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Murat Mazibaş
(MURAT MAZIBAS)

Personal Details

First Name:Murat
Middle Name:
Last Name:Mazibas
Suffix:
RePEc Short-ID:pma2044
[This author has chosen not to make the email address public]
1-3 Perth Road, DD1 4HN, Dundee, United Kingdom

Affiliation

School of Business
University of Dundee

Dundee, United Kingdom
http://www.dundee.ac.uk/business/
RePEc:edi:sbdunuk (more details at EDIRC)

Research output

as
Jump to: Articles Chapters

Articles

  1. Dooruj Rambaccussing & Murat Mazibas, 2020. "True versus Spurious Long Memory in Cryptocurrencies," JRFM, MDPI, vol. 13(9), pages 1-11, August.
  2. Murat Mazibaş & Yusuf Tuna, 2017. "Understanding the Recent Growth in Consumer Loans and Credit Cards in Emerging Markets: Evidence from Turkey," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(10), pages 2333-2346, October.
  3. Harris, Richard D.F. & Mazibas, Murat, 2013. "Dynamic hedge fund portfolio construction: A semi-parametric approach," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 139-149.
  4. Harris, Richard D.F. & Mazibas, Murat, 2010. "Dynamic hedge fund portfolio construction," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 351-357, December.
  5. Ünsal BAN & Murat MAZIBAŞ, 2009. "Banka başarısızlıklarının yapay sinir ağlarıylatahmini: Türk bankacılık sistemi üzerine karşılaştırmalı bir uygulama," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 24(282), pages 27-53.
  6. Murat MAZİBAŞ, 2003. "Operasyonel Risk Yönetimi Ve Türk Bankacılık Sistemi," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 18(203), pages 32-41.

Chapters

  1. Richard D. F. Harris & Murat Mazibas, 2012. "Factor-Based Hedge Fund Replication with Risk Constraints," Palgrave Macmillan Books, in: Greg N. Gregoriou & Maher Kooli (ed.), Hedge Fund Replication, chapter 3, pages 30-47, Palgrave Macmillan.

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.

Articles

  1. Dooruj Rambaccussing & Murat Mazibas, 2020. "True versus Spurious Long Memory in Cryptocurrencies," JRFM, MDPI, vol. 13(9), pages 1-11, August.

    Cited by:

    1. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    2. Walid Chkili, 2021. "Modeling Bitcoin price volatility: long memory vs Markov switching," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 433-448, September.
    3. Thanasis Stengos, 2021. "Recent Developments in Cryptocurrency Markets: Co-Movements, Spillovers and Forecasting," JRFM, MDPI, vol. 14(3), pages 1-3, February.
    4. Kerolly Kedma Felix do Nascimento & Fábio Sandro dos Santos & Jader Silva Jale & Silvio Fernando Alves Xavier Júnior & Tiago A. E. Ferreira, 2023. "Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1095-1114, March.
    5. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).

  2. Murat Mazibaş & Yusuf Tuna, 2017. "Understanding the Recent Growth in Consumer Loans and Credit Cards in Emerging Markets: Evidence from Turkey," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(10), pages 2333-2346, October.

    Cited by:

    1. Kim, Hyeongjun & Cho, Hoon & Ryu, Doojin, 2018. "An empirical study on credit card loan delinquency," Economic Systems, Elsevier, vol. 42(3), pages 437-449.
    2. Cesar Leandro, Julio & Botelho, Delane, 2022. "Consumer over-indebtedness: A review and future research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 535-551.
    3. Jale Minibas-Poussard & Haluk Baran Bingöl & Christine Roland-Levy & Erkin Diyarbakirlioglu & Tutku Seckin-Celik, 2020. "Money, banks, and savings: A comparative analysis of Turkish laypeople's social representations over five periods (1999-2017)," Post-Print hal-02919362, HAL.
    4. Nur Annisa Hasniawati & Eva R. Lase & Akhis R. Hutabarat, 2020. "Indonesian Household Payment Choice: A Nested Logit Analysis," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(special i), pages 291-313.

  3. Harris, Richard D.F. & Mazibas, Murat, 2013. "Dynamic hedge fund portfolio construction: A semi-parametric approach," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 139-149.

    Cited by:

    1. José Luis Miralles‐Quirós & María Mar Miralles‐Quirós & José Manuel Nogueira, 2019. "Diversification benefits of using exchange‐traded funds in compliance to the sustainable development goals," Business Strategy and the Environment, Wiley Blackwell, vol. 28(1), pages 244-255, January.
    2. León, Ángel & Moreno, Manuel, 2015. "Lower Partial Moments under Gram Charlier Distribution: Performance Measures and Efficient Frontiers," QM&ET Working Papers 15-3, University of Alicante, D. Quantitative Methods and Economic Theory.
    3. Massimiliano Kaucic & Roberto Daris, 2015. "Multi-Objective Stochastic Optimization Programs for a Non-Life Insurance Company under Solvency Constraints," Risks, MDPI, vol. 3(3), pages 1-30, September.
    4. Wenbo Wu & Jiaqi Chen & Zhibin (Ben) Yang & Michael L. Tindall, 2021. "A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection," Management Science, INFORMS, vol. 67(7), pages 4577-4601, July.
    5. Víctor Adame-García & Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero, 2017. "“Resolution of optimization problems and construction of efficient portfolios: An application to the Euro Stoxx 50 index"," IREA Working Papers 201702, University of Barcelona, Research Institute of Applied Economics, revised Feb 2017.
    6. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    7. Ricardo Laborda & Ramiro Losada, 2017. "Why is investors'mutual fund market allocation far from the optimum?," CNMV Working Papers CNMV Working Papers no. 6, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    8. Roumpis, Efthymios & Syriopoulos, Theodore, 2014. "Dynamics and risk factors in hedge funds returns: Implications for portfolio construction and performance evaluation," The Journal of Economic Asymmetries, Elsevier, vol. 11(C), pages 58-77.
    9. Miralles-Quirós, José Luis & Miralles-Quirós, María del Mar, 2017. "The Copula ADCC-GARCH model can help PIIGS to fly," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 1-12.
    10. Ñíguez, Trino-Manuel & Perote, Javier, 2016. "Multivariate moments expansion density: Application of the dynamic equicorrelation model," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 216-232.
    11. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2014. "Semi-nonparametric VaR forecasts for hedge funds during the recent crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 330-343.
    12. Kuang, Wei, 2021. "Which clean energy sectors are attractive? A portfolio diversification perspective," Energy Economics, Elsevier, vol. 104(C).
    13. Bedi, Prateek & Nashier, Tripti, 2020. "On the investment credentials of Bitcoin: A cross-currency perspective," Research in International Business and Finance, Elsevier, vol. 51(C).

  4. Harris, Richard D.F. & Mazibas, Murat, 2010. "Dynamic hedge fund portfolio construction," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 351-357, December.

    Cited by:

    1. Stafylas, Dimitrios & Andrikopoulos, Athanasios, 2020. "Determinants of hedge fund performance during ‘good’ and ‘bad’ economic periods," Research in International Business and Finance, Elsevier, vol. 52(C).
    2. Luo, Cuicui & Seco, Luis & Wu, Lin-Liang Bill, 2015. "Portfolio optimization in hedge funds by OGARCH and Markov Switching Model," Omega, Elsevier, vol. 57(PA), pages 34-39.
    3. Li, Chenlu & Li, Baibing & Tee, Kai-Hong, 2020. "Are hedge funds active market liquidity timers?," International Review of Financial Analysis, Elsevier, vol. 67(C).
    4. Harris, Richard D.F. & Mazibas, Murat, 2013. "Dynamic hedge fund portfolio construction: A semi-parametric approach," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 139-149.
    5. Stafylas, Dimitrios & Andrikopoulos, Athanasios & Tolikas, Konstantinos, 2023. "Hedge fund performance persistence under different business cycles and stock market regimes," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    6. Roumpis, Efthymios & Syriopoulos, Theodore, 2014. "Dynamics and risk factors in hedge funds returns: Implications for portfolio construction and performance evaluation," The Journal of Economic Asymmetries, Elsevier, vol. 11(C), pages 58-77.
    7. Stafylas, Dimitrios & Anderson, Keith & Uddin, Moshfique, 2018. "Hedge fund performance attribution under various market conditions," International Review of Financial Analysis, Elsevier, vol. 56(C), pages 221-237.

Chapters

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