Luca Trapin

Personal Details

First Name:	Luca
Middle Name:
Last Name:	Trapin
Suffix:
RePEc Short-ID:	ptr380
	https://sites.google.com/site/lucatrapin/home?authuser=0

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. Marco Bee & Julien Hambuckers & Luca Trapin, 2018. "Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach," DEM Working Papers 2018/08, Department of Economics and Management.
   • M. Bee & J. Hambuckers & L. Trapin, 2019. "Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1255-1266, August.

   Cited by:

   1. Marco Bee & Julien Hambuckers & Luca Trapin, 2019. "An improved approach for estimating large losses in insurance analytics and operational risk using the g-and-h distribution," DEM Working Papers 2019/11, Department of Economics and Management.
   2. Marco Bee, 2022. "The truncated g-and-h distribution: estimation and application to loss modeling," Computational Statistics, Springer, vol. 37(4), pages 1771-1794, September.

2. Alessandro Chessa & Irene Crimaldi & Massimo Riccaboni & Luca Trapin, 2014. "Cluster analysis of weighted bipartite networks: a new copula-based approach," Working Papers 3/2014, IMT School for Advanced Studies Lucca, revised Apr 2014.

   Cited by:

   1. Neelu Chaudhary & Hardeo Kumar Thakur & Rinky Dwivedi, 2022. "An ensemble model to optimize modularity in dynamic bipartite networks," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2248-2260, October.
   2. Camacho-Villa, Tania Carolina & Zepeda-Villarreal, Ernesto Adair & Díaz-José, Julio & Rendon-Medel, Roberto & Govaerts, Bram, 2023. "The contribution of strong and weak ties to resilience: The case of small-scale maize farming systems in Mexico," Agricultural Systems, Elsevier, vol. 210(C).

Articles

1. Marco Bee & Debbie J. Dupuis & Luca Trapin, 2018. "Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 398-415, April.

   Cited by:

   1. Xiafei Li & Dongxin Li & Xuhui Zhang & Guiwu Wei & Lan Bai & Yu Wei, 2021. "Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1501-1523, December.
   2. Hoga, Yannick, 2021. "The uncertainty in extreme risk forecasts from covariate-augmented volatility models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 675-686.
   3. Tan, Shay-Kee & Ng, Kok-Haur & Chan, Jennifer So-Kuen & Mohamed, Ibrahim, 2019. "Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 537-551.
   4. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
   5. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.

2. Corsi, Fulvio & Lillo, Fabrizio & Pirino, Davide & Trapin, Luca, 2018. "Measuring the propagation of financial distress with Granger-causality tail risk networks," Journal of Financial Stability, Elsevier, vol. 38(C), pages 18-36.

   Cited by:

   1. Shirokikh Oleg & Pastukhov Grigory & Semenov Alexander & Butenko Sergiy & Veremyev Alexander & Boginski Vladimir & Pasiliao Eduardo L., 2022. "Networks of causal relationships in the U.S. stock market," Dependence Modeling, De Gruyter, vol. 10(1), pages 177-190, January.
   2. Roman Horvath, 2020. "Natural Catastrophes and Financial Development: An Empirical Analysis," Working Papers IES 2020/14, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised May 2020.
   3. Wen, Shigang & Li, Jianping & Huang, Chuangxia & Zhu, Xiaoqian, 2023. "Extreme risk spillovers among traditional financial and FinTech institutions: A complex network perspective," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 190-202.
   4. Laurentiu Dumitru ANDREI & Petre BREZEANU & Sorin-Marius DINU & Tiberiu DIACONESCU & Constantin ANGHELACHE, 2019. "Correlations and Turbulence of the European Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 88-100, March.
   5. Chen, Wei & Qu, Shuai & Jiang, Manrui & Jiang, Cheng, 2021. "The construction of multilayer stock network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
   6. ZHANG, Ping & WANG, Yiru & ZHAO, Min & YANG, Tzu-Yi, 2021. "Measuring Systemic Risk Of China'S Listed Banks," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 25(3), pages 6-28, September.
   7. Thiago Christiano Silva & Solange Maria Guerra & Benjamin Miranda Tabak, 2019. "Fiscal Risk and Financial Fragility," Working Papers Series 495, Central Bank of Brazil, Research Department.
      • Silva, Thiago Christiano & Guerra, Solange Maria & Tabak, Benjamin Miranda, 2020. "Fiscal risk and financial fragility," Emerging Markets Review, Elsevier, vol. 45(C).
   8. Kumar, Sudarshan & Bansal, Avijit & Chakrabarti, Anindya S., 2019. "Ripples on financial networks," IIMA Working Papers WP 2019-10-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
   9. Mazzarisi, Piero & Zaoli, Silvia & Campajola, Carlo & Lillo, Fabrizio, 2020. "Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
   10. Horvath, Roman, 2021. "Natural catastrophes and financial depth: An empirical analysis," Journal of Financial Stability, Elsevier, vol. 53(C).
   11. Nina Tessler & Itzhak Venezia, 2022. "A multicountry measure of comovement and contagion in international markets: definition and applications," Review of Quantitative Finance and Accounting, Springer, vol. 58(4), pages 1307-1330, May.
   12. Oliver E. Williams & Lucas Lacasa & Ana P. Millán & Vito Latora, 2022. "The shape of memory in temporal networks," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
   13. Wu, Shan & Tong, Mu & Yang, Zhongyi & Zhang, Tianyi, 2021. "Interconnectedness, systemic risk, and the influencing factors: Some evidence from China’s financial institutions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
   14. Mazzarisi, Piero & Zaoli, Silvia & Lillo, Fabrizio & Delgado, Luis & Gurtner, Gérald, 2020. "New centrality and causality metrics assessing air traffic network interactions," Journal of Air Transport Management, Elsevier, vol. 85(C).
   15. Franch, Fabio & Nocciola, Luca & Vouldis, Angelos, 2022. "Temporal networks in the analysis of financial contagion," Working Paper Series 2667, European Central Bank.
   16. Xue Cui & Lu Yang, 2024. "Systemic risk and idiosyncratic networks among global systemically important banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 58-75, January.
   17. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.
   18. Wu, Fei & Zhang, Dayong & Zhang, Zhiwei, 2019. "Connectedness and risk spillovers in China’s stock market: A sectoral analysis," Economic Systems, Elsevier, vol. 43(3).
   19. Piero Mazzarisi & Silvia Zaoli & Carlo Campajola & Fabrizio Lillo, 2020. "Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages," Papers 2005.01160, arXiv.org, revised May 2021.
   20. Marfatia, Hardik & Zhao, Wan-Li & Ji, Qiang, 2020. "Uncovering the global network of economic policy uncertainty," Research in International Business and Finance, Elsevier, vol. 53(C).
   21. Nguyen, Linh Hoang & Lambe, Brendan John, 2021. "International tail risk connectedness: Network and determinants," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
   22. Chen, Wang & Ho, Kung-Cheng & Yang, Lu, 2020. "Network structures and idiosyncratic contagion in the European sovereign credit default swap market," International Review of Financial Analysis, Elsevier, vol. 72(C).
   23. Paresh Kumar Narayan & Syed Aun R. Rizvi & Ali Sakti, 2022. "Did green debt instruments aid diversification during the COVID-19 pandemic?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-15, December.
   24. Samuel Ugwu & Pierre Miasnikof & Yuri Lawryshyn, 2023. "Distance Correlation Market Graph: The Case of S&P500 Stocks," Mathematics, MDPI, vol. 11(18), pages 1-13, September.
   25. Nicoló Andrea Caserini & Paolo Pagnottoni, 2022. "Effective transfer entropy to measure information flows in credit markets," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 729-757, October.
   26. Liu, Bing-Yue & Fan, Ying & Ji, Qiang & Hussain, Nazim, 2022. "High-dimensional CoVaR network connectedness for measuring conditional financial contagion and risk spillovers from oil markets to the G20 stock system," Energy Economics, Elsevier, vol. 105(C).
   27. Sudarshan Kumar & Tiziana Di Matteo & Anindya S. Chakrabarti, 2020. "Disentangling shock diffusion on complex networks: Identification through graph planarity," Papers 2001.01518, arXiv.org.
   28. Jan Kolesnik, 2021. "The Contagion Effect and its Mitigation in the Modern Banking System," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 1009-1024.
   29. Fuwei Xu, 2024. "Modeling the Paths of China’s Systemic Financial Risk Contagion: A Ripple Network Perspective Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 47-73, January.
   30. Su, Zhi & Liu, Peng & Fang, Tong, 2022. "Uncertainty matters in US financial information spillovers: Evidence from a directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 229-242.
   31. Huang, Qi-An & Zhao, Jun-Chan & Wu, Xiao-Qun, 2022. "Financial risk propagation between Chinese and American stock markets based on multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

3. Luca Trapin, 2018. "Can Volatility Models Explain Extreme Events?," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 297-315.

   Cited by:

   1. James, Robert & Leung, Henry & Leung, Jessica Wai Yin & Prokhorov, Artem, 2023. "Forecasting tail risk measures for financial time series: An extreme value approach with covariates," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 29-50.
   2. Julia S. Mehlitz & Benjamin R. Auer, 2021. "Time‐varying dynamics of expected shortfall in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 895-925, June.
   3. Cristi Spulbar & Elena Loredana Minea, 2022. "Inefficient Stock Markets And Their Implications In The Context Of Extreme Financial Events: A Theoretical Framework," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 38-41, February.
   4. Hoga, Yannick, 2021. "The uncertainty in extreme risk forecasts from covariate-augmented volatility models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 675-686.
   5. Tobias Fissler & Yannick Hoga, 2021. "Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability," Papers 2104.10673, arXiv.org, revised Feb 2022.
   6. Zhu, Bo & Lin, Renda & Liu, Jiahao, 2020. "Magnitude and persistence of extreme risk spillovers in the global energy market: A high-dimensional left-tail interdependence perspective," Energy Economics, Elsevier, vol. 89(C).
   7. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.

4. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.

   Cited by:

   1. Katleho Makatjane & Ntebogang Moroke, 2021. "Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index," IJFS, MDPI, vol. 9(2), pages 1-18, March.
   2. Hamed Tabasi & Vahidreza Yousefi & Jolanta Tamošaitienė & Foroogh Ghasemi, 2019. "Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models," Administrative Sciences, MDPI, vol. 9(2), pages 1-17, May.
   3. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.

5. Bee, Marco & Dupuis, Debbie J. & Trapin, Luca, 2016. "Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 86-99.

   Cited by:

   1. Małgorzata Just & Krzysztof Echaust, 2021. "An Optimal Tail Selection in Risk Measurement," Risks, MDPI, vol. 9(4), pages 1-16, April.
   2. 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.
   3. Krzysztof Echaust & Małgorzata Just, 2020. "Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection," Mathematics, MDPI, vol. 8(1), pages 1-24, January.
   4. H. Kaibuchi & Y. Kawasaki & G. Stupfler, 2022. "GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 22(7), pages 1277-1294, July.
      • Hibiki Kaibuchi & Yoshinori Kawasaki & Gilles Stupfler, 2021. "GARCH-UGH: A bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Papers 2104.09879, arXiv.org.
   5. Wang, Yi-Chiuan & Wu, Jyh-Lin & Lai, Yi-Hao, 2018. "New evidence on asymmetric return–volume dependence and extreme movements," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 212-227.
   6. A Clements & D Preve, 2019. "A Practical Guide to Harnessing the HAR Volatility Model," NCER Working Paper Series 120, National Centre for Econometric Research.
      • Clements, Adam & Preve, Daniel P.A., 2021. "A Practical Guide to harnessing the HAR volatility model," Journal of Banking & Finance, Elsevier, vol. 133(C).
   7. Łuczak, Aleksandra & Just, Małgorzata, 2021. "Sustainable development of territorial units: MCDM approach with optimal tail selection," Ecological Modelling, Elsevier, vol. 457(C).
   8. Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
   9. Wang, Tianyi & Liang, Fang & Huang, Zhuo & Yan, Hong, 2022. "Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model," Economic Modelling, Elsevier, vol. 109(C).
   10. Hoga, Yannick, 2021. "The uncertainty in extreme risk forecasts from covariate-augmented volatility models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 675-686.
   11. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
   12. Guo, Yangli & He, Feng & Liang, Chao & Ma, Feng, 2022. "Oil price volatility predictability: New evidence from a scaled PCA approach," Energy Economics, Elsevier, vol. 105(C).
   13. Hamidreza Arian & Hossein Poorvasei & Azin Sharifi & Shiva Zamani, 2020. "The Uncertain Shape of Grey Swans: Extreme Value Theory with Uncertain Threshold," Papers 2011.06693, arXiv.org.
   14. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
   15. Jiawen Luo & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2022. "Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies," Working Papers 202258, University of Pretoria, Department of Economics.
   16. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.
   17. Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.

6. Marco Bee & Debbie J. Dupuis & Luca Trapin, 2016. "US stock returns: are there seasons of excesses?," Quantitative Finance, Taylor & Francis Journals, vol. 16(9), pages 1453-1464, September.

   Cited by:

   1. Marco Bee & Massimo Riccaboni & Luca Trapin, 2016. "An extreme value analysis of the last century crises across industries in the U.S. economy," Working Papers 02/2016, IMT School for Advanced Studies Lucca, revised Feb 2016.
      • Bee, Marco & Riccaboni, Massimo & Trapin, Luca, 2017. "An extreme value analysis of the last century crises across industries in the U.S. economy," Journal of Economic Dynamics and Control, Elsevier, vol. 81(C), pages 65-78.
   2. Cordero, Arkangel M., 2023. "Community and aftershock: New venture founding in the wake of deadly natural disasters," Journal of Business Venturing, Elsevier, vol. 38(2).

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-ECM: Econometrics (2) 2014-04-29 2018-12-24
2. NEP-ORE: Operations Research (2) 2014-04-29 2018-12-24
3. NEP-CMP: Computational Economics (1) 2014-04-29
4. NEP-NET: Network Economics (1) 2014-04-29
5. NEP-RMG: Risk Management (1) 2018-12-24

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