4. NEP-FOR: Forecasting (1) 2023-08-21. Author is listed
  5. NEP-RMG: Risk Management (1) 2023-08-21. Author is listed

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Apostolos Ampountolas should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

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