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Burak Saltoğlu
(Burak Saltoglu)

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.

    Mentioned in:

    1. MIDAS Regression is Now in EViews
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2016-03-26 00:30:00

Working papers

  1. Mehmet Y. Gürdal & Tolga U. Kuzubaþ & Burak Saltoðlu, 2016. "Measures of Individual Risk Attitudes and Portfolio Choice: Evidence from Pension Participants," Working Papers 2016/02, Bogazici University, Department of Economics.

    Cited by:

    1. Hiroyuki Yamada & Yuki Kanayama & Kanako Yoshikawa & Kyaw Wai Aung, 2020. "Risk attitude, risky behavior, and price determination in the sex market: A case study of Yangon, Myanmar," Keio-IES Discussion Paper Series 2020-013, Institute for Economics Studies, Keio University.
    2. Safdar Ullah Khan & Satyanarayana Ramella & Habib Ur Rahman & Zulfiqar Hyder, 2022. "Household Portfolio Allocations: Evidence on Risk Preferences from the Household, Income, and Labour Dynamics in Australia (HILDA) Survey Using Tobit Models," JRFM, MDPI, vol. 15(4), pages 1-13, April.
    3. Krčál, Ondřej & Staněk, Rostislav & Slanicay, Martin, 2019. "Made for the job or by the job? A lab-in-the-field experiment with firefighters," Research in Economics, Elsevier, vol. 73(4), pages 271-276.
    4. Blake, David & Duffield, Mel & Tonks, Ian & Haig, Alistair & Blower, Dean & MacPhee, Laura, 2022. "Smart defaults: Determining the number of default funds in a pension scheme," The British Accounting Review, Elsevier, vol. 54(4).
    5. Ondřej Krčál & Rostislav Staněk & Martin Slanicay, 2019. "Made for the job or by the job? A lab-in-the-field experiment with firefighters," MUNI ECON Working Papers 2019-05, Masaryk University, revised Feb 2023.

  2. Tolga Umut Kuzubas & Burak Saltoglu & Can Sever, 2014. "Systemic Risk and Heterogeneous Leverage in Banking Network: Implications for Banking Regulation," Working Papers 2014/01, Bogazici University, Department of Economics.

    Cited by:

    1. De Caux, Robert & McGroarty, Frank & Brede, Markus, 2017. "The evolution of risk and bailout strategy in banking systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 109-118.

  3. Tolga Umut Kuzubas & Inci Omercikoglu & Burak Saltoglu, 2013. "Network Centrality Measures and Systemic Risk: An Application to the Turkish Financial Crisis," Working Papers 2013/12, Bogazici University, Department of Economics.

    Cited by:

    1. Eduard Baumohl & Evzen Kocenda & Stefan Lyocsa & Tomas Vyrost, 2016. "Networks of volatility spillovers among stock markets," KIER Working Papers 941, Kyoto University, Institute of Economic Research.
    2. Michel Alexandre & Kau^e Lopes de Moraes & Francisco Aparecido Rodrigues, 2021. "Risk-dependent centrality in the Brazilian stock market," Papers 2103.09059, arXiv.org.
    3. Andrea Barón & María Victoria Landaberry & Rodrigo Lluberas & Jorge Ponce, 2020. "Commercial and banking credit network in Uruguay," Documentos de trabajo 2020006, Banco Central del Uruguay.
    4. Deev, Oleg & Lyócsa, Štefan, 2020. "Connectedness of financial institutions in Europe: A network approach across quantiles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    5. Thiago Christiano Silva & Solange Maria Guerra & Benjamin Miranda Tabak & Rodrigo Cesar de Castro Miranda, 2016. "Financial Networks, Bank Efficiency and Risk-Taking," Working Papers Series 428, Central Bank of Brazil, Research Department.
    6. Frank Emmert-Streib & Aliyu Musa & Kestutis Baltakys & Juho Kanniainen & Shailesh Tripathi & Olli Yli-Harja & Herbert Jodlbauer & Matthias Dehmer, 2017. "Computational Analysis of the structural properties of Economic and Financial Networks," Papers 1710.04455, arXiv.org.
    7. Barroso, João Barata Ribeiro Blanco & Silva, Thiago Christiano & Souza, Sergio Rubens Stancato de, 2018. "Identifying systemic risk drivers in financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 650-674.
    8. Thiago Christiano Silva & Sergio Rubens Stancato de Souza & Benjamin Miranda Tabak, 2016. "Structure and Dynamics of the Global Financial Network," Working Papers Series 439, Central Bank of Brazil, Research Department.
    9. Solange Maria Guerra & Benjamin Miranda Tabak & Rodrigo Andrés De Souza Penaloza & Rodrigo César De Castro Mirand, 2014. "Systemic Risk Measures," Anais do XLI Encontro Nacional de Economia [Proceedings of the 41st Brazilian Economics Meeting] 124, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    10. Michel Alexandre & Thiago Christiano Silva & Colm Connaughton & Francisco A. Rodrigues, 2021. "The Role of (non-)Topological Features as Drivers of Systemic Risk: a machine learning approach," Working Papers Series 556, Central Bank of Brazil, Research Department.
    11. Michel Alexandre & Felipe Jordão Xavier & Thiago Christiano Silva & Francisco A. Rodrigues, 2022. "Nestedness in the Brazilian Financial System," Working Papers Series 566, Central Bank of Brazil, Research Department.
    12. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhang, Wei, 2019. "Financial systemic risk measurement based on causal network connectedness analysis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 290-307.
    13. Yun, Tae-Sub & Jeong, Deokjong & Park, Sunyoung, 2019. "“Too central to fail” systemic risk measure using PageRank algorithm," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 251-272.
    14. Song, Jae Wook & Ko, Bonggyun & Cho, Poongjin & Chang, Woojin, 2016. "Time-varying causal network of the Korean financial system based on firm-specific risk premiums," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 287-302.
    15. Hossein Dastkhan & Naser Shams Gharneh, 2016. "Determination of Systemically Important Companies with Cross-Shareholding Network Analysis: A Case Study from an Emerging Market," IJFS, MDPI, vol. 4(3), pages 1-17, June.
    16. Onur Polat, 2021. "Time-Varying Network Connectedness of G-7 Economic Policy Uncertainties: A Locally Stationary TVP-VAR Approach," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 7(2), pages 47-59, December.
    17. Saidane, Dhafer & Sène, Babacar & Désiré Kanga, Kouamé, 2021. "Pan-African banks, banking interconnectivity: A new systemic risk measure in the WAEMU," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    18. Seabrook, Isobel & Caccioli, Fabio & Aste, Tomaso, 2022. "Quantifying impact and response in markets using information filtering networks," LSE Research Online Documents on Economics 115308, London School of Economics and Political Science, LSE Library.
    19. Giulia Masi & Giorgio Ricchiuti, 2020. "From FDI network topology to macroeconomic instability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 133-158, January.
    20. Alexandre, Michel & Silva, Thiago Christiano & Connaughton, Colm & Rodrigues, Francisco A., 2021. "The drivers of systemic risk in financial networks: a data-driven machine learning analysis," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    21. Silva, Thiago Christiano & Dias, Felipe A.M. & dos Reis, Vinicius E. & Tabak, Benjamin M., 2022. "The role of network topology in competition and ticket pricing in air transportation: Evidence from Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 601(C).
    22. Fiedor, Paweł, 2014. "Sector strength and efficiency on developed and emerging financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 180-188.
    23. Lai, Yujie & Hu, Yibo, 2021. "A study of systemic risk of global stock markets under COVID-19 based on complex financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    24. Qin, Xiao & Wang, Ze, 2023. "Share pledge financing network and systemic risks: Evidence from China," Journal of Banking & Finance, Elsevier, vol. 152(C).
    25. Xu, Qifa & Li, Mengting & Jiang, Cuixia & He, Yaoyao, 2019. "Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    26. Yao, Yanzhen & Li, Jianping & Zhu, Xiaoqian & Wei, Lu, 2017. "Expected default based score for identifying systemically important banks," Economic Modelling, Elsevier, vol. 64(C), pages 589-600.
    27. Ardekani, Aref Mahdavi & Distinguin, Isabelle & Tarazi, Amine, 2020. "Do banks change their liquidity ratios based on network characteristics?," European Journal of Operational Research, Elsevier, vol. 285(2), pages 789-803.
    28. Nasirian, Farzaneh & Mahdavi Pajouh, Foad & Balasundaram, Balabhaskar, 2020. "Detecting a most closeness-central clique in complex networks," European Journal of Operational Research, Elsevier, vol. 283(2), pages 461-475.
    29. Kuzubaş, Tolga Umut & Saltoğlu, Burak & Sever, Can, 2016. "Systemic risk and heterogeneous leverage in banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 358-375.

  4. Saltoglu, Burak & Yenilmez, Taylan, 2010. "Analyzing Systemic Risk with Financial Networks An Application During a Financial Crash," MPRA Paper 26684, University Library of Munich, Germany.

    Cited by:

    1. Kuzubaş, Tolga Umut & Ömercikoğlu, Inci & Saltoğlu, Burak, 2014. "Network centrality measures and systemic risk: An application to the Turkish financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 203-215.
    2. Martinez-Jaramillo, Serafin & Alexandrova-Kabadjova, Biliana & Bravo-Benitez, Bernardo & Solórzano-Margain, Juan Pablo, 2014. "An empirical study of the Mexican banking system’s network and its implications for systemic risk," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 242-265.
    3. Tolga Umut Kuzubas & Burak Saltoglu & Can Sever, 2014. "Systemic Risk and Heterogeneous Leverage in Banking Network: Implications for Banking Regulation," Working Papers 2014/01, Bogazici University, Department of Economics.

  5. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.

    Cited by:

    1. Kumar SANTOSH & Meher Kumar BHARAT & Ramona BIRAU & Mircea Laurentiu SIMION & Anand ABHISHEK & Singh MANOHAR, 2023. "Quantifying Long-Term Volatility for Developed Stock Markets: An Empirical Case Study Using PGARCH Model on Toronto Stock Exchange (TSX)," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-68.
    2. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
    3. Bharat Kumar Meher & Iqbal Thonse Hawaldar & Mathew Thomas Gil & Deebom Zorle Dum, 2021. "Measuring Leverage Effect of Covid 19 on Stock Price Volatility of Energy Companies Using High Frequency Data," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 489-502.

  6. Saltoglu, Burak & Yazgan, Ege, 2009. "The role of Regime Shifts in the Term Structure of Interest Rates: Further evidence from an Emerging Market," MPRA Paper 18741, University Library of Munich, Germany.

    Cited by:

    1. Kannan S. Thuraisamy, 2015. "Volatility Dynamics in the Term Structure of Latin American Sovereign International Bonds," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(5), pages 859-866, September.
    2. Kang, Bo Soo & Ryu, Doojin & Ryu, Doowon, 2014. "Phase-shifting behaviour revisited: An alternative measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 167-173.

  7. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.

    Cited by:

    1. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    2. Chan-Guk Huh & Jie Wu, 2015. "Linkage between US monetary policy and emerging economies: the case of Korea?s financial market and monetary policy," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 4(3), pages 1-18, September.
    3. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Improving the Predictive ability of oil for inflation: An ADL-MIDAS Approach," Working Papers 025, Centre for Econometric and Allied Research, University of Ibadan.
    4. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    5. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    6. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, vol. 94(2), pages 233-263, November.
    7. Afees A. Salisu & Umar B. Ndako & Idris Adediran, 2018. "Forecasting GDP of OPEC: The role of oil price," Working Papers 044, Centre for Econometric and Allied Research, University of Ibadan.
    8. Julián Alonso Cárdenas-Cárdenas & Edgar Caicedo-García & Eliana R. González Molano, 2020. "Estimación de la variación del precio de los alimentos con modelos de frecuencias mixtas," Borradores de Economia 1109, Banco de la Republica de Colombia.
    9. Yunxu Wang & Chi-Wei Su & Yuchen Zhang & Oana-Ramona Lobonţ & Qin Meng, 2023. "Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product," Mathematics, MDPI, vol. 11(19), pages 1-14, September.
    10. Jennie Bai & Eric Ghysels & Jonathan H. Wright, 2013. "State Space Models and MIDAS Regressions," Econometric Reviews, Taylor & Francis Journals, vol. 32(7), pages 779-813, October.
    11. Asgharian, Hossein & Hou, Ai Jun & Javed, Farrukh, 2013. "Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach," Knut Wicksell Working Paper Series 2013/4, Lund University, Knut Wicksell Centre for Financial Studies.
    12. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Forecasting GDP with energy series: ADL-MIDAS vs. Linear Time Series Models," Working Papers 035, Centre for Econometric and Allied Research, University of Ibadan.
    13. Anindya Biswas, 2014. "The output gap and expected security returns," Review of Financial Economics, John Wiley & Sons, vol. 23(3), pages 131-140, September.
    14. Biswas, Anindya, 2014. "The output gap and expected security returns," Review of Financial Economics, Elsevier, vol. 23(3), pages 131-140.
    15. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
    16. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.

  8. Danielsson, Jon & Saltoglu, Burak, 2003. "Anatomy of a market crash: a market microstructure analysis of the Turkish overnight liquidity crisis," LSE Research Online Documents on Economics 24855, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Goldstein, Itay & Yuan, Kathy & Ozdenoren, Emre, 2010. "Learning and Complementarities: Implications for Speculative Attacks," CEPR Discussion Papers 7651, C.E.P.R. Discussion Papers.
    2. Kuzubaş, Tolga Umut & Ömercikoğlu, Inci & Saltoğlu, Burak, 2014. "Network centrality measures and systemic risk: An application to the Turkish financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 203-215.
    3. Başak Tanyeri, 2010. "Financial Transparency and Sources of Hidden Capital in Turkish Banks," Journal of Financial Services Research, Springer;Western Finance Association, vol. 37(1), pages 25-43, February.
    4. Saltoglu, Burak & Yenilmez, Taylan, 2010. "Analyzing Systemic Risk with Financial Networks An Application During a Financial Crash," MPRA Paper 26684, University Library of Munich, Germany.
    5. Raphael Solomon, 2004. "When Bad Things Happen to Good Banks: Contagious Bank Runs and Currency Crises," Staff Working Papers 04-18, Bank of Canada.
    6. Eross, Andrea & Urquhart, Andrew & Wolfe, Simon, 2016. "Liquidity risk contagion in the interbank market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 45(C), pages 142-155.
    7. Andrea Eross & Andrew Urquhart & Simon Wolfe, 2019. "Investigating risk contagion initiated by endogenous liquidity shocks: evidence from the US and eurozone interbank markets," The European Journal of Finance, Taylor & Francis Journals, vol. 25(1), pages 35-53, January.
    8. Burak Saltoğlu, 2013. "Turkish Banking Sector Current Status and the Future Challenges," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 41(1), pages 75-86, March.

Articles

  1. Gürdal, Mehmet Y. & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2017. "Measures of individual risk attitudes and portfolio choice: Evidence from pension participants," Journal of Economic Psychology, Elsevier, vol. 62(C), pages 186-203.
    See citations under working paper version above.
  2. Kuzubaş, Tolga Umut & Saltoğlu, Burak & Sever, Can, 2016. "Systemic risk and heterogeneous leverage in banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 358-375.

    Cited by:

    1. Aida Barkauskaite & Ausrine Lakstutiene & Justyna Witkowska, 2018. "Measurement of Systemic Risk in a Common European Union Risk-Based Deposit Insurance System: Formal Necessity or Value-Adding Process?," Risks, MDPI, vol. 6(4), pages 1-21, December.
    2. Morteza Alaeddini & Philippe Madiès & Paul J. Reaidy & Julie Dugdale, 2023. "Interbank money market concerns and actors’ strategies—A systematic review of 21st century literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 573-654, April.
    3. Jiang, Shanshan & Fan, Hong, 2018. "Credit risk contagion coupling with sentiment contagion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 186-202.

  3. Ahmet Faruk Aysan & Huseyin Ozturk & Ali Yavuz Polat & Burak Saltoğlu, 2016. "Macroeconomic Drivers of Loan Quality in Turkey," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(1), pages 98-109, January.

    Cited by:

    1. Alberto Delgado, José Luis & Demirbaş, Dilek & Aysan, Ahmet Faruk, 2022. "Old But Resilient Story: Impact Of Decentralization On Social Welfare," MPRA Paper 115432, University Library of Munich, Germany.
    2. Segun Thompson Bolarinwa & Olawale Akinyele & Xuan Vinh Vo, 2021. "Determinants of nonperforming loans after recapitalization in the Nigerian banking industry: Does efficiency matter?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1509-1524, September.
    3. Ahmet Faruk Aysan & Dilek Demirbaş & José Luis Alberto Delgado, 2022. "Old But Resilient Story: Impact Of Decentralization On Social Welfare," Working Papers hal-03866662, HAL.

  4. Burak Saltoglu & Taylan Yenilmez, 2015. "When does low interconnectivity cause systemic risk?," Quantitative Finance, Taylor & Francis Journals, vol. 15(12), pages 1933-1942, December.

    Cited by:

    1. Cem Iskender Aydin & Begum Ozkaynak & Beatriz Rodríguez-Labajos & Taylan Yenilmez, 2017. "Network effects in environmental justice struggles: An investigation of conflicts between mining companies and civil society organizations from a network perspective," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-20, July.
    2. Kuzubaş, Tolga Umut & Saltoğlu, Burak & Sever, Can, 2016. "Systemic risk and heterogeneous leverage in banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 358-375.

  5. Kuzubaş, Tolga Umut & Ömercikoğlu, Inci & Saltoğlu, Burak, 2014. "Network centrality measures and systemic risk: An application to the Turkish financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 203-215.
    See citations under working paper version above.
  6. Burak Saltoğlu, 2013. "Turkish Banking Sector Current Status and the Future Challenges," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 41(1), pages 75-86, March.

    Cited by:

    1. Simone Auer & Emidio Cocozza & Andrea COlabella, 2016. "The financial systems in Russia and Turkey: recent developments and challenges," Questioni di Economia e Finanza (Occasional Papers) 358, Bank of Italy, Economic Research and International Relations Area.

  7. Emre Alper, C. & Fendoglu, Salih & Saltoglu, Burak, 2012. "MIDAS volatility forecast performance under market stress: Evidence from emerging stock markets," Economics Letters, Elsevier, vol. 117(2), pages 528-532.

    Cited by:

    1. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    2. Yan, Xiang & Bai, Jiancheng & Li, Xiafei & Chen, Zhonglu, 2022. "Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?," Resources Policy, Elsevier, vol. 75(C).
    3. Barsoum, Fady & Stankiewicz, Sandra, 2015. "Forecasting GDP growth using mixed-frequency models with switching regimes," International Journal of Forecasting, Elsevier, vol. 31(1), pages 33-50.
    4. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Wang, Jianqiong, 2020. "Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models," Energy, Elsevier, vol. 212(C).
    5. Liu, Min, 2022. "The driving forces of green bond market volatility and the response of the market to the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 288-309.
    6. Alper Gormus, N., 2016. "Do different time-horizons in volatility have any significance for the emerging markets?," Economics Letters, Elsevier, vol. 145(C), pages 29-32.
    7. Murat Körs & Mehmet Baha Karan, 2023. "Stock exchange volatility forecasting under market stress with MIDAS regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 295-306, January.
    8. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.
    9. Xinjie Lu & Feng Ma & Jiqian Wang & Jing Liu, 2022. "Forecasting oil futures realized range‐based volatility with jumps, leverage effect, and regime switching: New evidence from MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 853-868, July.
    10. Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022. "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, vol. 108(C).

  8. Burak Saltoglu & M. Ege Yazgan, 2012. "The Role of Regime Shifts in the Term Structure of Interest Rates: Further Evidence from an Emerging Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 48(S5), pages 48-63, November.
    See citations under working paper version above.
  9. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003;," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.

    Cited by:

    1. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Discussion Papers 2008-10, School of Economics, The University of New South Wales.
    2. Lee, Tae-Hwy & Long, Xiangdong, 2009. "Copula-based multivariate GARCH model with uncorrelated dependent errors," Journal of Econometrics, Elsevier, vol. 150(2), pages 207-218, June.
    3. John M Maheu & Thomas H McCurdy, 2008. "Do high-frequency measures of volatility improve forecasts of return distributions?," Working Papers tecipa-324, University of Toronto, Department of Economics.
    4. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Out-of-sample Comparison of Copula Specifications in Multivariate Density Forecasts," Tinbergen Institute Discussion Papers 08-105/4, Tinbergen Institute.
    5. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Anthony Garratt & James Mitchell & Shaun P. Vahey, 2009. "Measuring output gap uncertainty," Reserve Bank of New Zealand Discussion Paper Series DP2009/15, Reserve Bank of New Zealand.
    7. Cheng, Xixin & Li, W.K. & Yu, Philip L.H. & Zhou, Xuan & Wang, Chao & Lo, P.H., 2011. "Modeling threshold conditional heteroscedasticity with regime-dependent skewness and kurtosis," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2590-2604, September.
    8. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    9. Francesco Ravazzolo & Shaun P. Vahey, 2010. "Forecast densities for economic aggregates from disaggregate ensembles," Working Paper 2010/02, Norges Bank.
    10. Del Brio, Esther B. & Ñíguez, Trino-Manuel & Perote, Javier, 2011. "Multivariate semi-nonparametric distributions with dynamic conditional correlations," International Journal of Forecasting, Elsevier, vol. 27(2), pages 347-364, April.
    11. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    12. Francesco Ravazzolo & Shaun P Vahey, 2010. "Measuring Core Inflation in Australia with Disaggregate Ensembles," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    13. Kyungchul Song, 2009. "Testing Predictive Ability and Power Robustification," PIER Working Paper Archive 09-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    14. Li, Yushu & Andersson, Jonas, 2014. "A Likelihood Ratio and Markov Chain Based Method to Evaluate Density Forecasting," Discussion Papers 2014/12, Norwegian School of Economics, Department of Business and Management Science.
    15. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    16. Rompolis, Leonidas S., 2010. "Retrieving risk neutral densities from European option prices based on the principle of maximum entropy," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 918-937, December.
    17. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.

  10. 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.

    Cited by:

    1. Sofiane Aboura, 2014. "When the U.S. Stock Market Becomes Extreme?," Risks, MDPI, vol. 2(2), pages 1-15, May.
    2. Nieto, María Rosa & Ruiz Ortega, Esther, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Joseph P. Romano & Michael Wolf, 2003. "Stepwise Multiple Testing as Formalized Data Snooping," Working Papers 17, Barcelona School of Economics.
    4. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
    5. 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.
    6. Huang Dashan & Yu Baimin & Lu Zudi & Fabozzi Frank J. & Focardi Sergio & Fukushima Masao, 2010. "Index-Exciting CAViaR: A New Empirical Time-Varying Risk Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-26, March.
    7. Kiani, Khurshid M., 2011. "Relationship between portfolio diversification and value at risk: Empirical evidence," Emerging Markets Review, Elsevier, vol. 12(4), pages 443-459.
    8. Siva Kiran GUPTHA. K & Prabhakar RAO. R, 2019. "GARCH based VaR estimation: An empirical evidence from BRICS stock markets," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(4(621), W), pages 201-218, Winter.
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    13. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
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    15. Maghyereh Aktham Issa & Awartani Basel, 2012. "Modeling and Forecasting Value-at-Risk in the UAE Stock Markets: The Role of Long Memory, Fat Tails and Asymmetries in Return Innovations," Review of Middle East Economics and Finance, De Gruyter, vol. 8(1), pages 1-22, August.
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    30. Hajo Holzmann & Matthias Eulert, 2014. "The role of the information set for forecasting - with applications to risk management," Papers 1404.7653, arXiv.org.
    31. Dias, Alexandra, 2013. "Market capitalization and Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5248-5260.
    32. Xiaoying Huang, 2017. "A Double-Exponential Jump model and its application to risk measure in Wheat spot market," Economics Bulletin, AccessEcon, vol. 37(2), pages 1298-1309.
    33. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    34. 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.
    35. Lidia Sanchis-Marco & Antonio Rubia Serrano, 2011. "On downside risk predictability through liquidity and trading activity: a quantile regression approach," Working Papers. Serie AD 2011-14, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    36. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    37. James M. O'Brien & Pawel J. Szerszen, 2014. "An Evaluation of Bank VaR Measures for Market Risk During and Before the Financial Crisis," Finance and Economics Discussion Series 2014-21, Board of Governors of the Federal Reserve System (U.S.).
    38. Chrétien, Stéphane & Coggins, Frank, 2010. "Performance and conservatism of monthly FHS VaR: An international investigation," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 323-333, December.
    39. Kim, Jun Sik & Ryu, Doojin, 2015. "Are the KOSPI 200 implied volatilities useful in value-at-risk models?," Emerging Markets Review, Elsevier, vol. 22(C), pages 43-64.
    40. 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.
    41. Fries, Christian P. & Nigbur, Tobias & Seeger, Norman, 2017. "Displaced relative changes in historical simulation: Application to risk measures of interest rates with phases of negative rates," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 175-198.
    42. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    43. Dimitrakopoulos, Dimitris N. & Kavussanos, Manolis G. & Spyrou, Spyros I., 2010. "Value at risk models for volatile emerging markets equity portfolios," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 515-526, November.
    44. Huang, Dashan & Yu, Baimin & Fabozzi, Frank J. & Fukushima, Masao, 2009. "CAViaR-based forecast for oil price risk," Energy Economics, Elsevier, vol. 31(4), pages 511-518, July.
    45. Assaf, A., 2009. "Extreme observations and risk assessment in the equity markets of MENA region: Tail measures and Value-at-Risk," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 109-116, June.
    46. Rubia, Antonio & Sanchis-Marco, Lidia, 2013. "On downside risk predictability through liquidity and trading activity: A dynamic quantile approach," International Journal of Forecasting, Elsevier, vol. 29(1), pages 202-219.
    47. Nikkin L. Beronilla & Dennis S. Mapa, 2008. "Range-based models in estimating value-at-risk (VaR)," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 45(2), pages 87-99, December.
    48. Roland Füss & Zeno Adams & Dieter G Kaiser, 2010. "The predictive power of value-at-risk models in commodity futures markets," Journal of Asset Management, Palgrave Macmillan, vol. 11(4), pages 261-285, October.
    49. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    50. Szubzda Filip & Chlebus Marcin, 2019. "Comparison of Block Maxima and Peaks Over Threshold Value-at-Risk models for market risk in various economic conditions," Central European Economic Journal, Sciendo, vol. 6(53), pages 70-85, January.
    51. Mateusz Buczyński & Marcin Chlebus, 2019. "Old-fashioned parametric models are still the best. A comparison of Value-at-Risk approaches in several volatility states," Working Papers 2019-12, Faculty of Economic Sciences, University of Warsaw.
    52. Wilson Calmon & Eduardo Ferioli & Davi Lettieri & Johann Soares & Adrian Pizzinga, 2021. "An Extensive Comparison of Some Well‐Established Value at Risk Methods," International Statistical Review, International Statistical Institute, vol. 89(1), pages 148-166, April.
    53. Alejandro Bernales & Diether W. Beuermann & Gonzalo Cortazar, 2014. "Thinly traded securities and risk management," Estudios de Economia, University of Chile, Department of Economics, vol. 41(1 Year 20), pages 5-48, June.
    54. Naqvi, Syed Muhammad Waqar Azeem & Rizvi, Syed Kumail Abbas & Orangzab & Ali, Muhammad, 2016. "Value at Risk at Asian Emerging Stock Markets," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 28(3), pages 311-319.
    55. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    56. Auerbach, Jonathan & Wan, Phyllis, 2020. "Forecasting the urban skyline with extreme value theory," International Journal of Forecasting, Elsevier, vol. 36(3), pages 814-828.
    57. 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.
    58. Cao, Guangxi & Zhang, Minjia, 2015. "Extreme values in the Chinese and American stock markets based on detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 25-35.
    59. Bucevska Vesna, 2013. "An Empirical Evaluation of GARCH Models in Value-at-Risk Estimation: Evidence from the Macedonian Stock Exchange," Business Systems Research, Sciendo, vol. 4(1), pages 49-64, March.
    60. Alexandru Stanga, 2008. "Measuring market risk: a copula and extreme value approach," Advances in Economic and Financial Research - DOFIN Working Paper Series 13, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    61. Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2021. "Bayesian estimation for a semiparametric nonlinear volatility model," Economic Modelling, Elsevier, vol. 98(C), pages 361-370.
    62. Santosh Mishra & Gloria Gonzalez-Rivera & Tae-Hwy Lee, 2004. "Jumps in Rank and Expected Returns. Introducing Varying Cross-sectional Risk," Econometric Society 2004 North American Winter Meetings 356, Econometric Society.
    63. Degiannakis, Stavros & Floros, Christos & Livada, Alexandra, 2012. "Evaluating Value-at-Risk Models before and after the Financial Crisis of 2008: International Evidence," MPRA Paper 80463, University Library of Munich, Germany.
    64. Seok-Oh Jeong & Kee-Hoon Kang, 2009. "Nonparametric estimation of value-at-risk," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1225-1238.
    65. 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.
    66. Levent C. Uslu & Burak Evre, 2017. "Liquidity Adjusted Value At Risk: Integrating The Uncertainty In Depth And Tightness," Eurasian Journal of Business and Management, Eurasian Publications, vol. 5(1), pages 55-69.
    67. Bekiros, Stelios D. & Georgoutsos, Dimitris A., 2005. "Estimation of Value-at-Risk by extreme value and conventional methods: a comparative evaluation of their predictive performance," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(3), pages 209-228, July.
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  11. Nowman, K. Ben & Saltoglu, Burak, 2003. "Continuous time and nonparametric modelling of U.S. interest rate models," International Review of Financial Analysis, Elsevier, vol. 12(1), pages 25-34.

    Cited by:

    1. Christiansen, Charlotte, 2005. "Level-ARCH Short Rate Models with Regime Switching: Bivariate Modeling of US and European Short Rates," Finance Research Group Working Papers F-2005-03, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    2. K. Ben Nowman & Burak Saltoglu, 2003. "An empirical comparison of interest rates using an interest rate model and nonparametric methods," Applied Economics Letters, Taylor & Francis Journals, vol. 10(10), pages 643-645.
    3. Chrysovalantis Gaganis & Fotios Pasiouras & Charalambos Spathis & Constantin Zopounidis, 2007. "A comparison of nearest neighbours, discriminant and logit models for auditing decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 23-40, January.
    4. Zi‐Yi Guo, 2021. "Out‐of‐sample performance of bias‐corrected estimators for diffusion processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 243-268, March.
    5. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00511979, HAL.
    6. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    7. Nowman, Khalid Ben, 2010. "Modelling the UK and Euro yield curves using the Generalized Vasicek model: Empirical results from panel data for one and two factor models," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 334-341, December.
    8. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," PSE-Ecole d'économie de Paris (Postprint) halshs-00511979, HAL.
    9. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00505165, HAL.
    10. Dominique Guegan & Patrick Rakotomarolahy, 2009. "The Multivariate k-Nearest Neighbor Model for Dependent Variables : One-Sided Estimation and Forecasting," Post-Print halshs-00423871, HAL.

  12. Lee, Tae-Hwy & Saltoglu, Burak, 2002. "Assessing the risk forecasts for Japanese stock market," Japan and the World Economy, Elsevier, vol. 14(1), pages 63-85, January.

    Cited by:

    1. He, Kaijian & Lai, Kin Keung & Yen, Jerome, 2011. "Value-at-risk estimation of crude oil price using MCA based transient risk modeling approach," Energy Economics, Elsevier, vol. 33(5), pages 903-911, September.
    2. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.

  13. Burc Kayahan & Thanasis Stengos & Burak Saltoglu, 2002. "Intra-Day Features of Realized Volatility: Evidence from an Emerging Market," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(1), pages 17-24, April.

    Cited by:

    1. Stavros Degiannakis, 2008. "ARFIMAX and ARFIMAX-TARCH realized volatility modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1169-1180.
    2. Degiannakis, Stavros, 2004. "Forecasting Realized Intra-day Volatility and Value at Risk: Evidence from a Fractional Integrated Asymmetric Power ARCH Skewed-t Model," MPRA Paper 80488, University Library of Munich, Germany.
    3. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    4. Xekalaki, Evdokia & Degiannakis, Stavros, 2005. "Evaluating volatility forecasts in option pricing in the context of a simulated options market," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 611-629, April.
    5. Georgios Chortareas & John Nankervis & Ying Jiang, 2007. "Forecasting Exchange Rate Volatility with High Frequency Data: Is the Euro Different?," Money Macro and Finance (MMF) Research Group Conference 2006 79, Money Macro and Finance Research Group.
    6. Balaban, Ercan & Ozgen, Tolga & Karidis, Socrates, 2018. "Intraday and interday distribution of stock returns and their asymmetric conditional volatility: Firm-level evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 905-915.
    7. Degiannakis, Stavros, 2004. "Volatility Forecasting: Evidence from a Fractional Integrated Asymmetric Power ARCH Skewed-t Model," MPRA Paper 96330, University Library of Munich, Germany.
    8. Balaban, Ercan & Ozgen, Tolga, 2016. "Trading session effects on stock returns and their conditional volatility: Firm-level evidence from a European Union accession country," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 264-271.

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