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Jose Olmo

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. Calvo Pardo, Héctor & Olmo, Jose & Mancini, Tullio, 2021. "Machine Learning the Carbon Footprint of Bitcoin Mining," CEPR Discussion Papers 16267, C.E.P.R. Discussion Papers.

    Cited by:

    1. Yerushalmi, Erez & Paladini, Stefania, 2023. "Blockchain in Financial Intermediation and Beyond: What are the Main Barriers for Widespread Adoption?," CAFE Working Papers 22, Centre for Accountancy, Finance and Economics (CAFE), Birmingham City Business School, Birmingham City University.
    2. Nishant Sapra & Imlak Shaikh & Ashutosh Dash, 2023. "Impact of Proof of Work (PoW)-Based Blockchain Applications on the Environment: A Systematic Review and Research Agenda," JRFM, MDPI, vol. 16(4), pages 1-29, March.

  2. Gonzalo, Jesús & Olmo, José, 2016. "Long-term optimal portfolio allocation under dynamic horizon-specific risk aversion," UC3M Working papers. Economics 23599, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Jamel Boukhatem, 2021. "Sukuk Market and Economic Welfare Nexus: A Partial Equilibrium Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 11(3), pages 142-145.
    2. Jozef Barunik & Josef Kurka, 2021. "Risks of heterogeneously persistent higher moments," Papers 2104.04264, arXiv.org, revised Mar 2024.

  3. Jose Olmo & William Pouliot, 2014. "Tests to Disentangle Breaks in Intercept from Slope in Linear Regression Models with Application to Management Performance in the Mutual Fund Industry," Discussion Papers 14-02, Department of Economics, University of Birmingham.

    Cited by:

    1. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.

  4. Iori, G. & Kapar, B. & Olmo, J., 2012. "The Cross-Section of Interbank Rates: A Nonparametric Empirical Investigation," Working Papers 12/03, Department of Economics, City University London.

    Cited by:

    1. Vahidin Jeleskovic & Anastasios Demertzidis, 2018. "Comparing different methods for the estimation of interbank intraday yield curves," MAGKS Papers on Economics 201839, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Anastasios Demertzidis & Vahidin Jeleskovic, 2021. "Empirical Estimation of Intraday Yield Curves on the Italian Interbank Credit Market e-MID," JRFM, MDPI, vol. 14(5), pages 1-23, May.
    3. Annika Birch & Tomaso Aste, 2014. "Systemic Losses Due to Counter Party Risk in a Stylized Banking System," Papers 1402.3688, arXiv.org.
    4. Miguel Sarmiento & Jorge Cely & Carlos León, 2015. "Monitoring the Unsecured Interbank Funds Market," Borradores de Economia 917, Banco de la Republica de Colombia.
    5. Brossard, Olivier & Saroyan, Susanna, 2016. "Hoarding and short-squeezing in times of crisis: Evidence from the Euro overnight money market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 163-185.
    6. Markus Engler & Vahidin Jeleskovic, 2016. "Intraday volatility, trading volume and trading intensity in the interbank market e-MID," MAGKS Papers on Economics 201648, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. Olivier Brossard & Susanna Saroyan, 2016. "Hoarding and short-squeezing in times of crisis: Evidence from the Euro overnight money market," Post-Print hal-01293693, HAL.

  5. Rafael González-Val & Jose Olmo, 2011. "Growth in a cross-section of cities: location, increasing returns or random growth?," Working Papers 2011/39, Institut d'Economia de Barcelona (IEB).

    Cited by:

    1. Catalina Bolancé & Zuhair Bahraoui & Ramon Alemany, 2015. "Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches," Working Papers XREAP2015-01, Xarxa de Referència en Economia Aplicada (XREAP), revised Jan 2015.
    2. Esther Vayá & José Ramón García & Joaquim Murillo & Javier Romaní & Jordi Suriñach, 2016. "“Economic Impact of Cruise Activity: The Port of Barcelona”," AQR Working Papers 201609, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2016.
    3. Mercedes Ayuso & Montserrat Guillén & Jens Perch Nielsen, 2016. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Working Papers XREAP2016-08, Xarxa de Referència en Economia Aplicada (XREAP), revised Dec 2016.
    4. Anna Castañer & Mª Mercè Claramunt & Alba Tadeo & Javier Varea, 2016. "Modelización de la dependencia del número de siniestros. Aplicación a Solvencia II," Working Papers XREAP2016-01, Xarxa de Referència en Economia Aplicada (XREAP), revised Sep 2016.
    5. Anna Castañer & Mª Mercè Claramunt, 2014. "Optimal stop-loss reinsurance: a dependence analysis," Working Papers XREAP2014-04, Xarxa de Referència en Economia Aplicada (XREAP), revised Apr 2014.

  6. Kapar, B. & Olmo, J., 2011. "The determinants of credit default swap spreads in the presence of structural breaks and counterparty risk," Working Papers 11/02, Department of Economics, City University London.

    Cited by:

    1. Shahzad, Syed Jawad Hussain & Nor, Safwan Mohd & Kumar, Ronald Ravinesh & Mensi, Walid, 2017. "Interdependence and contagion among industry-level US credit markets: An application of wavelet and VMD based copula approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 310-324.
    2. Syed Jawad Hussain Shahzad & Safwan Mohd Nor & Nur Azura Sanusi & Ronald Ravinesh Kumar, 2018. "The Determinants of Credit Risk: Analysis of US Industry-level Indices," Global Business Review, International Management Institute, vol. 19(5), pages 1152-1165, October.
    3. Bratis, Theodoros & Laopodis, Nikiforos T. & Kouretas, Georgios P., 2020. "Systemic risk and financial stability dynamics during the Eurozone debt crisis," Journal of Financial Stability, Elsevier, vol. 47(C).
    4. Miroslav Mateev & Elena Marinova, 2019. "Relation between Credit Default Swap Spreads and Stock Prices: A Non-linear Perspective," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(1), pages 1-26, January.

  7. Gonzalo, Jesús & Olmo, José, 2010. "Conditional stochastic dominance tests in dynamic settings," UC3M Working papers. Economics we1029, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Linton, Oliver & Seo, Myung Hwan & Whang, Yoon-Jae, 2023. "Testing stochastic dominance with many conditioning variables," Journal of Econometrics, Elsevier, vol. 235(2), pages 507-527.
    2. E. Agliardi & M. Pinar & T. Stengos, 2014. "Assessing temporal trends and industry contributions to air and water pollution using stochastic dominance," Working Papers wp981, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Stelios Arvanitis & O. Scaillet & Nikolas Topaloglou, 2018. "Spanning Tests for Markowitz Stochastic Dominance," Swiss Finance Institute Research Paper Series 18-08, Swiss Finance Institute.
    4. Olmo, José & Sanso-Navarro, Marcos, 2012. "Forecasting the performance of hedge fund styles," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2351-2365.
    5. Mehmet Pinar & Thanasis Stengos & Nikolas Topaloglou, 2022. "Stochastic dominance spanning and augmenting the human development index with institutional quality," Annals of Operations Research, Springer, vol. 315(1), pages 341-369, August.
    6. Agliardi, Elettra & Agliardi, Rossella & Pinar, Mehmet & Stengos, Thanasis & Topaloglou, Nikolas, 2012. "A new country risk index for emerging markets: A stochastic dominance approach," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 741-761.

  8. González-Val, Rafael & Olmo, Jose, 2010. "A Statistical Test of City Growth: Location, Increasing Returns and Random Growth," MPRA Paper 27139, University Library of Munich, Germany.

    Cited by:

    1. Chen, Zhihong & Fu, Shihe & Zhang, Dayong, 2010. "Searching for the parallel growth of cities," MPRA Paper 21528, University Library of Munich, Germany.

  9. Pouliot, W. & Olmo, J., 2008. "U-statistic Type Tests for Structural Breaks in Linear Regression Models," Working Papers 08/15, Department of Economics, City University London.

    Cited by:

    1. Olmo, Jose & Pilbeam, Keith & Pouliot, William, 2011. "Detecting the presence of insider trading via structural break tests," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2820-2828, November.
    2. Olmo, J. & Pilbeam, K. & Pouliot, W., 2009. "Detecting the Presence of Informed Price Trading Via Structural Break Tests," Working Papers 1580, Department of Economics, City University London.

  10. Olmo, J. & Pouliot, W., 2008. "Early Detection Techniques for Market Risk Failure," Working Papers 08/09, Department of Economics, City University London.

    Cited by:

    1. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.
    2. Pouliot, W. & Olmo, J., 2008. "U-statistic Type Tests for Structural Breaks in Linear Regression Models," Working Papers 08/15, Department of Economics, City University London.

  11. Martinez, O. & Olmo, J., 2008. "A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences," Working Papers 08/08, Department of Economics, City University London.

    Cited by:

  12. Escanciano, J. C. & Olmo, J., 2007. "Estimation risk effects on backtesting for parametric value-at-risk models," Working Papers 07/11, Department of Economics, City University London.

    Cited by:

    1. Bontemps, Christian, 2014. "Simple moment-based tests for value-at-risk models and discrete distribution," TSE Working Papers 14-535, Toulouse School of Economics (TSE).
    2. Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2008. "Backtesting Value-at-Risk : A GMM Duration-based Test," Post-Print halshs-00363165, HAL.
    3. Bontemps, Christian, 2013. "Moment-Based Tests for Discrete Distributions," IDEI Working Papers 772, Institut d'Économie Industrielle (IDEI), Toulouse, revised Oct 2014.
    4. Evers, Corinna & Rohde, Johannes, 2014. "Model Risk in Backtesting Risk Measures," Hannover Economic Papers (HEP) dp-529, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

  13. Juan Carlos Escanciano & Jose Olmo, 2007. "Backtesting Parametric Value-at-Risk with Estimation Risk," CAEPR Working Papers 2007-005, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington, revised Sep 2008.

    Cited by:

    1. Juan Carlos Escanciano & Zaichao Du, 2015. "Backtesting Expected Shortfall: Accounting for Tail Risk," CAEPR Working Papers 2015-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    2. Kimera Naradh & Retius Chifurira & Knowledge Chinhamu, 2022. "Analysis of stock exchange risk and currency in South African Financial Markets using stable parameter estimation," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 11(1), pages 120-131, January.
    3. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    4. Mohamed El Ghourabi & Christian Francq & Fedya Telmoudi, 2016. "Consistent Estimation of the Value at Risk When the Error Distribution of the Volatility Model is Misspecified," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 46-76, January.
    5. Wied, Dominik & Weiß, Gregor N.F. & Ziggel, Daniel, 2016. "Evaluating Value-at-Risk forecasts: A new set of multivariate backtests," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 121-132.
    6. Denisa Banulescu & Christophe Hurlin & Jeremy Leymarie & O. Scaillet, 2019. "Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures," Swiss Finance Institute Research Paper Series 19-48, Swiss Finance Institute.
    7. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 499-512, October.
    8. Francq, Christian & Zakoian, Jean-Michel, 2015. "Looking for efficient qml estimation of conditional value-at-risk at multiple risk levels," MPRA Paper 67195, University Library of Munich, Germany.
    9. Zolotko, Mikhail & Okhrin, Ostap, 2014. "Modelling the general dependence between commodity forward curves," Energy Economics, Elsevier, vol. 43(C), pages 284-296.
    10. Sander Barendse & Erik Kole & Dick van Dijk, 2023. "Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 528-568.
    11. Lönnbark, Carl, 2013. "On the role of the estimation error in prediction of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 847-853.
    12. Emese Lazar & Ning Zhang, 2017. "Model Risk of Expected Shortfall," ICMA Centre Discussion Papers in Finance icma-dp2017-10, Henley Business School, University of Reading.
    13. Christophe Boucher & Jon Danielsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk models-at-risk," Post-Print hal-02312332, HAL.
    14. Michael B. Gordy & Alexander J. McNeil, 2017. "Spectral backtests of forecast distributions with application to risk management," Papers 1708.01489, arXiv.org, revised Jul 2019.
    15. Mateusz Buczyński & Marcin Chlebus, 2021. "GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks," Working Papers 2021-08, Faculty of Economic Sciences, University of Warsaw.
    16. Dimitriadis, Timo & Liu, Xiaochun & Schnaitmann, Julie, 2020. "Encompassing tests for value at risk and expected shortfall multi-step forecasts based on inference on the boundary," Hohenheim Discussion Papers in Business, Economics and Social Sciences 11-2020, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    17. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, January.
    18. Wang, Zheqi & Crook, Jonathan & Andreeva, Galina, 2020. "Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default," European Journal of Operational Research, Elsevier, vol. 287(2), pages 725-738.
    19. Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2008. "Backtesting Value-at-Risk : A GMM Duration-based Test," Post-Print halshs-00363165, HAL.
    20. Argyropoulos, Christos & Panopoulou, Ekaterini, 2019. "Backtesting VaR and ES under the magnifying glass," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 22-37.
    21. Frédérique Bec, 2015. "Cyclicality and term structure of Value-at-Risk within a threshold autoregression setup," Post-Print hal-02980012, HAL.
    22. Daniel Rösch & Harald Scheule, 2014. "Forecasting Mortgage Securitization Risk Under Systematic Risk and Parameter Uncertainty," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(3), pages 563-586, September.
    23. Sylvain Benoit & Christophe Hurlin & Christophe Perignon, 2015. "Implied Risk Exposures," Review of Finance, European Finance Association, vol. 19(6), pages 2183-2222.
    24. Elena-Ivona DUMITRESCU, 2011. "Backesting Value-at-Risk: From DQ (Dynamic Quantile) to DB (Dynamic Binary) Tests," LEO Working Papers / DR LEO 262, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    25. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    26. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org, revised Oct 2019.
    27. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2020. "Forecasting value at risk with intra-day return curves," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1023-1038.
    28. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    29. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.
    30. Juan Carlos Escanciano & Pei Pei, 2012. "Pitfalls in Backtesting Historical Simulation VaR Models," CAEPR Working Papers 2012-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    31. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    32. Zaichao Du & Juan Carlos Escanciano, 2015. "A Nonparametric Distribution-Free Test for Serial Independence of Errors," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1011-1034, December.
    33. León, Ángel & Ñíguez, Trino-Manuel, 2020. "Modeling asset returns under time-varying semi-nonparametric distributions," Journal of Banking & Finance, Elsevier, vol. 118(C).
    34. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    35. León, Ángel & Ñíguez, Trino-Manuel, 2021. "The transformed Gram Charlier distribution: Parametric properties and financial risk applications," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 323-349.
    36. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
    37. Elena-Ivona Dumitrescu & Christophe Hurlin & Vinson Pham, 2012. "Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests," Working Papers halshs-00671658, HAL.
    38. Zaichao Du & Juan Carlos Escanciano & Guangwei Zhu, 2017. "Automatic Portmanteau Tests with Applications to Market Risk Management," CAEPR Working Papers 2017-002, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    39. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    40. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
    41. Christian Gouriéroux & Jean-Michel Zakoian, 2012. "Estimation Adjusted VaR," Working Papers 2012-16, Center for Research in Economics and Statistics.
    42. Francq, Christian & Zakoian, Jean-Michel, 2015. "Joint inference on market and estimation risks in dynamic portfolios," MPRA Paper 68100, University Library of Munich, Germany.
    43. Benjamin Mögel & Benjamin R. Auer, 2018. "How accurate are modern Value-at-Risk estimators derived from extreme value theory?," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 979-1030, May.
    44. D. Th. Vezeris & C. J. Schinas & Th. S. Kyrgos & V. A. Bizergianidou & I. P. Karkanis, 2020. "Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 975-1054, December.
    45. Taylor, James W., 2020. "Forecast combinations for value at risk and expected shortfall," International Journal of Forecasting, Elsevier, vol. 36(2), pages 428-441.
    46. Yannick Hoga & Matei Demetrescu, 2023. "Monitoring Value-at-Risk and Expected Shortfall Forecasts," Management Science, INFORMS, vol. 69(5), pages 2954-2971, May.
    47. Evers, Corinna & Rohde, Johannes, 2014. "Model Risk in Backtesting Risk Measures," Hannover Economic Papers (HEP) dp-529, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    48. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    49. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.

  14. Gonzalo, Jesús & Olmo, José, 2005. "Contagion versus flight to quality in financial markets," UC3M Working papers. Economics we051810, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Felices, Guillermo & Grisse, Christian & Yang, Jing, 2009. "International financial transmission: emerging and mature markets," Bank of England working papers 373, Bank of England.
    2. Jammazi, Rania & Tiwari, Aviral Kr. & Ferrer, Román & Moya, Pablo, 2015. "Time-varying dependence between stock and government bond returns: International evidence with dynamic copulas," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 74-93.
    3. Soylu, Pınar Kaya & Güloğlu, Bülent, 2019. "Financial contagion and flight to quality between emerging markets and U.S. bond market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    4. Withanage, Yeshan & Jayasinghe, Prabhath, 2017. "Volatility Spillovers between South Asian Stock Markets: Evidence from Sri Lanka, India and Pakistan," MPRA Paper 82782, University Library of Munich, Germany, revised Nov 2017.
    5. Baur, Dirk G. & Lucey, Brian M., 2009. "Flights and contagion--An empirical analysis of stock-bond correlations," Journal of Financial Stability, Elsevier, vol. 5(4), pages 339-352, December.
    6. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2017. "Specification Testing in Hawkes Models," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 139-171.
    7. Paulo Horta & Carlos Mendes & Isabel Vieira, 2008. "Contagion effects of the US Subprime Crisis on Developed Countries," CEFAGE-UE Working Papers 2008_08, University of Evora, CEFAGE-UE (Portugal).
    8. Silvapulle, Param & Fenech, Jean Pierre & Thomas, Alice & Brooks, Rob, 2016. "Determinants of sovereign bond yield spreads and contagion in the peripheral EU countries," Economic Modelling, Elsevier, vol. 58(C), pages 83-92.
    9. Craig S. Hakkio & William R. Keeton, 2009. "Financial stress: what is it, how can it be measured, and why does it matter?," Economic Review, Federal Reserve Bank of Kansas City, vol. 94(Q II), pages 5-50.
    10. Dirk G. Baur, 2007. "Stock-bond co-movements and cross-country linkages," The Institute for International Integration Studies Discussion Paper Series iiisdp216, IIIS.
    11. Robert B. Durand & Markus Junker & Alex Szimayer, 2010. "The flight‐to‐quality effect: a copula‐based analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 50(2), pages 281-299, June.
    12. Kallenberg, Wilbert C.M., 2008. "Modelling dependence," Insurance: Mathematics and Economics, Elsevier, vol. 42(1), pages 127-146, February.
    13. Dirk G. Baur & Brian M. Lucey, 2007. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Institute for International Integration Studies Discussion Paper Series iiisdp198, IIIS.
    14. Guidolin, Massimo & Hansen, Erwin & Pedio, Manuela, 2019. "Cross-asset contagion in the financial crisis: A Bayesian time-varying parameter approach," Journal of Financial Markets, Elsevier, vol. 45(C), pages 83-114.
    15. Paulo Horta & Sérgio Lagoa & Luís Martins, 2016. "Unveiling investor-induced channels of financial contagion in the 2008 financial crisis using copulas," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 625-637, April.
    16. Grillini, Stefano & Ozkan, Aydin & Sharma, Abhijit, 2022. "Static and dynamic liquidity spillovers in the Eurozone: The role of financial contagion and the Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).
    17. Selcuk Bayraci & Sercan Demiralay & Hatice Gaye Gencer, 2018. "Stock†Bond Co†Movements And Flight†To†Quality In G7 Countries: A Time†Frequency Analysis," Bulletin of Economic Research, Wiley Blackwell, vol. 70(1), pages 29-49, January.
    18. Ponrajah, Jeremey & Ning, Cathy, 2023. "Stock–bond dependence and flight to/from quality," International Review of Financial Analysis, Elsevier, vol. 86(C).
    19. Chiu-Lan Chang & Paul L. Hsueh, 2013. "An Investigation of the Flight-to-Quality Effect: Evidence from Asia-Pacific Countries," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S4), pages 53-69, September.

  15. Olmo, José, 2005. "Testing the existence of clustering in the extreme values," UC3M Working papers. Economics we051809, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Hautsch, Nikolaus & Herrera, Rodrigo, 2015. "Multivariate dynamic intensity peaks-over-threshold models," CFS Working Paper Series 516, Center for Financial Studies (CFS).

  16. Jose Olmo & Jesus Gonzalo, 2004. "Which Extreme Values are Really Extremes?," Econometric Society 2004 North American Winter Meetings 144, Econometric Society.

    Cited by:

    1. Ana-Maria Gavril, 2009. "Exchange Rate Risk: Heads or Tails," Advances in Economic and Financial Research - DOFIN Working Paper Series 35, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    2. Alfonso Novales & Laura Garcia-Jorcano, 2019. "Backtesting Extreme Value Theory models of expected shortfall," Documentos de Trabajo del ICAE 2019-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    3. Koopman, Siem Jan & Shephard, Neil & Creal, Drew, 2009. "Testing the assumptions behind importance sampling," Journal of Econometrics, Elsevier, vol. 149(1), pages 2-11, April.
    4. Wendy Shinyie & Noriszura Ismail & Abdul Jemain, 2013. "Semi-parametric Estimation for Selecting Optimal Threshold of Extreme Rainfall Events," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2325-2352, May.
    5. Amélie Charles & Olivier Darné, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Post-Print hal-01122507, HAL.
    6. Olmo, J., 2009. "Extreme Value Theory Filtering Techniques for Outlier Detection," Working Papers 09/09, Department of Economics, City University London.
    7. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    8. Jalal, Amine & Rockinger, Michael, 2008. "Predicting tail-related risk measures: The consequences of using GARCH filters for non-GARCH data," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 868-877, December.
    9. Schluter, Christian & Trede, Mark, 2008. "Identifying multiple outliers in heavy-tailed distributions with an application to market crashes," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 700-713, September.
    10. Loriano Mancini & Fabio Trojani, 2011. "Robust Value at Risk Prediction," Journal of Financial Econometrics, Oxford University Press, vol. 9(2), pages 281-313, Spring.
    11. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2017. "Exploiting Spillovers to Forecast Crashes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(8), pages 936-955, December.
    12. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    13. Nikola Radivojevic & Milena Cvjetkovic & Saša Stepanov, 2016. "The new hybrid value at risk approach based on the extreme value theory," Estudios de Economia, University of Chile, Department of Economics, vol. 43(1 Year 20), pages 29-52, June.
    14. Bekiros, Stelios D. & Georgoutsos, Dimitris A., 2008. "The extreme-value dependence of Asia-Pacific equity markets," Journal of Multinational Financial Management, Elsevier, vol. 18(3), pages 197-208, July.
    15. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".

Articles

  1. Jose Olmo, 2023. "A nonparametric predictive regression model using partitioning estimators based on Taylor expansions," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 294-318, May.

    Cited by:

    1. Christis Katsouris, 2024. "Robust Estimation in Network Vector Autoregression with Nonstationary Regressors," Papers 2401.04050, arXiv.org.

  2. Richard J. McGee & Jose Olmo, 2022. "Optimal characteristic portfolios," Quantitative Finance, Taylor & Francis Journals, vol. 22(10), pages 1853-1870, October.

    Cited by:

    1. Auh, Jun Kyung & Cho, Wonho, 2023. "Factor-based portfolio optimization," Economics Letters, Elsevier, vol. 228(C).

  3. Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2022. "Machine Learning the Carbon Footprint of Bitcoin Mining," JRFM, MDPI, vol. 15(2), pages 1-30, February.
    See citations under working paper version above.
  4. Burcu Kapar & Jose Olmo, 2021. "Analysis of Bitcoin prices using market and sentiment variables," The World Economy, Wiley Blackwell, vol. 44(1), pages 45-63, January.

    Cited by:

    1. Peter Fratrič & Giovanni Sileno & Sander Klous & Tom Engers, 2022. "Manipulation of the Bitcoin market: an agent-based study," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-29, December.
    2. Balcilar, Mehmet & Ozdemir, Huseyin & Agan, Busra, 2022. "Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Bhuiyan, Rubaiyat Ahsan & Husain, Afzol & Zhang, Changyong, 2021. "A wavelet approach for causal relationship between bitcoin and conventional asset classes," Resources Policy, Elsevier, vol. 71(C).
    4. 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).
    5. Adel Benhamed & Ahlem Selma Messai & Ghassen El Montasser, 2023. "On the Determinants of Bitcoin Returns and Volatility: What We Get from Gets?," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    6. Joseph J. French, 2021. "#Bitcoin, #COVID-19: Twitter-Based Uncertainty and Bitcoin Before and during the Pandemic," IJFS, MDPI, vol. 9(2), pages 1-7, May.
    7. A. V. Biju & Aparna Merin Mathew & P. P. Nithi Krishna & M. P. Akhil, 2022. "Is the future of bitcoin safe? A triangulation approach in the reality of BTC market through a sentiments analysis," Digital Finance, Springer, vol. 4(4), pages 275-290, December.
    8. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    9. Dibooglu, Sel & Cevik, Emrah I. & Gillman, Max, 2022. "Gold, silver, and the US dollar as harbingers of financial calm and distress," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 200-210.
    10. Dias, Ishanka K. & Fernando, J.M. Ruwani & Fernando, P. Narada D., 2022. "Does investor sentiment predict bitcoin return and volatility? A quantile regression approach," International Review of Financial Analysis, Elsevier, vol. 84(C).

  5. Maria Kyriacou & Jose Olmo & Marius Strittmatter, 2021. "Optimal portfolio allocation using option‐implied information," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(2), pages 266-285, February.

    Cited by:

    1. Massimo Guidolin & Kai Wang, 2022. "The Empirical Performance of Option Implied Volatility Surface-Driven Optimal Portfolios," BAFFI CAREFIN Working Papers 22190, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

  6. Ricardo Laborda & Jose Olmo, 2021. "An Empirical Analysis of Terrorism and Stock Market Spillovers: The Case of Spain," Defence and Peace Economics, Taylor & Francis Journals, vol. 32(1), pages 68-86, January.

    Cited by:

    1. Bouri, Elie & Hammoud, Rami & Kassm, Christina Abou, 2023. "The effect of oil implied volatility and geopolitical risk on GCC stock sectors under various market conditions," Energy Economics, Elsevier, vol. 120(C).

  7. Laborda, Ricardo & Olmo, Jose, 2021. "Volatility spillover between economic sectors in financial crisis prediction: Evidence spanning the great financial crisis and Covid-19 pandemic," Research in International Business and Finance, Elsevier, vol. 57(C).

    Cited by:

    1. Jeonghwa Cha & Kyungbo Park & Hangook Kim & Jongyi Hong, 2023. "Crisis Index Prediction Based on Momentum Theory and Earnings Downside Risk Theory: Focusing on South Korea’s Energy Industry," Energies, MDPI, vol. 16(5), pages 1-20, February.
    2. Md. Bokhtiar Hasan & Masnun Mahi & Tapan Sarker & Md. Ruhul Amin, 2021. "Spillovers of the COVID-19 Pandemic: Impact on Global Economic Activity, the Stock Market, and the Energy Sector," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    3. Hasan Fehmi Baklaci & Tezer Yelkenci, 2022. "Cross-time-frequency analysis of volatility linkages in global currency markets: an extended framework," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(2), pages 267-314, June.
    4. Pham, Son Duy & Nguyen, Thao Thac Thanh & Do, Hung Xuan & Vo, Xuan Vinh, 2023. "Portfolio diversification during the COVID-19 pandemic: Do vaccinations matter?," Journal of Financial Stability, Elsevier, vol. 65(C).
    5. Tabak, Benjamin Miranda & Silva, Igor Bettanin Dalla Riva e & Silva, Thiago Christiano, 2022. "Analysis of connectivity between the world’s banking markets: The COVID-19 global pandemic shock," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 324-336.
    6. Ahmad, Wasim & Hernandez, Jose Arreola & Saini, Seema & Mishra, Ritesh Kumar, 2021. "The US equity sectors, implied volatilities, and COVID-19: What does the spillover analysis reveal?," Resources Policy, Elsevier, vol. 72(C).
    7. Constantin Anghelache & Madalina Gabriela Anghel & Stefan Virgil Iacob, 2022. "The Social - Economic State Of Romania Under The Impact Of Crisis," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 29-39, August.
    8. Mensi, Walid & Vo, Xuan Vinh & Ko, Hee-Un & Kang, Sang Hoon, 2023. "Frequency spillovers between green bonds, global factors and stock market before and during COVID-19 crisis," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 558-580.
    9. Gofran, Ruhana Zareen & Gregoriou, Andros & Haar, Lawrence, 2022. "Impact of Coronavirus on liquidity in financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    10. Bouteska, Ahmed & Hajek, Petr & Fisher, Ben & Abedin, Mohammad Zoynul, 2023. "Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network," Research in International Business and Finance, Elsevier, vol. 64(C).
    11. Jana, Rabin K & Ghosh, Indranil & Goyal, Vinay, 2022. "Spillover nexus of financial stress during black Swan events," Finance Research Letters, Elsevier, vol. 48(C).
    12. Hasan, Md. Bokhtiar & Mahi, Masnun & Hassan, M. Kabir & Bhuiyan, Abul Bashar, 2021. "Impact of COVID-19 pandemic on stock markets: Conventional vs. Islamic indices using wavelet-based multi-timescales analysis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    13. Dai, Zhifeng & Peng, Yongxin, 2022. "Economic policy uncertainty and stock market sector time-varying spillover effect: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    14. Tam Hoang-Nhat Dang & Nhan Thien Nguyen & Duc Hong Vo, 2023. "Sectoral volatility spillovers and their determinants in Vietnam," Economic Change and Restructuring, Springer, vol. 56(1), pages 681-700, February.
    15. Dorota Zebrowska-Suchodolska & Andrzej Karpio & Krzysztof Kompa, 2021. "COVID-19 Pandemic: Stock Markets Situation in European Ex-Communist Countries," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 1106-1128.
    16. Thai Hung, Ngo & Nguyen, Linh Thi My & Vinh Vo, Xuan, 2022. "Exchange rate volatility connectedness during Covid-19 outbreak: DECO-GARCH and Transfer Entropy approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    17. Sharma, Gagan Deep & Shahbaz, Muhammad & Singh, Sanjeet & Chopra, Ritika & Cifuentes-Faura, Javier, 2023. "Investigating the nexus between green economy, sustainability, bitcoin and oil prices: Contextual evidence from the United States," Resources Policy, Elsevier, vol. 80(C).
    18. Choi, Sun-Yong, 2022. "Dynamic volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    19. Hernandez, Jose Arreola & Shahzad, Syed Jawad Hussain & Sadorsky, Perry & Uddin, Gazi Salah & Bouri, Elie & Kang, Sang Hoon, 2022. "Regime specific spillovers across US sectors and the role of oil price volatility," Energy Economics, Elsevier, vol. 107(C).
    20. Umar, Zaghum & Polat, Onur & Choi, Sun-Yong & Teplova, Tamara, 2022. "Dynamic connectedness between non-fungible tokens, decentralized finance, and conventional financial assets in a time-frequency framework," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    21. Al-Nassar, Nassar S. & Yousaf, Imran & Makram, Beljid, 2023. "Spillovers between positively and negatively affected service sectors from the COVID-19 health crisis: Implications for portfolio management," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    22. Zhang, Yi & Zhou, Long & Chen, Yajiao & Liu, Fang, 2022. "The contagion effect of jump risk across Asian stock markets during the Covid-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).

  8. Jose Olmo, 2021. "Optimal portfolio allocation and asset centrality revisited," Quantitative Finance, Taylor & Francis Journals, vol. 21(9), pages 1475-1490, September.

    Cited by:

    1. Christis Katsouris, 2023. "Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models," Papers 2305.11282, arXiv.org, revised Jul 2023.
    2. Jilber Urbina & Miguel Santolino & Montserrat Guillen, 2021. "Covariance Principle for Capital Allocation: A Time-Varying Approach," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    3. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.

  9. Calvo-Pardo, Hector & Mancini, Tullio & Olmo, Jose, 2021. "Granger causality detection in high-dimensional systems using feedforward neural networks," International Journal of Forecasting, Elsevier, vol. 37(2), pages 920-940.

    Cited by:

    1. Esra Alp Coşkun & Hakan Kahyaoglu & Chi Keung Marco Lau, 2023. "Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.
    2. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.

  10. Cheang, Chi Wan & Olmo, Jose & Ma, Tiejun & Sung, Ming-Chien & McGroarty, Frank, 2020. "Optimal asset allocation using a combination of implied and historical information," International Review of Financial Analysis, Elsevier, vol. 67(C).

    Cited by:

    1. Yi Huang & Wei Zhu & Duan Li & Shushang Zhu & Shikun Wang, 2023. "Integrating Different Informations for Portfolio Selection," Papers 2305.17881, arXiv.org.

  11. Kapar, Burcu & Olmo, Jose & Ghalayini, Rim, 2020. "Financial integration in the United Arab Emirates Stock Markets," Finance Research Letters, Elsevier, vol. 33(C).

    Cited by:

    1. Hsiang-Hsi Liu & Chien-Kuo Tseng, 2022. "Common Components in Co-integrated System and Its Estimation and Application: Evidence from Five Stock Markets in Asia-Pacific Chinese Region," Bulletin of Applied Economics, Risk Market Journals, vol. 9(2), pages 101-121.
    2. Ngo Thai Hung, 2021. "Financial connectedness of GCC emerging stock markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(4), pages 753-773, December.

  12. Kapar, Burcu & Olmo, Jose, 2019. "An analysis of price discovery between Bitcoin futures and spot markets," Economics Letters, Elsevier, vol. 174(C), pages 62-64.

    Cited by:

    1. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    2. Oliver Entrop & Bart Frijns & Marco Seruset, 2020. "The determinants of price discovery on bitcoin markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(5), pages 816-837, May.
    3. Virginie Terraza & Aslı Boru İpek & Mohammad Mahdi Rounaghi, 2024. "The nexus between the volatility of Bitcoin, gold, and American stock markets during the COVID-19 pandemic: evidence from VAR-DCC-EGARCH and ANN models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    4. Helder Miguel Correia Virtuoso Sebastião & Paulo José Osório Rupino Da Cunha & Pedro Manuel Cortesão Godinho, 2021. "Cryptocurrencies and blockchain. Overview and future perspectives," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 21(3), pages 305-342.
    5. Panpan Zhu & Xing Zhang & You Wu & Hao Zheng & Yinpeng Zhang, 2021. "Investor attention and cryptocurrency: Evidence from the Bitcoin market," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-28, February.
    6. Chen, Yu-Lun & Chang, Yung Ting & Yang, J. Jimmy, 2023. "Cryptocurrency hacking incidents and the price dynamics of Bitcoin spot and futures," Finance Research Letters, Elsevier, vol. 55(PB).
    7. Yakup Söylemez, 2019. "Cryptocurrency Derivatives: The Case of Bitcoin," Contributions to Economics, in: Umit Hacioglu (ed.), Blockchain Economics and Financial Market Innovation, chapter 0, pages 515-530, Springer.
    8. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    9. Hu, Yang & Hou, Yang Greg & Oxley, Les, 2020. "What role do futures markets play in Bitcoin pricing? Causality, cointegration and price discovery from a time-varying perspective?," International Review of Financial Analysis, Elsevier, vol. 72(C).
    10. Pati, Pratap Chandra, 2022. "Informativeness of CME Micro Bitcoin Futures in Pricing of Bitcoin: Intraday Evidence," Finance Research Letters, Elsevier, vol. 49(C).
    11. Domingo, Ribeiro-Soriano & Piñeiro-Chousa, Juan & Ángeles López-Cabarcos, M., 2020. "What factors drive returns on initial coin offerings?," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    12. Carl Luft & Jin Man Lee & Jin W. Choi, 2019. "“Chicago Mercantile Exchange Bitcoin Futures: Volatility, Liquidity and Margin”," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 69(3), pages 55-74, July-Sept.
    13. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Post-Print halshs-04250269, HAL.
    14. Wang, Qiyu & Chong, Terence Tai-Leung, 2021. "Factor pricing of cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    15. Ruan, Qingsong & Meng, Lu & Lv, Dayong, 2021. "Effect of introducing Bitcoin futures on the underlying Bitcoin market efficiency: A multifractal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    16. Liu, Ruozhou & Wan, Shanfeng & Zhang, Zili & Zhao, Xuejun, 2020. "Is the introduction of futures responsible for the crash of Bitcoin?," Finance Research Letters, Elsevier, vol. 34(C).
    17. Shynkevich, Andrei, 2021. "Bitcoin arbitrage," Finance Research Letters, Elsevier, vol. 40(C).
    18. Alexander, Carol & Heck, Daniel F., 2020. "Price discovery in Bitcoin: The impact of unregulated markets," Journal of Financial Stability, Elsevier, vol. 50(C).
    19. Shimeng Shi & Yukun Shi, 2021. "Bitcoin futures: trade it or ban it?," The European Journal of Finance, Taylor & Francis Journals, vol. 27(4-5), pages 381-396, March.
    20. Dimpfl, Thomas & Peter, Franziska J., 2021. "Nothing but noise? Price discovery across cryptocurrency exchanges," Journal of Financial Markets, Elsevier, vol. 54(C).
    21. Hattori, Takahiro & Ishida, Ryo, 2021. "Did the introduction of Bitcoin futures crash the Bitcoin market at the end of 2017?," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    22. Yu‐Lun Chen & J. Jimmy Yang, 2024. "Time‐varying price discovery in regular and microbitcoin futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(1), pages 103-121, January.
    23. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2019. "A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series," Papers 1909.10957, arXiv.org, revised Jul 2021.
    24. Cevik, Emrah Ismail & Gunay, Samet & Dibooglu, Sel & Yıldırım, Durmuş Çağrı, 2023. "The impact of expected and unexpected events on Bitcoin price development: Introduction of futures market and COVID-19," Finance Research Letters, Elsevier, vol. 54(C).
    25. Huang, Yingying & Duan, Kun & Urquhart, Andrew, 2023. "Time-varying dependence between Bitcoin and green financial assets: A comparison between pre- and post-COVID-19 periods," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    26. Prashant Sharma & Prashant Gupta & Dinesh Kumar Sharma & Gaurav Agarwal, 2022. "Investigating the Efficiency of Bitcoin Futures in Price Discovery," International Journal of Economics and Financial Issues, Econjournals, vol. 12(3), pages 104-109, May.
    27. Fassas, Athanasios P., 2021. "Price discovery in US money market benchmarks: LIBOR vs. SOFR," Economics Letters, Elsevier, vol. 204(C).
    28. Sebastião, Helder & Godinho, Pedro, 2020. "Bitcoin futures: An effective tool for hedging cryptocurrencies," Finance Research Letters, Elsevier, vol. 33(C).
    29. Umar, Muhammad & Rizvi, Syed Kumail Abbas & Naqvi, Bushra, 2021. "Dance with the devil? The nexus of fourth industrial revolution, technological financial products and volatility spillovers in global financial system," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    30. Fassas, Athanasios P. & Papadamou, Stephanos & Koulis, Alexandros, 2020. "Price discovery in bitcoin futures," Research in International Business and Finance, Elsevier, vol. 52(C).
    31. Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2021. "Trading activity and price discovery in Bitcoin futures markets," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 107-120.
    32. Gil-Alana, Luis Alberiko & Abakah, Emmanuel Joel Aikins & Rojo, María Fátima Romero, 2020. "Cryptocurrencies and stock market indices. Are they related?," Research in International Business and Finance, Elsevier, vol. 51(C).
    33. Alexander, Carol & Choi, Jaehyuk & Massie, Hamish R.A. & Sohn, Sungbin, 2020. "Price discovery and microstructure in ether spot and derivative markets," International Review of Financial Analysis, Elsevier, vol. 71(C).
    34. Gemayel, Roland & Franus, Tatiana & Bowden, James, 2023. "Price discovery between Bitcoin spot markets and exchange traded products," Economics Letters, Elsevier, vol. 228(C).
    35. Lee, Seungho & Meslmani, Nabil El & Switzer, Lorne N., 2020. "Pricing Efficiency and Arbitrage in the Bitcoin Spot and Futures Markets," Research in International Business and Finance, Elsevier, vol. 53(C).
    36. Lin, Mei-Yin & An, Che-Lun, 2021. "The relationship between Bitcoin and resource commodity futures: Evidence from NARDL approach," Resources Policy, Elsevier, vol. 74(C).
    37. Shimeng Shi, 2022. "Bitcoin futures risk premia," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2190-2217, December.
    38. Bao Doan & Huy Pham & Binh Nguyen Thanh, 2022. "Price discovery in the cryptocurrency market: evidence from institutional activity," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(1), pages 111-131, March.
    39. Yang Hu & Yang (Greg) Hou & Les Oxley, 2019. "Spot and Futures Prices of Bitcoin: Causality, Cointegration and Price Discovery from a Time-Varying Perspective," Working Papers in Economics 19/13, University of Waikato.
    40. Jun Deng & Huifeng Pan & Shuyu Zhang & Bin Zou, 2021. "Optimal Bitcoin trading with inverse futures," Annals of Operations Research, Springer, vol. 304(1), pages 139-163, September.
    41. Ma, Yechi & Ahmad, Ferhana & Liu, Miao & Wang, Zilong, 2020. "Portfolio optimization in the era of digital financialization using cryptocurrencies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    42. Wang, Jying-Nan & Liu, Hung-Chun & Hsu, Yuan-Teng, 2020. "Time-of-day periodicities of trading volume and volatility in Bitcoin exchange: Does the stock market matter?," Finance Research Letters, Elsevier, vol. 34(C).
    43. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-04250269, HAL.
    44. Efe Caglar Cagli & Pinar Evrim Mandaci, 2021. "Information transmission between bitcoin derivatives and spot markets: high-frequency causality analysis with Fourier approximation," Economics and Business Letters, Oviedo University Press, vol. 10(4), pages 394-402.
    45. Rahul Kumar Singh, 2023. "Efficiency of Wheat Futures across APMC Mandis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(3), pages 681-701, September.
    46. Jinghong Wu & Ke Xu & Xinwei Zheng & Jian Chen, 2021. "Fractional cointegration in bitcoin spot and futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(9), pages 1478-1494, September.
    47. John W Goodell & Stéphane Goutte, 2020. "Diversifying with cryptocurrencies during COVID-19," Working Papers halshs-02876529, HAL.

  13. Matthew Lyon & Jose Olmo, 2018. "Does the PPP condition hold for oil†exporting countries? A quantile cointegration regression approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 23(2), pages 79-93, April.

    Cited by:

    1. Mudeer A. Khattak & Buerhan Saiti & Shabeer Khan, 2023. "Does market power explain margins in dual banking? Evidence from panel quantile regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1826-1844, April.

  14. Antonio F Galvao & Ted Juhl & Gabriel Montes-Rojas & Jose Olmo, 2018. "Testing Slope Homogeneity in Quantile Regression Panel Data with an Application to the Cross-Section of Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 211-243.

    Cited by:

    1. Yiren Wang & Liangjun Su & Yichong Zhang, 2022. "Low-rank Panel Quantile Regression: Estimation and Inference," Papers 2210.11062, arXiv.org.
    2. Daan Opschoor & Dick van Dijk & Philip Hans Franses, 2021. "Heterogeneity in Manufacturing Growth Risk," Tinbergen Institute Discussion Papers 21-036/III, Tinbergen Institute.
    3. Chuliá, Helena & Koser, Christoph & Uribe, Jorge M., 2021. "Analyzing the Nonlinear Pricing of Liquidity Risk according to the Market State," Finance Research Letters, Elsevier, vol. 38(C).

  15. Antonio F. Galvao & Gabriel Montes–Rojas & Jose Olmo & Suyong Song, 2018. "On solving endogeneity with invalid instruments: an application to investment equations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 689-716, June.

    Cited by:

    1. Ali Al-Sharadqah & Majid Mojirsheibani & William Pouliot, 2020. "On the performance of weighted bootstrapped kernel deconvolution density estimators," Statistical Papers, Springer, vol. 61(4), pages 1773-1798, August.

  16. M. Angeles Carnero & Jose Olmo & Lorenzo Pascual, 2018. "Modelling the Dynamics of Fuel and EU Allowance Prices during Phase 3 of the EU ETS," Energies, MDPI, vol. 11(11), pages 1-23, November.

    Cited by:

    1. Vlad-Cosmin Bulai & Alexandra Horobet & Oana Cristina Popovici & Lucian Belascu & Sofia Adriana Dumitrescu, 2021. "A VaR-Based Methodology for Assessing Carbon Price Risk across European Union Economic Sectors," Energies, MDPI, vol. 14(24), pages 1-21, December.
    2. Xing Zhang & Chongchong Zhang & Zhuoqun Wei, 2019. "Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors," Energies, MDPI, vol. 12(22), pages 1-23, November.
    3. Friedrich, Marina & Mauer, Eva-Maria & Pahle, Michael & Tietjen, Oliver, 2020. "From fundamentals to financial assets: the evolution of understanding price formation in the EU ETS," EconStor Preprints 196150, ZBW - Leibniz Information Centre for Economics, revised 2020.
    4. Nader Trabelsi & Aviral Kumar Tiwari, 2023. "CO2 Emission Allowances Risk Prediction with GAS and GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 775-805, February.
    5. Qiao, Sen & Dang, Yi Jing & Ren, Zheng Yu & Zhang, Kai Quan, 2023. "The dynamic spillovers among carbon, fossil energy and electricity markets based on a TVP-VAR-SV method," Energy, Elsevier, vol. 266(C).
    6. Joao Leitao & Joaquim Ferreira & Ernesto Santibanez‐Gonzalez, 2021. "Green bonds, sustainable development and environmental policy in the European Union carbon market," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 2077-2090, May.

  17. Jose Olmo & Marcos Sanso-Navarro, 2018. "Unconventional monetary policies and the credit market," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 11(5), pages 480-498.

    Cited by:

    1. Rui Wang, 2021. "Evaluating the Unconventional Monetary Policy of the Bank of Japan: A DSGE Approach," JRFM, MDPI, vol. 14(6), pages 1-18, June.

  18. Laborda, Ricardo & Olmo, Jose, 2017. "Optimal asset allocation for strategic investors," International Journal of Forecasting, Elsevier, vol. 33(4), pages 970-987.

    Cited by:

    1. Reza Bradrania & Davood Pirayesh Neghab, 2022. "State-dependent Asset Allocation Using Neural Networks," Papers 2211.00871, arXiv.org.
    2. Bradrania, Reza & Pirayesh Neghab, Davood, 2021. "State-dependent asset allocation using neural networks," MPRA Paper 115254, University Library of Munich, Germany.

  19. Juan Laborda & Ricardo Laborda & Jose Olmo, 2016. "Investing in the size factor," Quantitative Finance, Taylor & Francis Journals, vol. 16(1), pages 85-100, January.

    Cited by:

    1. Laborda, Ricardo & Olmo, Jose, 2017. "Optimal asset allocation for strategic investors," International Journal of Forecasting, Elsevier, vol. 33(4), pages 970-987.
    2. Choi, Jungjun & Yang, Xiye, 2022. "Asymptotic properties of correlation-based principal component analysis," Journal of Econometrics, Elsevier, vol. 229(1), pages 1-18.

  20. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.

    Cited by:

    1. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    2. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2019. "Quantifying Risk in Traditional Energy and Sustainable Investments," Sustainability, MDPI, vol. 11(3), pages 1-22, January.

  21. Katja Ahoniemi & Ana-Maria Fuertes & Jose Olmo, 2016. "Overnight News and Daily Equity Trading Risk Limits," Journal of Financial Econometrics, Oxford University Press, vol. 14(3), pages 525-551.

    Cited by:

    1. Barbara Będowska-Sójka, 2018. "Is intraday data useful for forecasting VaR? The evidence from EUR/PLN exchange rate," Risk Management, Palgrave Macmillan, vol. 20(4), pages 326-346, November.
    2. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    3. Dohyun Chun & Donggyu Kim, 2021. "State Heterogeneity Analysis of Financial Volatility Using High-Frequency Financial Data," Papers 2102.13404, arXiv.org.
    4. Dohyun Chun & Donggyu Kim, 2022. "State Heterogeneity Analysis of Financial Volatility using high‐frequency Financial Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 105-124, January.
    5. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.

  22. Rafael Gonz�lez-Val & Jose Olmo, 2015. "Growth in a Cross-section of Cities: Location, Increasing Returns or Random Growth?," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(2), pages 230-261, June.
    See citations under working paper version above.
  23. Olmo, Jose & Sanso-Navarro, Marcos, 2015. "Changes in the transmission of monetary policy during crisis episodes: Evidence from the euro area and the U.S," Economic Modelling, Elsevier, vol. 48(C), pages 155-166.

    Cited by:

    1. Massimo Guidolin & Manuela Pedio, 2019. "Does the Cost of Private Debt Respond to Monetary Policy? Heteroskedasticity-Based Identification in a Model with Regimes," BAFFI CAREFIN Working Papers 19118, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    2. Chikashi Tsuji, 2016. "Did the expectations channel work? Evidence from quantitative easing in Japan, 2001–06," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1210996-121, December.
    3. Hummaira Jabeen, 2022. "Monetary Policy Shock Transmission in Emerging Markets," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 8(4), pages 379-390, December.
    4. Frijters, Paul & Antić, Nemanja, 2016. "Can collapsing business networks explain economic downturns?," Economic Modelling, Elsevier, vol. 54(C), pages 289-308.
    5. Liu, Dayu & Xu, Ning & Zhao, Tingting & Song, Yang, 2018. "Identifying the nonlinear correlation between business cycle and monetary policy rule: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 73(C), pages 45-54.
    6. Juan S. Holguín & Jorge M. Uribe, 2020. "The credit supply channel of monetary policy: evidence from a FAVAR model with sign restrictions," Empirical Economics, Springer, vol. 59(5), pages 2443-2472, November.

  24. Iori Giulia & Kapar Burcu & Olmo Jose, 2015. "Bank characteristics and the interbank money market: a distributional approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(3), pages 249-283, June.

    Cited by:

    1. Green, Christopher & Bai, Ye & Murinde, Victor & Ngoka, Kethi & Maana, Isaya & Tiriongo, Samuel, 2016. "Overnight interbank markets and the determination of the interbank rate: A selective survey," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 149-161.
    2. Paolo Barucca & Fabrizio Lillo, 2018. "The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market," Computational Management Science, Springer, vol. 15(1), pages 33-53, January.
    3. Bednarek, Peter & Dinger, Valeriya & Schultz, Alison & von Westernhagen, Natalja, 2023. "Banks of a feather: The informational advantage of being alike," Discussion Papers 09/2023, Deutsche Bundesbank.
    4. Katarzyna Bech & Grant Hillier, 2015. "Nonparametric testing for exogeneity with discrete regressors and instruments," CeMMAP working papers CWP11/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Domenico Di Gangi & Giacomo Bormetti & Fabrizio Lillo, 2022. "Score Driven Generalized Fitness Model for Sparse and Weighted Temporal Networks," Papers 2202.09854, arXiv.org, revised Mar 2022.
    6. Berardi, Simone & Tedeschi, Gabriele, 2017. "From banks' strategies to financial (in)stability," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 255-272.
    7. Anastasios Demertzidis, 2019. "Interbank transactions on the intraday frequency: -Different market states and the effects of the financial crisis-," MAGKS Papers on Economics 201932, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    8. Temizsoy, Asena & Iori, Giulia & Montes-Rojas, Gabriel, 2015. "The role of bank relationships in the interbank market," Journal of Economic Dynamics and Control, Elsevier, vol. 59(C), pages 118-141.
    9. Paolo Barucca & Fabrizio Lillo, 2015. "The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market," Papers 1511.08068, arXiv.org, revised Sep 2017.

  25. Laborda, Ricardo & Olmo, Jose, 2014. "Investor sentiment and bond risk premia," Journal of Financial Markets, Elsevier, vol. 18(C), pages 206-233.

    Cited by:

    1. Zhou, Liyun & Huang, Jialiang, 2020. "Contagion of future-level sentiment in Chinese Agricultural Futures Markets," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    2. Wei Zhang & Yingxiu Zhao & Pengfei Wang & Dehua Shen, 2020. "Investor Sentiment and the Return Rate of P2P Lending Platform," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(1), pages 97-113, March.
    3. Chen, Wen-Yi & Chen, Mei-Ping, 2022. "Twitter’s daily happiness sentiment, economic policy uncertainty, and stock index fluctuations," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    4. Oguzhan Cepni & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2019. "Time-Varying Risk Aversion and the Predictability of Bond Premia," Working Papers 201906, University of Pretoria, Department of Economics.
    5. Manish K. Singh & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2019. "“Increasing contingent guarantees: The asymmetrical effect on sovereign risk of different government interventions"," IREA Working Papers 201914, University of Barcelona, Research Institute of Applied Economics, revised Sep 2019.
    6. Edmans, Alex & Fernandez-Perez, Adrian & Garel, Alexandre & Indriawan, Ivan, 2022. "Music sentiment and stock returns around the world," Journal of Financial Economics, Elsevier, vol. 145(2), pages 234-254.
    7. Laborda, Ricardo & Muñoz, Fernando, 2016. "Optimal allocation of government bond funds through the business cycle. Is money smart?," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 46-67.
    8. Oguzhan Cepni & Rangan Gupta & I. Ethem Guney & M. Hasan Yilmaz, 2019. "Forecasting Local Currency Bond Risk Premia of Emerging Markets: The Role of Cross-Country Macro-Financial Linkages," Working Papers 201957, University of Pretoria, Department of Economics.
    9. Louis RAFFESTIN, 2016. "Do bond credit ratings lead to excess comovement," LEO Working Papers / DR LEO 2481, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    10. Juan Andrés Espinosa-Torres & Jose E. Gomez-Gonzalez & Luis Fernando Melo-Velandia & José Fernando Moreno-Gutiérrez, 2015. "The International Transmission of Risk: Causal Relations Among Developed and Emerging Countries’ Term Premia," Borradores de Economia 12609, Banco de la Republica.
    11. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    12. Balcilar, Mehmet & Gupta, Rangan & Wang, Shixuan & Wohar, Mark E., 2020. "Oil price uncertainty and movements in the US government bond risk premia," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    13. Roland Füss & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia In The Cross-Section of Global Equity," Working Papers on Finance 1913, University of St. Gallen, School of Finance, revised May 2020.
    14. Juan Andrés Espinosa Torres & Luis Fernando Melo Velandia & José Fernando Moreno Gutiérrez, 2014. "Estimación de la prima por vencimiento de los TES en pesos del gobierno colombiano," Borradores de Economia 854, Banco de la Republica de Colombia.
    15. Turkmen Muldur Gozde & Kandir Serkan Yılmaz & Onal Yıldırım Beyazıt, 2019. "Investor Sentiment and Speculative Bond Yield Spreads," Foundations of Management, Sciendo, vol. 11(1), pages 177-186, January.
    16. Zhou, Liyun & Huang, Jialiang, 2020. "Excess co-movement of agricultural futures prices: Perspective from contagious investor sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    17. Chi-Wei Su & Xu-Yu Cai & Ran Tao, 2020. "Can Stock Investor Sentiment Be Contagious in China?," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    18. Dan Zhang & Arash Farnoosh & Zhengwei Ma, 2022. "Does the Launch of Shanghai Crude Oil Futures Stabilize the Spot Market ? A Financial Cycle Perspective," Post-Print hal-03910474, HAL.
    19. Raffestin, Louis, 2017. "Do bond credit ratings lead to excess comovement?," Journal of Banking & Finance, Elsevier, vol. 85(C), pages 41-55.
    20. Agoraki, Maria-Eleni K. & Aslanidis, Nektarios & Kouretas, Georgios P., 2022. "U.S. banks’ lending, financial stability, and text-based sentiment analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 73-90.
    21. Laborda, Ricardo & Olmo, Jose, 2017. "Optimal asset allocation for strategic investors," International Journal of Forecasting, Elsevier, vol. 33(4), pages 970-987.
    22. Li, Yulin, 2021. "Investor sentiment and sovereign bonds," Journal of International Money and Finance, Elsevier, vol. 115(C).
    23. Bansal, Naresh & Connolly, Robert A. & Stivers, Chris, 2015. "Equity volatility as a determinant of future term-structure volatility," Journal of Financial Markets, Elsevier, vol. 25(C), pages 33-51.
    24. Elie Bouri & Riza Demirer & Rangan Gupta & Mark E. Wohar, 2019. "Gold, Platinum and the Predictability of Bond Risk Premia," Working Papers 201967, University of Pretoria, Department of Economics.
    25. Balbás, Alejandro & Laborda Herrero, Ricardo, 2017. "Interest Rate Future Quality Options and Negative Interest Rates," INDEM - Working Paper Business Economic Series 24859, Instituto para el Desarrollo Empresarial (INDEM).
    26. Bouri, Elie & Gupta, Rangan & Majumdar, Anandamayee & Subramaniam, Sowmya, 2021. "Time-varying risk aversion and forecastability of the US term structure of interest rates," Finance Research Letters, Elsevier, vol. 42(C).
    27. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2021. "Variants of consumption‐wealth ratios and predictability of U.S. government bond risk premia," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 661-674, June.
    28. Roland Fuess & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia in the Cross-Section of Global Equity and Currency Returns," BAFFI CAREFIN Working Papers 19116, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    29. Islam, Mohd. Anisul, 2021. "Investor sentiment in the equity market and investments in corporate-bond funds," International Review of Financial Analysis, Elsevier, vol. 78(C).
    30. Xu, Alan, 2022. "Air pollution and mediation effects in stock market, longitudinal evidence from China," International Review of Financial Analysis, Elsevier, vol. 83(C).
    31. Oguzhan Cepni & Rangan Gupta & Mark E. Wohar, 2019. "Variants of Consumption-Wealth Ratios and Predictability of U.S. Government Bond Risk Premia: Old is still Gold," Working Papers 201912, University of Pretoria, Department of Economics.
    32. Juan Andrés Espinosa Torres & Luis Fernando Melo Velandia & José Fernando Moreno Gutiérrez, 2014. "Estimación de la prima por vencimiento de los TES en pesos del gobierno colombiano," Borradores de Economia 12333, Banco de la Republica.

  26. Jesus Gonzalo & Jose Olmo, 2014. "Conditional Stochastic Dominance Tests In Dynamic Settings," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 819-838, August.
    See citations under working paper version above.
  27. Mark Hallam & Jose Olmo, 2014. "Semiparametric Density Forecasts of Daily Financial Returns from Intraday Data," Journal of Financial Econometrics, Oxford University Press, vol. 12(2), pages 408-432.

    Cited by:

    1. Song, Shijia & Li, Handong, 2023. "A method for predicting VaR by aggregating generalized distributions driven by the dynamic conditional score," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 203-214.
    2. Dovern, Jonas & Manner, Hans, 2016. "Robust Evaluation of Multivariate Density Forecasts," VfS Annual Conference 2016 (Augsburg): Demographic Change 145547, Verein für Socialpolitik / German Economic Association.
    3. Gao, Bin & Yang, Chunpeng, 2017. "Forecasting stock index futures returns with mixed-frequency sentiment," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 69-83.
    4. Hallam, Mark & Olmo, Jose, 2014. "Forecasting daily return densities from intraday data: A multifractal approach," International Journal of Forecasting, Elsevier, vol. 30(4), pages 863-881.
    5. Jonas Dovern & Hans Manner, 2018. "Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts," CESifo Working Paper Series 7023, CESifo.
    6. Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.
    7. Dovern, Jonas & Manner, Hans, 2016. "Order Invariant Evaluation of Multivariate Density Forecasts," Working Papers 0608, University of Heidelberg, Department of Economics.

  28. Hallam, Mark & Olmo, Jose, 2014. "Forecasting daily return densities from intraday data: A multifractal approach," International Journal of Forecasting, Elsevier, vol. 30(4), pages 863-881.

    Cited by:

    1. Gao, Bin & Yang, Chunpeng, 2017. "Forecasting stock index futures returns with mixed-frequency sentiment," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 69-83.
    2. Méndez-Gordillo, Alma Rosa & Campos-Amezcua, Rafael & Cadenas, Erasmo, 2022. "Wind speed forecasting using a hybrid model considering the turbulence of the airflow," Renewable Energy, Elsevier, vol. 196(C), pages 422-431.
    3. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
    4. Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.

  29. Antonio Galvao & Kengo Kato & Gabriel Montes-Rojas & Jose Olmo, 2014. "Testing linearity against threshold effects: uniform inference in quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 413-439, April.

    Cited by:

    1. Junho Lee & Ying Sun & Huixia Judy Wang, 2021. "Spatial cluster detection with threshold quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    2. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.
    3. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    4. Martins, Luis F., 2021. "The US debt–growth nexus along the business cycle," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    5. Chung-Ming Kuan & Christos Michalopoulos & Zhijie Xiao, 2017. "Quantile Regression on Quantile Ranges – A Threshold Approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 99-119, January.
    6. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    7. Sokbae (Simon) Lee & Hyunmin Park & Myung Hwan Seo & Youngki Shin, 2014. "A contribution to the Reinhart and Rogoff debate: not 90 percent but maybe 30 percent," CeMMAP working papers 39/14, Institute for Fiscal Studies.
    8. Liwen Zhang & Huixia Judy Wang & Zhongyi Zhu, 2017. "Composite change point estimation for bent line quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 145-168, February.
    9. Christis Katsouris, 2023. "Estimation and Inference in Threshold Predictive Regression Models with Locally Explosive Regressors," Papers 2305.00860, arXiv.org, revised May 2023.

  30. Laborda, Juan & Laborda, Ricardo & Olmo, Jose, 2014. "Optimal currency carry trade strategies," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 52-66.

    Cited by:

    1. Yamani, Ehab, 2019. "Diversification role of currency momentum for carry trade: Evidence from financial crises," Journal of Multinational Financial Management, Elsevier, vol. 49(C), pages 1-19.
    2. Chang‐Che Wu & MeiChi Huang & Chih‐Chiang Wu, 2021. "The role of asymmetry and dynamics in carry trade and general financial markets," The Financial Review, Eastern Finance Association, vol. 56(2), pages 331-353, May.
    3. Lei Pan & Svetlana Maslyuk-Escobedo & Vinod Mishra, 2019. "Carry Trade Returns and Commodity Prices under Capital and Interest Rate Controls: Empirical Evidence from China," Monash Economics Working Papers 16-18, Monash University, Department of Economics.
    4. Lumengo Bonga-Bonga & Tebogo Maake, 2021. "The Relationship between Carry Trade and Asset Markets in South Africa," JRFM, MDPI, vol. 14(7), pages 1-13, July.
    5. Laborda, Ricardo, 2018. "Optimal combination of currency strategies," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 129-140.
    6. Dupuy, Philippe, 2021. "Risk-adjusted return managed carry trade," Journal of Banking & Finance, Elsevier, vol. 129(C).

  31. Yuzhi Cai & Gabriel Montes‐Rojas & Jose Olmo, 2013. "Quantile Double AR Time Series Models for Financial Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 551-560, September.

    Cited by:

    1. Kai Yang & Qingqing Zhang & Xinyang Yu & Xiaogang Dong, 2023. "Bayesian inference for a mixture double autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 188-207, May.
    2. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
    3. Zhu, Huafeng & Zhang, Xingfa & Liang, Xin & Li, Yuan, 2017. "On a vector double autoregressive model," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 86-95.
    4. Zhu Huafeng & Zhang Xingfa & Liang Xin & Li Yuan, 2018. "Moving Average Model with an Alternative GARCH-Type Error," Journal of Systems Science and Information, De Gruyter, vol. 6(2), pages 165-177, April.

  32. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.

    Cited by:

    1. Fei, Fei & Fuertes, Ana-Maria & Kalotychou, Elena, 2017. "Dependence in credit default swap and equity markets: Dynamic copula with Markov-switching," International Journal of Forecasting, Elsevier, vol. 33(3), pages 662-678.
    2. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    3. Chiu, Yen-Chen & Chuang, I-Yuan, 2016. "The performance of the switching forecast model of value-at-risk in the Asian stock markets," Finance Research Letters, Elsevier, vol. 18(C), pages 43-51.
    4. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    5. Dimitriadis, Timo & Liu, Xiaochun & Schnaitmann, Julie, 2020. "Encompassing tests for value at risk and expected shortfall multi-step forecasts based on inference on the boundary," Hohenheim Discussion Papers in Business, Economics and Social Sciences 11-2020, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    6. Meng, Xiaochun & Taylor, James W., 2018. "An approximate long-memory range-based approach for value at risk estimation," International Journal of Forecasting, Elsevier, vol. 34(3), pages 377-388.
    7. Marius Matei & Xari Rovira & Núria Agell, 2019. "Bivariate Volatility Modeling with High-Frequency Data," Econometrics, MDPI, vol. 7(3), pages 1-15, September.
    8. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    9. Reber, Beat, 2017. "Does mispricing, liquidity or third-party certification contribute to IPO downside risk?," International Review of Financial Analysis, Elsevier, vol. 51(C), pages 25-53.
    10. Seyed Mohammad Sina Seyfi & Azin Sharifi & Hamidreza Arian, 2020. "Portfolio Risk Measurement Using a Mixture Simulation Approach," Papers 2011.07994, arXiv.org.
    11. Arian, Hamid & Moghimi, Mehrdad & Tabatabaei, Ehsan & Zamani, Shiva, 2022. "Encoded Value-at-Risk: A machine learning approach for portfolio risk measurement," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 500-525.
    12. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, vol. 29(4), pages 592-604.
    13. Lazar, Emese & Xue, Xiaohan, 2020. "Forecasting risk measures using intraday data in a generalized autoregressive score framework," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1057-1072.
    14. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    15. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    16. Seyfi, Seyed Mohammad Sina & Sharifi, Azin & Arian, Hamidreza, 2021. "Portfolio Value-at-Risk and expected-shortfall using an efficient simulation approach based on Gaussian Mixture Model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1056-1079.
    17. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    18. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    19. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    20. Andrada-Félix, Julián & Fernández-Rodríguez, Fernando & Fuertes, Ana-Maria, 2016. "Combining nearest neighbor predictions and model-based predictions of realized variance: Does it pay?," International Journal of Forecasting, Elsevier, vol. 32(3), pages 695-715.
    21. Ana-Maria Fuertes & Elena Kalotychou & Natasa Todorovic, 2015. "Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?," Review of Quantitative Finance and Accounting, Springer, vol. 45(2), pages 251-278, August.
    22. Taylor, James W., 2020. "Forecast combinations for value at risk and expected shortfall," International Journal of Forecasting, Elsevier, vol. 36(2), pages 428-441.
    23. Jan G. De Gooijer, 2023. "Penalized Averaging of Quantile Forecasts from GARCH Models with Many Exogenous Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 407-424, June.

  33. Antonio F. Galvao & Gabriel Montes-Rojas & Jose Olmo, 2013. "A panel data test for poverty traps," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1943-1952, May.

    Cited by:

    1. Golub, A. & Potashnikov, V., 2022. "Theoretical analysis of development traps: On the example of Russia," Journal of the New Economic Association, New Economic Association, vol. 54(2), pages 56-74.

  34. Martinez Oscar & Olmo Jose, 2012. "A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-39, September.
    See citations under working paper version above.
  35. Olmo, José & Sanso-Navarro, Marcos, 2012. "Forecasting the performance of hedge fund styles," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2351-2365.

    Cited by:

    1. Laborda, Ricardo, 2018. "Optimal combination of currency strategies," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 129-140.
    2. 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.

  36. J. Carlos Escanciano & Jose Olmo, 2011. "Robust Backtesting Tests for Value-at-risk Models," Journal of Financial Econometrics, Oxford University Press, vol. 9(1), pages 132-161, Winter.

    Cited by:

    1. Farkas, Walter & Fringuellotti, Fulvia & Tunaru, Radu, 2020. "A cost-benefit analysis of capital requirements adjusted for model risk," Journal of Corporate Finance, Elsevier, vol. 65(C).
    2. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 499-512, October.
    3. Igor L. Kheifets, 2015. "Specification tests for nonlinear dynamic models," Econometrics Journal, Royal Economic Society, vol. 18(1), pages 67-94, February.
    4. Christian Francq & Jean-Michel Zakoian, 2019. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Papers 1909.04661, arXiv.org.
    5. Sander Barendse & Erik Kole & Dick van Dijk, 2023. "Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 528-568.
    6. Christophe Boucher & Jon Danielsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk models-at-risk," Post-Print hal-02312332, HAL.
    7. Olivier de Bandt & Jean-Cyprien Héam & Claire Labonne & Santiago Tavolaro, 2015. "La mesure du risque systémique après la crise financière," Revue économique, Presses de Sciences-Po, vol. 66(3), pages 481-500.
    8. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, January.
    9. Filippo Curti & Marco Migueis, 2016. "Predicting Operational Loss Exposure Using Past Losses," Finance and Economics Discussion Series 2016-2, Board of Governors of the Federal Reserve System (U.S.).
    10. Argyropoulos, Christos & Panopoulou, Ekaterini, 2019. "Backtesting VaR and ES under the magnifying glass," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 22-37.
    11. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    12. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    13. Juan Carlos Escanciano & Pei Pei, 2012. "Pitfalls in Backtesting Historical Simulation VaR Models," CAEPR Working Papers 2012-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    14. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    15. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
    16. Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.
    17. Christian Gouriéroux & Jean-Michel Zakoian, 2012. "Estimation Adjusted VaR," Working Papers 2012-16, Center for Research in Economics and Statistics.
    18. Francq, Christian & Zakoian, Jean-Michel, 2015. "Joint inference on market and estimation risks in dynamic portfolios," MPRA Paper 68100, University Library of Munich, Germany.
    19. Zulu, Thulani & Manguzvane, Mathias Mandla & Bonga-Bonga, Lumengo, 2023. "Assessing the contribution of South African Insurance Firms to Systemic Risk," MPRA Paper 116815, University Library of Munich, Germany.
    20. Evers, Corinna & Rohde, Johannes, 2014. "Model Risk in Backtesting Risk Measures," Hannover Economic Papers (HEP) dp-529, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    21. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    22. Köksal, Bülent & Orhan, Mehmet, 2012. "Market risk of developed and developing countries during the global financial crisis," MPRA Paper 37523, University Library of Munich, Germany.
    23. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.
    24. Bogdan Wlodarczyk, 2017. "Zmiennosc cen na globalnym rynku surowcow a ryzyko banku," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 15(66), pages 107-124.

  37. Jose Olmo & Keith Pilbeam, 2011. "Uncovered interest parity and the efficiency of the foreign exchange market: a re‐examination of the evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 16(2), pages 189-204, April.

    Cited by:

    1. Ladislav Kristoufek & Miloslav Vosvrda, 2015. "Gold, currencies and market efficiency," Papers 1510.08615, arXiv.org.
    2. Kohlscheen, Emanuel, 2014. "The impact of monetary policy on the exchange rate: A high frequency exchange rate puzzle in emerging economies," Journal of International Money and Finance, Elsevier, vol. 44(C), pages 69-96.
    3. Beckmann, Joscha & Czudaj, Robert, 2017. "Exchange rate expectations since the financial crisis: Performance evaluation and the role of monetary policy and safe haven," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168291, Verein für Socialpolitik / German Economic Association.
    4. Daniel L. Thornton, 2007. "Resolving the unbiasedness and forward premium puzzles," Working Papers 2007-014, Federal Reserve Bank of St. Louis.
    5. Katarzyna Czech & Łukasz Pietrych, 2021. "The Efficiency of the Polish Zloty Exchange Rate Market: The Uncovered Interest Parity and Fractal Analysis Approaches," Risks, MDPI, vol. 9(8), pages 1-17, August.
    6. Yutaka Kurihara, 2015. "Are Japanese Stock Prices Important Deterministic Elements of Exchange Rate Returns?," Bulletin of Applied Economics, Risk Market Journals, vol. 2(2), pages 1-9.
    7. Jorge Andrés Muñoz Mendoza & Carmen Lissette Veloso Ramos & Sandra María Sepúlveda Yelpo & Carlos Leandro Delgado Fuentealba & Edinson Edgardo Cornejo Saavedra, 2022. "Exchange Markets and Stock Markets Integration in Latin-America," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(3), pages 1-24, Julio - S.
    8. Efthymios Argyropoulos & Nikolaos Elias & Dimitris Smyrnakis & Elias Tzavalis, 2021. "Can country-specific interest rate factors explain the forward premium anomaly?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(2), pages 252-269, April.
    9. Katarzyna Anna Czech, & Adam Waszkowski, 2012. "Foreign Exchange Market Efficiency. Empirical Results For The Usd/Eur Market," "e-Finanse", University of Information Technology and Management, Institute of Financial Research and Analysis, vol. 8(3), pages 1-9, October.
    10. H. Kent Baker & Satish Kumar & Kirti Goyal & Prashant Gupta, 2023. "International journal of finance and economics: A bibliometric overview," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 9-46, January.
    11. Elias, Nikolaos & Smyrnakis, Dimitris & Tzavalis, Elias, 2022. "Predicting future exchange rate changes based on interest rates and holding-period returns differentials net of the forward risk premium effects," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 694-715.
    12. Michael D. Goldberg & Olesia Kozlova & Deniz Ozabaci, 2020. "Forward Rate Bias in Developed and Developing Countries: More Risky Not Less Rational," Econometrics, MDPI, vol. 8(4), pages 1-26, December.
    13. Lucjan Orlowski & Carolyne Soper & Monika Sywak, 2023. "Uncovered equity returns parity in non‐euro Central European EU member countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 307-315, January.

  38. Keith Pilbeam & Jose Olmo, 2011. "The forward discount puzzle and market efficiency," Annals of Finance, Springer, vol. 7(1), pages 119-135, February.

    Cited by:

    1. Yutaka Kurihara, 2015. "Are Japanese Stock Prices Important Deterministic Elements of Exchange Rate Returns?," Bulletin of Applied Economics, Risk Market Journals, vol. 2(2), pages 1-9.
    2. Norman C. Miller, 2014. "Exchange Rate Economics," Books, Edward Elgar Publishing, number 14981.
    3. Beckmann, Joscha & Belke, Ansgar & Czudaj, Robert, 2014. "Regime-dependent adjustment in energy spot and futures markets," Economic Modelling, Elsevier, vol. 40(C), pages 400-409.
    4. Azzam, Islam & El-Masry, Ahmed A. & Yamani, Ehab, 2023. "Foreign exchange market efficiency during COVID-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 717-730.
    5. Ahmad, Rubi & Rhee, S. Ghon & Wong, Yuen Meng, 2012. "Foreign exchange market efficiency under recent crises: Asia-Pacific focus," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1574-1592.

  39. Olmo, Jose & Pilbeam, Keith & Pouliot, William, 2011. "Detecting the presence of insider trading via structural break tests," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2820-2828, November.

    Cited by:

    1. Jonathan A. Batten & Igor Lončarski & Peter G. Szilagyi, 2018. "When Kamay Met Hill: Organisational Ethics in Practice," Journal of Business Ethics, Springer, vol. 147(4), pages 779-792, February.
    2. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.
    3. Xihan Xiong & Zhipeng Wang & Tianxiang Cui & William Knottenbelt & Michael Huth, 2023. "Market Misconduct in Decentralized Finance (DeFi): Analysis, Regulatory Challenges and Policy Implications," Papers 2311.17715, arXiv.org, revised Mar 2024.
    4. Batten, Jonathan A. & Lončarski, Igor & Szilagyi, Peter G., 2021. "Strategic insider trading in foreign exchange markets," Journal of Corporate Finance, Elsevier, vol. 69(C).
    5. Luke M. Bennett & Wei Hu, 2023. "Filtration enlargement‐based time series forecast in view of insider trading," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 112-140, February.
    6. Michael Buchner & Tobias A. Jopp, 2019. "Full steam ahead: Insider knowledge, stock trading and the nationalization of the railways in Prussia around 1879," Working Papers 0151, European Historical Economics Society (EHES).
    7. Nguyen, Vinh & Tran, Anh & Zeckhauser, Richard, 2017. "Stock splits to profit insider trading: Lessons from an emerging market," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 69-87.
    8. Keshab Bhattarai, 2015. "Financial deepening and economic growth," Applied Economics, Taylor & Francis Journals, vol. 47(11), pages 1133-1150, March.
    9. Simon de Bonviller & Alec Zuo & Sarah Ann Wheeler, 2019. "Is there evidence of insider trading in Australian water markets?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(2), pages 307-327, April.
    10. Jose Olmo & William Pouliot, 2014. "Tests to Disentangle Breaks in Intercept from Slope in Linear Regression Models with Application to Management Performance in the Mutual Fund Industry," Discussion Papers 14-02, Department of Economics, University of Birmingham.
    11. J. James Reade & Sachiko Akie, 2013. "Using Forecasting to Detect Corruption in International Football," Working Papers 2013-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    12. James, Robert & Leung, Henry & Prokhorov, Artem, 2023. "A machine learning attack on illegal trading," Journal of Banking & Finance, Elsevier, vol. 148(C).
    13. Cline, Brandon N. & Posylnaya, Valeriya V., 2019. "Illegal insider trading: Commission and SEC detection," Journal of Corporate Finance, Elsevier, vol. 58(C), pages 247-269.
    14. Hanedar, Avni Önder & Yaldız Hanedar, Elmas & Göktan, Mehmet Gökhan, 2022. "Insider trading on Ottoman sovereign default: The Ottoman General Debt Bond at European and İstanbul financial markets," Finance Research Letters, Elsevier, vol. 47(PB).
    15. Sahbi FARHANI, 2012. "Tests of Parameters Instability: Theoretical Study and Empirical Analysis on Two Types of Models (ARMA Model and Market Model)," International Journal of Economics and Financial Issues, Econjournals, vol. 2(3), pages 246-266.
    16. James Reade, 2014. "Detecting corruption in football," Chapters, in: John Goddard & Peter Sloane (ed.), Handbook on the Economics of Professional Football, chapter 25, pages 419-446, Edward Elgar Publishing.
    17. Keshab Bhattarai, 2015. "Financial Deepening and Economic Growth in Advanced and Emerging Economies," Review of Development Economics, Wiley Blackwell, vol. 19(1), pages 178-195, February.
    18. John Goddard & Peter Sloane (ed.), 2014. "Handbook on the Economics of Professional Football," Books, Edward Elgar Publishing, number 14821.

  40. Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.
    See citations under working paper version above.
  41. Antonio F. Galvao Jr. & Gabriel Montes‐Rojas & Jose Olmo, 2011. "Threshold quantile autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 253-267, May.

    Cited by:

    1. Junho Lee & Ying Sun & Huixia Judy Wang, 2021. "Spatial cluster detection with threshold quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    2. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    3. Liu Xiaochun & Luger Richard, 2018. "Markov-switching quantile autoregression: a Gibbs sampling approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(2), pages 1, April.
    4. Cathy Chen & Richard Gerlach, 2013. "Semi-parametric quantile estimation for double threshold autoregressive models with heteroskedasticity," Computational Statistics, Springer, vol. 28(3), pages 1103-1131, June.
    5. Olivier Damette & Beum-Jo Park, 2015. "Tobin Tax and Volatility: A Threshold Quantile Autoregressive Regression Framework," Review of International Economics, Wiley Blackwell, vol. 23(5), pages 996-1022, November.
    6. Yunmi Kim & Lijuan Huo & Tae-Hwan Kim, 2020. "Dealing with Markov-Switching Parameters in Quantile Regression Models," Working papers 2020rwp-166, Yonsei University, Yonsei Economics Research Institute.
    7. Martins, Luis F., 2021. "The US debt–growth nexus along the business cycle," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    8. Jean-Paul Chavas & Salvatore Falco, 2017. "Resilience, Weather and Dynamic Adjustments in Agroecosystems: The Case of Wheat Yield in England," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(2), pages 297-320, June.
    9. Tang, Yanlin & Song, Xinyuan & Zhu, Zhongyi, 2015. "Threshold effect test in censored quantile regression," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 149-156.
    10. Chung-Ming Kuan & Christos Michalopoulos & Zhijie Xiao, 2017. "Quantile Regression on Quantile Ranges – A Threshold Approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 99-119, January.
    11. Camille Aït-Youcef, 2019. "How index investment impacts commodities : A story about the financialization of agricultural commodities," Post-Print hal-03484371, HAL.
    12. Lijuan Huo & Tae-Hwan Kim & Yunmi Kim, 2013. "Testing for Autocorrelation in Quantile Regression Models," Working papers 2013rwp-54, Yonsei University, Yonsei Economics Research Institute.
    13. Chavas, Jean-Paul & Grainger, Corbett & Hudson, Nicholas, 2016. "How should economists model climate? Tipping points and nonlinear dynamics of carbon dioxide concentrations," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 56-65.
    14. Neil Foster-McGregor & Anders Isaksson & Florian Kaulich, 2013. "Importing, Productivity and Absorptive Capacity in Sub-Saharan African Manufacturing Firms," wiiw Working Papers 105, The Vienna Institute for International Economic Studies, wiiw.
    15. Tae-Hwan Kim & Dong Jin Lee & Paul Mizen, 2020. "Impulse Response Analysis in Conditional Quantile Models and an Application to Monetary Policy," Working papers 2020rwp-164, Yonsei University, Yonsei Economics Research Institute.
    16. Christis Katsouris, 2023. "Estimation and Inference in Threshold Predictive Regression Models with Locally Explosive Regressors," Papers 2305.00860, arXiv.org, revised May 2023.
    17. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    18. Montes-Rojas, Gabriel, 2017. "Reduced form vector directional quantiles," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 20-30.

  42. Escanciano, J. Carlos & Olmo, Jose, 2010. "Backtesting Parametric Value-at-Risk With Estimation Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 36-51.
    See citations under working paper version above.
  43. Olmo, Jose & Pilbeam, Keith, 2009. "Uncovered Interest Parity: Are Empirical Rejections of It Valid?," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 24, pages 369-384.

    Cited by:

    1. Bhatti, Razzaque H., 2014. "The existence of uncovered interest parity in the CIS countries," Economic Modelling, Elsevier, vol. 40(C), pages 227-241.

  44. Jose Olmo & Keith Pilbeam, 2009. "The profitability of carry trades," Annals of Finance, Springer, vol. 5(2), pages 231-241, March.

    Cited by:

    1. Keith Pilbeam & Jose Olmo, 2011. "The forward discount puzzle and market efficiency," Annals of Finance, Springer, vol. 7(1), pages 119-135, February.
    2. Vistesen, Claus, 2008. "Of Low Yielders and Carry Trading – the JPY and CHF as Market Risk Sentiment Gauges," MPRA Paper 9952, University Library of Munich, Germany.
    3. Claus VISTESEN, 2009. "Carry Trade Fundamentals And The Financial Crisis 2007-2010," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 4(2(8)_ Sum).

  45. Jose Olmo, 2008. "On the role of volatility for modelling risk exposure," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 1(2), pages 219-234.

    Cited by:

    1. Lambert, Philippe & Laurent, Sébastien & Veredas, David, 2012. "Testing conditional asymmetry: A residual-based approach," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1229-1247.

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