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Emese Lazar

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. Carol Alexander & Emese Lazar & Silvia Stanescu, 2018. "Analytic Moments for GARCH Processes," Papers 1808.09666, arXiv.org, revised Sep 2018.

    Cited by:

    1. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2013. "Forecasting VaR using analytic higher moments for GARCH processes," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 36-45.
    2. Carol Alexander & Emese Lazar & Silvia Stanescu, 2011. "Analytic Approximations to GARCH Aggregated Returns Distributions with Applications to VaR and ETL," ICMA Centre Discussion Papers in Finance icma-dp2011-08, Henley Business School, University of Reading.

  2. 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.

    Cited by:

    1. Taylor, James W., 2022. "Forecasting Value at Risk and expected shortfall using a model with a dynamic omega ratio," Journal of Banking & Finance, Elsevier, vol. 140(C).
    2. Banulescu-Radu, Denisa & Hurlin, Christophe & Leymarie, Jeremy & Scaillet, Olivier, 2020. "Backtesting marginal expected shortfalland related systemic risk measures," Working Papers unige:134136, University of Geneva, Geneva School of Economics and Management.
    3. Ning Zhang & Yujing Gong & Xiaohan Xue, 2023. "Less disagreement, better forecasts: Adjusted risk measures in the energy futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(10), pages 1332-1372, October.
    4. Michael Grabchak & Eliana Christou, 2021. "A note on calculating expected shortfall for discrete time stochastic volatility models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-16, December.
    5. d’Addona, Stefano & Khanom, Najrin, 2022. "Estimating tail-risk using semiparametric conditional variance with an application to meme stocks," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 241-260.
    6. Li, Dan & Clements, Adam & Drovandi, Christopher, 2023. "A Bayesian approach for more reliable tail risk forecasts," Journal of Financial Stability, Elsevier, vol. 64(C).
    7. Hammadi Zouari, 2022. "On the Effectiveness of Stock Index Futures for Tail Risk Protection," International Journal of Economics and Financial Issues, Econjournals, vol. 12(3), pages 38-52, May.

  3. Avino, Davide & Lazar, Emese & Varotto, Simone, 2012. "Which market drives credit spreads in tranquil and crisis periods? An analysis of the contribution to price discovery of bonds, CDS, stocks and options," MPRA Paper 56781, University Library of Munich, Germany.

    Cited by:

    1. Abid, Ilyes & Dhaoui, Abderrazak & Goutte, Stéphane & Guesmi, Khaled, 2019. "Contagion and bond pricing: The case of the ASEAN region," Research in International Business and Finance, Elsevier, vol. 47(C), pages 371-385.
    2. Mariya Paskaleva & Ani Stoitsova-Stoykova, 2017. "Linkages and Efficiency Between iTraxx Europe and Financial Market Dynamics in South-East Europe Capital Markets in Post-crisis Period," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 172-179.

  4. Avino, Davide & Lazar, Emese & Varotto, Simone, 2012. "Price Discovery of Credit Spreads in Tranquil and Crisis Periods," MPRA Paper 42847, University Library of Munich, Germany.

    Cited by:

    1. Narayan, Paresh Kumar, 2015. "An analysis of sectoral equity and CDS spreads," Working Papers fe_2015_02, Deakin University, Department of Economics.
    2. Jitmaneeroj, Boonlert, 2018. "Is Thailand’s credit default swap market linked to bond and stock markets? Evidence from the term structure of credit spreads," Research in International Business and Finance, Elsevier, vol. 46(C), pages 324-341.
    3. Griffin, Paul A. & Lont, David H., 2018. "Game changer? The impact of the VW emission-cheating scandal on the interrelation between large automakers’ equity and credit markets," Journal of Contemporary Accounting and Economics, Elsevier, vol. 14(2), pages 179-196.
    4. Shahzad, Syed Jawad Hussain & Nor, Safwan Mohd & Hammoudeh, Shawkat & Shahbaz, Muhammad, 2017. "Directional and bidirectional causality between U.S. industry credit and stock markets and their determinants," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 46-61.
    5. Avino, Davide & Lazar, Emese & Varotto, Simone, 2015. "Time varying price discovery," Economics Letters, Elsevier, vol. 126(C), pages 18-21.
    6. Seema Narayan & Russell Smyth, 2015. "The Financial Econometrics of Price Discovery and Predictability," Monash Economics Working Papers 06-15, Monash University, Department of Economics.
    7. Batten, Jonathan A. & Jacoby, Gady & Liao, Rose C., 2014. "Corporate yield spreads and real interest rates," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 89-100.
    8. Gehde-Trapp, Monika & Gündüz, Yalin & Nasev, Julia, 2015. "The liquidity premium in CDS transaction prices: Do frictions matter?," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 184-205.
    9. Tran, Vu & Alsakka, Rasha & ap Gwilym, Owain, 2014. "Sovereign rating actions and the implied volatility of stock index options," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 101-113.
    10. Shahzad, Syed Jawad Hussain & Mensi, Walid & Hammoudeh, Shawkat & Balcilar, Mehmet & Shahbaz, Muhammad, 2018. "Distribution specific dependence and causality between industry-level U.S. credit and stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 114-133.
    11. Lamia Bekkour & Thorsten Lehnert & Maria Chiara Amadori, 2011. "The Relative Informational Efficiency of Stocks, Options and Credit Default Swaps," LSF Research Working Paper Series 11-13, Luxembourg School of Finance, University of Luxembourg.

  5. Avino, Davide & Lazar, Emese, 2012. "Rethinking Capital Structure Arbitrage," MPRA Paper 42850, University Library of Munich, Germany.

    Cited by:

    1. Robert Goldberg, 2015. "A methodology for computing and comparing implied equity and corporate-debt Sharpe Ratios," Review of Quantitative Finance and Accounting, Springer, vol. 44(4), pages 733-754, May.
    2. Byström, Hans, 2013. "Stock Prices and Stock Return Volatilities Implied by the Credit Market," Working Papers 2013:25, Lund University, Department of Economics, revised 14 Feb 2014.

  6. Symeonidis, Lazaros & Prokopczuk, Marcel & Brooks, Chris & Lazar, Emese, 2012. "Futures basis, inventory and commodity price volatility: An empirical analysis," MPRA Paper 39903, University Library of Munich, Germany.

    Cited by:

    1. Yao, Wei & Alexiou, Constantinos, 2022. "Exploring the transmission mechanism of speculative and inventory arbitrage activity to commodity price volatility. Novel evidence for the US economy," International Review of Financial Analysis, Elsevier, vol. 80(C).
    2. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2020. "Does sophistication of the weighting scheme enhance the performance of long-short commodity portfolios?," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 164-180.
    3. Adrian Fernandez-Perez & Ana-Maria Fuertes & Joelle Miffre, 2023. "The Negative Pricing of the May 2020 WTI Contract," Post-Print hal-03933797, HAL.
    4. Sévi, Benoît, 2015. "Explaining the convenience yield in the WTI crude oil market using realized volatility and jumps," Economic Modelling, Elsevier, vol. 44(C), pages 243-251.
    5. Adhikari, Ramesh & Putnam, Kyle J., 2020. "Comovement in the commodity futures markets: An analysis of the energy, grains, and livestock sectors," Journal of Commodity Markets, Elsevier, vol. 18(C).
    6. Niaz Bashiri Behmiri, Maryam Ahmadi, Juha-Pekka Junttila, and Matteo Manera, 2021. "Financial Stress and Basis in Energy Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    7. Maryam Ahmadi & Niaz Bashiri Behmiri & Matteo Manera, 2020. "The theory of storage in the crude oil futures market, the role of financial conditions," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1160-1175, July.
    8. Ge, Yiqing & Tang, Ke, 2020. "Commodity prices and GDP growth," International Review of Financial Analysis, Elsevier, vol. 71(C).
    9. Couleau, Anabelle & Trujillo-Barrera, Andres A. & Etienne, Xiaoli L., 2023. "Dynamic Market Momentum: The case of Intraday Coffee Futures Prices," 2023 Annual Meeting, July 23-25, Washington D.C. 335655, Agricultural and Applied Economics Association.
    10. Chiu, Yuan-Shyi Peter & Chang, Huei-Hsin, 2014. "Optimal run time for EPQ model with scrap, rework and stochastic breakdowns: A note," Economic Modelling, Elsevier, vol. 37(C), pages 143-148.
    11. Kim, Soohyeon & Kim, Jihyo & Heo, Eunnyeong, 2017. "Convenience yield of accessible inventories and imports: A case study of the Chinese copper market," Resources Policy, Elsevier, vol. 52(C), pages 277-283.
    12. Floros, Christos & Salvador, Enrique, 2014. "Calendar anomalies in cash and stock index futures: International evidence," Economic Modelling, Elsevier, vol. 37(C), pages 216-223.
    13. Suleyman Degirmen & Omur Saltik, 2017. "Impacts of realized volatility of oil price over foreign trade related activities in Turkey," Economic Change and Restructuring, Springer, vol. 50(3), pages 193-209, August.
    14. John T. Cuddington & Arturo L. Va'squez Cordano, 2013. "Linkages between spot and futures prices: Tests of the Fama-French-Samuelson hypotheses," Working Papers 2013-09, Colorado School of Mines, Division of Economics and Business.
    15. Santeramo, Fabio Gaetano & Lamonaca, Emilia & Contò, Francesco & Stasi, Antonio & Nardone, Gianluca, 2017. "Drivers of grain price volatility: a cursory critical review," MPRA Paper 79427, University Library of Munich, Germany.
    16. Baur, Dirk G. & Dimpfl, Thomas, 2018. "The asymmetric return-volatility relationship of commodity prices," Energy Economics, Elsevier, vol. 76(C), pages 378-387.
    17. Boos, Dominik & Grob, Linus, 2023. "Tracking speculative trading," Journal of Financial Markets, Elsevier, vol. 64(C).
    18. Fernandez-Perez, Adrian & Frijns, Bart & Fuertes, Ana-Maria & Miffre, Joelle, 2018. "The skewness of commodity futures returns," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 143-158.
    19. Raphaël Chiappini & Yves Jégourel, 2014. "Futures Market Volatility, Exchange Rate Uncertainty and Cereals Exports: Empirical Evidence from France," GREDEG Working Papers 2014-34, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    20. Stefan Ederer & Christine Heumesser & Cornelia Staritz, 2016. "Financialization and commodity prices -- an empirical analysis for coffee, cotton, wheat and oil," International Review of Applied Economics, Taylor & Francis Journals, vol. 30(4), pages 462-487, July.
    21. Oliveira, Sydnei Marssal de & Ribeiro, Celma de Oliveira & Cicogna, Maria Paula Vieira, 2018. "Uncertainty effects on production mix and on hedging decisions: The case of Brazilian ethanol and sugar," Energy Economics, Elsevier, vol. 70(C), pages 516-524.
    22. Christina Sklibosios Nikitopoulos & Alice Carole Thomas & Jianxin Wang, 2024. "Hedging pressure and oil volatility: Insurance versus liquidity demands," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 252-280, February.
    23. Karapanagiotidis, Paul, 2014. "Dynamic modeling of commodity futures prices," MPRA Paper 56805, University Library of Munich, Germany.
    24. Manuel Ammann & Mathis Moerke & Marcel Prokopczuk & Christoph Matthias Würsig, 2023. "Commodity tail risks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(2), pages 168-197, February.
    25. Hollstein, Fabian & Prokopczuk, Marcel & Tharann, Björn & Wese Simen, Chardin, 2021. "Predictability in commodity markets: Evidence from more than a century," Journal of Commodity Markets, Elsevier, vol. 24(C).
    26. Boda Kang & Christina Sklibosios Nikitopoulos & Marcel Prokopczuk, 2019. "Economic Determinants of Oil Futures Volatility: A Term Structure Perspective," Research Paper Series 401, Quantitative Finance Research Centre, University of Technology, Sydney.
    27. Chun, Dohyun & Cho, Hoon & Kim, Jihun, 2019. "Crude oil price shocks and hedging performance: A comparison of volatility models," Energy Economics, Elsevier, vol. 81(C), pages 1132-1147.
    28. Haase, Marco & Zimmermann, Heinz & Huss, Matthias, 2023. "Wheat price volatility regimes over 140 years: An analysis of daily price ranges," Journal of Commodity Markets, Elsevier, vol. 31(C).
    29. Zaremba, Adam & Bianchi, Robert J. & Mikutowski, Mateusz, 2021. "Long-run reversal in commodity returns: Insights from seven centuries of evidence," Journal of Banking & Finance, Elsevier, vol. 133(C).
    30. Fouquau, Julien & Six, Pierre, 2015. "A comparison of the convenience yield and interest-adjusted basis," Finance Research Letters, Elsevier, vol. 14(C), pages 142-149.
    31. Bredin, Don & O'Sullivan, Conall & Spencer, Simon, 2021. "Forecasting WTI crude oil futures returns: Does the term structure help?," Energy Economics, Elsevier, vol. 100(C).
    32. Adrian, Fernandez-Perez & Ana-Maria, Fuertes & Joelle, Miffre, 2022. "The Negative Pricing of the May 2020 WTI Contract," MPRA Paper 112352, University Library of Munich, Germany, revised 20 Dec 2021.
    33. Dominik Boos, 2024. "Risky times: Seasonality and event risk of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(5), pages 767-783, May.
    34. Ederer, Stefan & Heumesser, Christine & Staritz, Cornelia, 2013. "The role of fundamentals and financialisation in recent commodity price developments: An empirical analysis for wheat, coffee, cotton, and oil," Working Papers 42, Austrian Foundation for Development Research (ÖFSE).
    35. Marek Kwas & Michał Rubaszek, 2021. "Forecasting Commodity Prices: Looking for a Benchmark," Forecasting, MDPI, vol. 3(2), pages 1-13, June.
    36. Weerawich Roekchamnong & Pongsa Pornchaiwiseskul & Anant Chiarawongse, 2014. "The Effects of Uncertainties on Inventory Management of Petroleum Products: A Case Study of Thailand," International Journal of Energy Economics and Policy, Econjournals, vol. 4(3), pages 380-390.
    37. Nicolas Merener, 2016. "Concentrated Production and Conditional Heavy Tails in Commodity Returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(1), pages 46-65, January.
    38. Selma Izadi & M. Kabir Hassan, 2018. "Portfolio and hedging effectiveness of financial assets of the G7 countries," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 8(2), pages 183-213, August.
    39. Yu, Dan & Chen, Chuang & Wang, Yudong & Zhang, Yaojie, 2023. "Hedging pressure momentum and the predictability of oil futures returns," Economic Modelling, Elsevier, vol. 121(C).
    40. 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).
    41. Miffre, Joëlle, 2016. "Long-short commodity investing: A review of the literature," Journal of Commodity Markets, Elsevier, vol. 1(1), pages 3-13.

  7. Carol Alexander & Emese Lazar, 2008. "Markov Switching GARCH Diffusion," ICMA Centre Discussion Papers in Finance icma-dp2008-01, Henley Business School, University of Reading.

    Cited by:

    1. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    2. Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.

  8. Carol Alexandra & Emese Lazar, 2005. "Asymmetries and Volatility Regimes in the European Equity Markets," ICMA Centre Discussion Papers in Finance icma-dp2005-14, Henley Business School, University of Reading.

    Cited by:

    1. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2008. "Asymmetric multivariate normal mixture GARCH," CFS Working Paper Series 2008/07, Center for Financial Studies (CFS).
    2. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).
    3. Badescu Alex & Kulperger Reg & Lazar Emese, 2008. "Option Valuation with Normal Mixture GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(2), pages 1-42, May.
    4. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
    5. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2006. "Multivariate normal mixture GARCH," CFS Working Paper Series 2006/09, Center for Financial Studies (CFS).

  9. Carol Alexandra & Emese Lazar, 2005. "On The Continuous Limit of GARCH," ICMA Centre Discussion Papers in Finance icma-dp2005-13, Henley Business School, University of Reading.

    Cited by:

    1. Christian M. Hafner & Sébastien Laurent & Francesco Violante, 2017. "Weak Diffusion Limits of Dynamic Conditional Correlation Models," Post-Print hal-01590010, HAL.
    2. Buccheri, Giuseppe & Corsi, Fulvio & Flandoli, Franco & Livieri, Giulia, 2021. "The continuous-time limit of score-driven volatility models," Journal of Econometrics, Elsevier, vol. 221(2), pages 655-675.
    3. Kallsen, Jan & Vesenmayer, Bernhard, 2009. "COGARCH as a continuous-time limit of GARCH(1,1)," Stochastic Processes and their Applications, Elsevier, vol. 119(1), pages 74-98, January.
    4. Badescu, Alexandru & Elliott, Robert J. & Ortega, Juan-Pablo, 2015. "Non-Gaussian GARCH option pricing models and their diffusion limits," European Journal of Operational Research, Elsevier, vol. 247(3), pages 820-830.
    5. Bali, Turan G. & Wu, Liuren, 2006. "A comprehensive analysis of the short-term interest-rate dynamics," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1269-1290, April.
    6. Rodrigo Alfaro & Natán Golberger, 2013. "The Impact of Persistence in Volatility over the Probability of Default," Working Papers Central Bank of Chile 689, Central Bank of Chile.

  10. Carol Alexandra & Emese Lazar, 2004. "The Equity Index Skew, Market Crashes and Asymmetric Normal Mixture GARCH," ICMA Centre Discussion Papers in Finance icma-dp2004-13, Henley Business School, University of Reading.

    Cited by:

    1. Xi, Yanhui & Peng, Hui & Qin, Yemei & Xie, Wenbiao & Chen, Xiaohong, 2015. "Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 117(C), pages 141-153.
    2. Emese Lazar & Carol Alexander, 2006. "Normal mixture GARCH(1,1): applications to exchange rate modelling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 307-336.
    3. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
    4. Goel, Anubha & Sharma, Amita, 2020. "Mixed value-at-risk and its numerical investigation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    5. Muhammad Ahsanuddin & Tayyab Raza Fraz & Samreen Fatima, 2019. "Studying the Volatility of Pakistan Stock Exchange and Shanghai Stock Exchange Markets in the Light of CPEC: An Application of GARCH and EGARCH Modelling," International Journal of Sciences, Office ijSciences, vol. 8(03), pages 125-132, March.
    6. Giannikis, D. & Vrontos, I.D. & Dellaportas, P., 2008. "Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1549-1571, January.

  11. Carol Alexandra & Emese Lazar, 2004. "Normal Mixture GARCH (1,1): Application to Exchange Rate Modelling," ICMA Centre Discussion Papers in Finance icma-dp2004-05, Henley Business School, University of Reading.

    Cited by:

    1. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    2. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02505861, HAL.
    3. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2008. "Asymmetric multivariate normal mixture GARCH," CFS Working Paper Series 2008/07, Center for Financial Studies (CFS).
    4. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    5. Wang, Xinyu & Qi, Zikang & Huang, Jianglu, 2023. "How do monetary shock, financial crisis, and quotation reform affect the long memory of exchange rate volatility? Evidence from major currencies," Economic Modelling, Elsevier, vol. 120(C).
    6. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    7. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    8. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc, 2006. "Accurate value-at-risk forecasting based on the normal-GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2295-2312, December.
    9. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Post-Print halshs-02505861, HAL.
    10. Paolella, Marc S. & Taschini, Luca, 2008. "An econometric analysis of emission allowance prices," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2022-2032, October.
    11. Maria Eugenia Sanin & Francesco Violante & Maria Mansanet-Bataller, 2015. "Understanding volatility dynamics in the EU-ETS market," Post-Print hal-02878047, HAL.
    12. Comte, Fabienne & Kappus, Johanna, 2015. "Density deconvolution from repeated measurements without symmetry assumption on the errors," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 31-46.
    13. BOUADDI, Mohammed & ROMBOUTS, Jeroen V.K., 2007. "Mixed exponential power asymmetric conditional heteroskedasticity," LIDAM Discussion Papers CORE 2007097, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Chlebus Marcin, 2017. "EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk," Central European Economic Journal, Sciendo, vol. 3(50), pages 01-25, December.
    15. Markus Haas & Stefan Mittnik & Bruce Mizrach, 2004. "Assessing Central Bank Credibility During the EMS Crises: Comparing Option and Spot Market-Based Forecasts," Departmental Working Papers 200424, Rutgers University, Department of Economics.
    16. Haas, Markus & Mittnik, Stefan, 2008. "Multivariate regimeswitching GARCH with an application to international stock markets," CFS Working Paper Series 2008/08, Center for Financial Studies (CFS).
    17. Xi, Yanhui & Peng, Hui & Qin, Yemei & Xie, Wenbiao & Chen, Xiaohong, 2015. "Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 117(C), pages 141-153.
    18. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2014. "Testing for Leverage Effects in the Returns of US Equities," Documents de travail du Centre d'Economie de la Sorbonne 14022r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Jan 2017.
    19. Badescu Alex & Kulperger Reg & Lazar Emese, 2008. "Option Valuation with Normal Mixture GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(2), pages 1-42, May.
    20. Li, Dan & Clements, Adam & Drovandi, Christopher, 2021. "Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 22-46.
    21. Nomikos, Nikos K. & Pouliasis, Panos K., 2011. "Forecasting petroleum futures markets volatility: The role of regimes and market conditions," Energy Economics, Elsevier, vol. 33(2), pages 321-337, March.
    22. Kuang-Liang Chang, 2011. "The optimal value-at-risk hedging strategy under bivariate regime switching ARCH framework," Applied Economics, Taylor & Francis Journals, vol. 43(21), pages 2627-2640.
    23. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
    24. Ausin, Maria Concepcion & Galeano, Pedro, 2007. "Bayesian estimation of the Gaussian mixture GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2636-2652, February.
    25. Pertaia Giorgi & Uryasev Stan, 2019. "Fitting heavy-tailed mixture models with CVaR constraints," Dependence Modeling, De Gruyter, vol. 7(1), pages 365-374, January.
    26. Ghassan, Hassan Belkacem & AlHajhoj, Hassan Rafdan, 2016. "Long run dynamic volatilities between OPEC and non-OPEC crude oil prices," Applied Energy, Elsevier, vol. 169(C), pages 384-394.
    27. de Souza Vasconcelos, Camila & Hadad Júnior, Eli, 2023. "Forecasting exchange rate: A bibliometric and content analysis," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 607-628.
    28. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
    29. Wang, Xi & Yang, Jiao-Hui & Wang, Kai-Li & Fawson, Christopher, 2017. "Dynamic information spillovers in intraregionally-focused spot and forward currency markets," Journal of International Money and Finance, Elsevier, vol. 71(C), pages 78-110.
    30. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2012. "Option Pricing for GARCH-type Models with Generalized Hyperbolic Innovations," PSE-Ecole d'économie de Paris (Postprint) hal-00511965, HAL.
    31. Cheung, Yin-Wong & Chung, Sang-Kuck, 2009. "A Long Memory Model with Mixed Normal GARCH for US Inflation Data," Santa Cruz Department of Economics, Working Paper Series qt2202s99q, Department of Economics, UC Santa Cruz.
    32. Bruno Solnik & Thaisiri Watewai, 2016. "International Correlation Asymmetries: Frequent-but-Small and Infrequent-but-Large Equity Returns," PIER Discussion Papers 31, Puey Ungphakorn Institute for Economic Research.
    33. Marc S. Paolella, 2016. "Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability," Econometrics, MDPI, vol. 4(2), pages 1-28, May.
    34. Guglielmo Maria Caporale & Timur Zekokh, 2018. "Modelling Volatility of Cryptocurrencies Using Markov-Switching Garch Models," CESifo Working Paper Series 7167, CESifo.
    35. Maciej Augustyniak & Mathieu Boudreault & Manuel Morales, 2018. "Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure," Methodology and Computing in Applied Probability, Springer, vol. 20(1), pages 165-188, March.
    36. Ñíguez, Trino-Manuel & Perote, Javier, 2016. "Multivariate moments expansion density: Application of the dynamic equicorrelation model," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 216-232.
    37. Jochen Krause & Marc S. Paolella, 2014. "A Fast, Accurate Method for Value-at-Risk and Expected Shortfall," Econometrics, MDPI, vol. 2(2), pages 1-25, June.
    38. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2010. "Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes," Post-Print halshs-00523371, HAL.
    39. Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    40. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01442618, HAL.
    41. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2017. "Testing for Leverage Effects in the Returns of US Equities," Post-Print halshs-00973922, HAL.
    42. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2012. "Option Pricing for GARCH-type Models with Generalized Hyperbolic Innovations," Post-Print hal-00511965, HAL.
    43. Ñíguez, Trino-Manuel & Perote, Javier, 2017. "Moments expansion densities for quantifying financial risk," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 53-69.
    44. 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.
    45. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 517-539, November.
    46. Maddalena Cavicchioli, 2021. "Statistical inference for mixture GARCH models with financial application," Computational Statistics, Springer, vol. 36(4), pages 2615-2642, December.
    47. Rubing Liang & Binbin Qin & Qiang Xia, 2024. "Bayesian Inference for Mixed Gaussian GARCH-Type Model by Hamiltonian Monte Carlo Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 193-220, January.
    48. Pouliasis, Panos K. & Papapostolou, Nikos C. & Kyriakou, Ioannis & Visvikis, Ilias D., 2018. "Shipping equity risk behavior and portfolio management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 178-200.
    49. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2010. "Option pricing for GARCH-type models with generalized hyperbolic innovations," Post-Print halshs-00469529, HAL.
    50. Atilla Çifter & Alper Özün, 2007. "The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 1(1), pages 7-34.
    51. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    52. Rita Laura D’Ecclesia & Daniele Clementi, 2021. "Volatility in the stock market: ANN versus parametric models," Annals of Operations Research, Springer, vol. 299(1), pages 1101-1127, April.
    53. Tanattrin Bunnag, 2015. "Hedging Petroleum Futures with Multivariate GARCH Models," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 105-120.
    54. Leonidas Tsiaras, 2010. "Dynamic Models of Exchange Rate Dependence Using Option Prices and Historical Returns," CREATES Research Papers 2010-35, Department of Economics and Business Economics, Aarhus University.
    55. Carol Alexandra & Emese Lazar, 2005. "The Continuous Limit of GARCH Processess," ICMA Centre Discussion Papers in Finance icma-dp2004-09, Henley Business School, University of Reading, revised Jul 2004.
    56. Mohamed Osman, 2015. "Dynamic Asymmetries in the Electric Consumption of the GCC Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 5(2), pages 461-467.
    57. Ames, Matthew & Bagnarosa, Guillaume & Peters, Gareth W., 2017. "Violations of uncovered interest rate parity and international exchange rate dependences," Journal of International Money and Finance, Elsevier, vol. 73(PA), pages 162-187.
    58. Jeffrey Chu & Saralees Nadarajah & Stephen Chan, 2015. "Statistical Analysis of the Exchange Rate of Bitcoin," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-27, July.
    59. Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Time-varying mixture GARCH models and asymmetric volatility," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 602-623.
    60. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    61. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 320-338.
    62. Wu, C.C. & Lee, Jack C., 2007. "Estimation of a utility-based asset pricing model using normal mixture GARCH(1,1)," Economic Modelling, Elsevier, vol. 24(2), pages 329-349, March.
    63. Yang Zhang & Yidong Peng & Xiuli Qu & Jing Shi & Ergin Erdem, 2021. "A Finite Mixture GARCH Approach with EM Algorithm for Energy Forecasting Applications," Energies, MDPI, vol. 14(9), pages 1-22, April.
    64. 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.
    65. Slim, Skander & Koubaa, Yosra & BenSaïda, Ahmed, 2017. "Value-at-Risk under Lévy GARCH models: Evidence from global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 30-53.
    66. Pedro Correia S. Bezerra & Pedro Henrique M. Albuquerque, 2017. "Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels," Computational Management Science, Springer, vol. 14(2), pages 179-196, April.
    67. Giannikis, D. & Vrontos, I.D. & Dellaportas, P., 2008. "Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1549-1571, January.
    68. Hassan Ghassan & Prashanta Banerjee, 2015. "A threshold cointegration analysis of asymmetric adjustment of OPEC and non-OPEC monthly crude oil prices," Empirical Economics, Springer, vol. 49(1), pages 305-323, August.
    69. Chikashi Tsuji, 2016. "Does the fear gauge predict downside risk more accurately than econometric models? Evidence from the US stock market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1220711-122, December.
    70. Bernard Njindan Iyke, 2019. "A Test Of The Efficiency Of The Foreign Exchange Market In Indonesia," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 0(12th BMEB), pages 1-26, January.
    71. Chevallier, Julien & Ielpo, Florian, 2017. "Investigating the leverage effect in commodity markets with a recursive estimation approach," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 763-778.
    72. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2014. "Testing for Leverage Effect in Financial Returns," Documents de travail du Centre d'Economie de la Sorbonne 14022, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    73. Chung, Sang-Kuck, 2009. "Bivariate mixed normal GARCH models and out-of-sample hedge performances," Finance Research Letters, Elsevier, vol. 6(3), pages 130-137, September.

  12. Carol Alexandra & Emese Lazar, 2003. "Symmetric Normal Mixture GARCH," ICMA Centre Discussion Papers in Finance icma-dp2003-09, Henley Business School, University of Reading.

    Cited by:

    1. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
    2. Cifter, Atilla, 2012. "Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 127-142, June.

Articles

  1. Lazar, Emese & Qi, Shuyuan, 2022. "Model risk in the over-the-counter market," European Journal of Operational Research, Elsevier, vol. 298(2), pages 769-784.

    Cited by:

    1. Zhang, Ning & Su, Xiaoman & Qi, Shuyuan, 2023. "An empirical investigation of multiperiod tail risk forecasting models," International Review of Financial Analysis, Elsevier, vol. 86(C).

  2. Yushuang Jiang & Emese Lazar, 2022. "Forecasting VIX Using Filtered Historical Simulation [A GARCH Option Pricing Model with Filtered Historical Simulation]," Journal of Financial Econometrics, Oxford University Press, vol. 20(4), pages 655-680.

    Cited by:

    1. Wu, Xinyu & Zhao, An & Liu, Li, 2023. "Forecasting VIX using two-component realized EGARCH model," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).

  3. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.

    Cited by:

    1. Carnero, M. Angeles & León, Angel & Ñíguez, Trino-Manuel, 2023. "Skewness in energy returns: estimation, testing and retain-->implications for tail risk," The Quarterly Review of Economics and Finance, Elsevier, vol. 90(C), pages 178-189.
    2. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    3. Chen, James Ming & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning," Resources Policy, Elsevier, vol. 73(C).

  4. 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.

    Cited by:

    1. Cathy W. S. Chen & Takaaki Koike & Wei-Hsuan Shau, 2024. "Tail risk forecasting with semi-parametric regression models by incorporating overnight information," Papers 2402.07134, arXiv.org.
    2. Chen, Cathy W.S. & Hsu, Hsiao-Yun & Watanabe, Toshiaki, 2023. "Tail risk forecasting of realized volatility CAViaR models," Finance Research Letters, Elsevier, vol. 51(C).
    3. Michel Ferreira Cardia Haddad & Szabolcs Blazsek & Philip Arestis & Franz Fuerst & Hsia Hua Sheng, 2023. "The two-component Beta-t-QVAR-M-lev: a new forecasting model," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 379-401, December.
    4. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.
    5. Deniz Erer, 2023. "The Impact of News Related Covid-19 on Exchange Rate Volatility:A New Evidence From Generalized Autoregressive Score Model," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(38), pages 105-126, June.
    6. Kuang, Wei, 2022. "The economic value of high-frequency data in equity-oil hedge," Energy, Elsevier, vol. 239(PA).
    7. Zaevski, Tsvetelin S. & Nedeltchev, Dragomir C., 2023. "From BASEL III to BASEL IV and beyond: Expected shortfall and expectile risk measures," International Review of Financial Analysis, Elsevier, vol. 87(C).
    8. Man Wang & Yihan Cheng, 2022. "Forecasting value at risk and expected shortfall using high‐frequency data of domestic and international stock markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1595-1607, December.
    9. Enilov, Martin & Mensi, Walid & Stankov, Petar, 2023. "Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic," Journal of Commodity Markets, Elsevier, vol. 29(C).
    10. Vincenzo Candila & Giampiero M. Gallo & Lea Petrella, 2020. "Mixed--frequency quantile regressions to forecast Value--at--Risk and Expected Shortfall," Papers 2011.00552, arXiv.org, revised Mar 2023.
    11. Zhang, Ning & Su, Xiaoman & Qi, Shuyuan, 2023. "An empirical investigation of multiperiod tail risk forecasting models," International Review of Financial Analysis, Elsevier, vol. 86(C).
    12. 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.

  5. Lazar, Emese & Zhang, Ning, 2019. "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 74-93.
    See citations under working paper version above.
  6. Avino, Davide & Lazar, Emese & Varotto, Simone, 2015. "Time varying price discovery," Economics Letters, Elsevier, vol. 126(C), pages 18-21.

    Cited by:

    1. Ivan Indriawan & Feng Jiao & Yiuman Tse, 2019. "The impact of the US stock market opening on price discovery of government bond futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(7), pages 779-802, July.
    2. Massimo Guidolin & Manuela Pedio & Alessandra tosi, 2019. "Time-Varying Price Discovery in Sovereign Credit Markets," BAFFI CAREFIN Working Papers 19120, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    3. Hong Li & Yanlin Shi, 2022. "Robust information share measures with an application on the international crude oil markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 555-579, April.
    4. 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.
    5. Griffin, Paul A. & Lont, David H., 2018. "Game changer? The impact of the VW emission-cheating scandal on the interrelation between large automakers’ equity and credit markets," Journal of Contemporary Accounting and Economics, Elsevier, vol. 14(2), pages 179-196.
    6. Hou, Yang & Li, Steven & Wen, Fenghua, 2019. "Time-varying volatility spillover between Chinese fuel oil and stock index futures markets based on a DCC-GARCH model with a semi-nonparametric approach," Energy Economics, Elsevier, vol. 83(C), pages 119-143.
    7. Hou, Yang & Nartea, Gilbert, 2017. "Price Discovery in the Stock Index Futures Market: Evidence from the Chinese stock market crash," MPRA Paper 81995, University Library of Munich, Germany.
    8. Yang Hou & Steven Li & Fenghua Wen, 2021. "Time-varying information share and autoregressive loading factors: evidence from S&P 500 cash and E-mini futures markets," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 91-110, July.
    9. Bohl, Martin T. & Gross, Christian & Souza, Waldemar, 2019. "The role of emerging economies in the global price formation process of commodities: Evidence from Brazilian and U.S. coffee markets," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 203-215.
    10. 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).
    11. Seema Narayan & Russell Smyth, 2015. "The Financial Econometrics of Price Discovery and Predictability," Monash Economics Working Papers 06-15, Monash University, Department of Economics.
    12. Li, Hong & Shi, Yanlin, 2021. "A new unique information share measure with applications on cross-listed Chinese banks," Journal of Banking & Finance, Elsevier, vol. 128(C).
    13. Hou, Yang & Li, Steven, 2017. "Time-Varying Price Discovery and Autoregressive Loading Factors: Evidence from S&P 500 Cash and E-Mini Futures Markets," MPRA Paper 81999, University Library of Munich, Germany.
    14. Donald Lien & Ziling Wang & Xiaojian Yu, 2021. "Quantile information share under Markov regime‐switching," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(4), pages 493-513, April.
    15. Kim, Jaeho & Linn, Scott C., 2022. "Price discovery under model uncertainty," Energy Economics, Elsevier, vol. 107(C).
    16. Ahmed, Osama, 2021. "Assessing the current situation of the world wheat market leadership: Using the semi-parametric approach," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 9(2).

  7. Avino, Davide & Lazar, Emese & Varotto, Simone, 2013. "Price discovery of credit spreads in tranquil and crisis periods," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 242-253.
    See citations under working paper version above.
  8. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2013. "Forecasting VaR using analytic higher moments for GARCH processes," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 36-45.

    Cited by:

    1. Ding, Shusheng & Cui, Tianxiang & Zhang, Yongmin, 2022. "Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect," International Review of Financial Analysis, Elsevier, vol. 83(C).
    2. Tsuji, Chikashi, 2020. "Correlation and spillover effects between the US and international banking sectors: New evidence and implications for risk management," International Review of Financial Analysis, Elsevier, vol. 70(C).
    3. Li, Jianping & Li, Jingyu & Zhu, Xiaoqian & Yao, Yinhong & Casu, Barbara, 2020. "Risk spillovers between FinTech and traditional financial institutions: Evidence from the U.S," International Review of Financial Analysis, Elsevier, vol. 71(C).
    4. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.
    5. Duan, Kun & Liu, Yang & Yan, Cheng & Huang, Yingying, 2023. "Differences in carbon risk spillovers with green versus traditional assets: Evidence from a full distributional analysis," Energy Economics, Elsevier, vol. 127(PA).
    6. Wei Kuang, 2022. "Oil tail-risk forecasts: from financial crisis to COVID-19," Risk Management, Palgrave Macmillan, vol. 24(4), pages 420-460, December.
    7. Zhang, Ning & Su, Xiaoman & Qi, Shuyuan, 2023. "An empirical investigation of multiperiod tail risk forecasting models," International Review of Financial Analysis, Elsevier, vol. 86(C).
    8. Chunyang Zhou & Xiao Qin & Xundi Diao & Yingchen He, 2016. "Estimating multi-period Value at Risk of oil futures prices," Applied Economics, Taylor & Francis Journals, vol. 48(32), pages 2994-3004, July.
    9. Chakraborty, Sandip & Kakani, Ram Kumar & Sampath, Aravind, 2022. "Portfolio risk and stress across the business cycle," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    10. Zhu, Hui-Ming & Li, ZhaoLai & You, WanHai & Zeng, Zhaofa, 2015. "Revisiting the asymmetric dynamic dependence of stock returns: Evidence from a quantile autoregression model," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 142-153.
    11. Chikashi Tsuji, 2016. "Does the fear gauge predict downside risk more accurately than econometric models? Evidence from the US stock market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1220711-122, December.

  9. Symeonidis, Lazaros & Prokopczuk, Marcel & Brooks, Chris & Lazar, Emese, 2012. "Futures basis, inventory and commodity price volatility: An empirical analysis," Economic Modelling, Elsevier, vol. 29(6), pages 2651-2663.
    See citations under working paper version above.
  10. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.

    Cited by:

    1. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    2. Simon A. BRODA & Markus HAAS & Jochen KRAUSE & Marc S. PAOLELLA & Sven C. STEUDE, 2011. "Stable Mixture GARCH Models," Swiss Finance Institute Research Paper Series 11-39, Swiss Finance Institute.
    3. Massimo Guidolin & Stuart Hyde & David McMillan & Sadayuki Ono, 2010. "Does the macroeconomy predict U.K. asset returns in a nonlinear fashion? comprehensive out-of-sample evidence," Working Papers 2010-039, Federal Reserve Bank of St. Louis.
    4. Kurter, Zeynep O., 2022. "How macroeconomic conditions affect systemic risk in the short and long-run?," The Warwick Economics Research Paper Series (TWERPS) 1407, University of Warwick, Department of Economics.
    5. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.
    6. Lolea Iulian-Cornel & Vilcu Lucian Constantin, 2018. "Measures of volatility for the Romanian Stock Exchange: a regime switching approach," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 12(1), pages 544-556, May.
    7. Jiro Hodoshima & Toshiyuki Yamawake, 2022. "Comparing Dynamic and Static Performance Indexes in the Stock Market: Evidence From Japan," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(2), pages 171-193, June.
    8. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 517-539, November.
    9. Trabelsi, Mohamed Ali & Hmida, Salma, 2017. "A Dynamic Correlation Analysis of Financial Contagion: Evidence from the Eurozone Stock Markets," MPRA Paper 83718, University Library of Munich, Germany, revised 2017.
    10. Toshiyuki Yam awake & Joseph Sheely & Roberto Serrano & Jiro Hodoshima, 2022. "Comparative Performance of Cryptocurrencies through the Aumann and Serrano Economic Index of Riskiness," Working Papers 2022-007, Brown University, Department of Economics.
    11. Qu, Hui & Duan, Qingling & Niu, Mengyi, 2018. "Modeling the volatility of realized volatility to improve volatility forecasts in electricity markets," Energy Economics, Elsevier, vol. 74(C), pages 767-776.
    12. Massimo Guidolin, 2013. "Markov switching models in asset pricing research," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 1, pages 3-44, Edward Elgar Publishing.
    13. Haas, Markus & Krause, Jochen & Paolella, Marc S. & Steude, Sven C., 2013. "Time-varying mixture GARCH models and asymmetric volatility," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 602-623.
    14. Cifter, Atilla, 2012. "Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 127-142, June.
    15. Focardi, Sergio M. & Fabozzi, Frank J. & Mazza, Davide, 2019. "Modeling local trends with regime shifting models with time-varying probabilities," International Review of Financial Analysis, Elsevier, vol. 66(C).
    16. Chon, Sora & Kim, Jaeho, 2021. "Does the Financial Leverage Effect Depend on Volatility Regimes?," Finance Research Letters, Elsevier, vol. 39(C).
    17. BenSaïda, Ahmed, 2015. "The frequency of regime switching in financial market volatility," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 63-79.

  11. Badescu Alex & Kulperger Reg & Lazar Emese, 2008. "Option Valuation with Normal Mixture GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(2), pages 1-42, May.

    Cited by:

    1. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    2. Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2011. "Option pricing with discrete time jump processes," Documents de travail du Centre d'Economie de la Sorbonne 11037r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Apr 2012.
    3. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2014. "Testing for Leverage Effects in the Returns of US Equities," Documents de travail du Centre d'Economie de la Sorbonne 14022r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Jan 2017.
    4. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
    5. ROMBOUTS, Jeroen V. K. & STENTOFT, Lars, 2010. "Option pricing with asymmetric heteroskedastic normal mixture models," LIDAM Discussion Papers CORE 2010049, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Badescu, Alexandru & Cui, Zhenyu & Ortega, Juan-Pablo, 2016. "A note on the Wang transform for stochastic volatility pricing models," Finance Research Letters, Elsevier, vol. 19(C), pages 189-196.
    7. Rombouts, Jeroen V.K. & Stentoft, Lars, 2011. "Multivariate option pricing with time varying volatility and correlations," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2267-2281, September.
    8. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2010. "Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes," Post-Print halshs-00523371, HAL.
    9. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2017. "Testing for Leverage Effects in the Returns of US Equities," Post-Print halshs-00973922, HAL.
    10. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 517-539, November.
    11. Aparna Bhat & Kirti Arekar, 2016. "Empirical Performance of Black-Scholes and GARCH Option Pricing Models during Turbulent Times: The Indian Evidence," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(3), pages 123-136, March.
    12. Badescu, Alex & Elliott, Robert J. & Siu, Tak Kuen, 2009. "Esscher transforms and consumption-based models," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 337-347, December.
    13. Dominique Guegan & Hanjarivo Lalaharison, 2010. "A short note on option pricing with Lévy Processes," Post-Print halshs-00542475, HAL.
    14. Badescu, Alexandru & Elliott, Robert J. & Ortega, Juan-Pablo, 2015. "Non-Gaussian GARCH option pricing models and their diffusion limits," European Journal of Operational Research, Elsevier, vol. 247(3), pages 820-830.
    15. Alexandru Badescu & Robert J. Elliott & Juan-Pablo Ortega, 2012. "Quadratic hedging schemes for non-Gaussian GARCH models," Papers 1209.5976, arXiv.org, revised Dec 2013.
    16. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2014. "Testing for Leverage Effect in Financial Returns," Documents de travail du Centre d'Economie de la Sorbonne 14022, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

  12. Emese Lazar & Carol Alexander, 2006. "Normal mixture GARCH(1,1): applications to exchange rate modelling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 307-336.
    See citations under working paper version above.
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