I. INDICATORI DE REZULTAT: Articolele științifice publicate/acceptate spre publicare în reviste din Science Citation Index Expanded, Social Sciences Citation Index sau Arts & Humanities Citation Index aflate printre primele 25% (sau cuartila Q1) în cadrul unui subdomeniu/Web of Science Category stabilit de Clarivate Analytics (număr); se iau în considerare cele mai favorabile cuartile corespunzătoare valorilor indicatorilor scientometrici Journal Impact Factor (JIF) sau Article Influence Score (AIS) din ultima ediție Journal Citation Reports (JCR) disponibilă la momentul trimiterii spre publicare

1. Agakishiev, I., Härdle, W. K., Kopa, M., Kozmik, K., & Petukhina, A. (2025). Multivariate probabilistic forecasting of electricity prices with trading applications. Energy Economics, 141, 108008. https://doi.org/10.1016/j.eneco.2024.108008
2. Lu, W., Wang, Y. F., Lin, M. B., Ren, R., & Härdle, W. K. (2024). Cross-Exchange Crypto Risk: A High-Frequency Dynamic Network Perspective. International Review of Financial Analysis. https://doi.org/10.1016/j.irfa.2024.103246
3. Anghel, DG, P Caraiani. (2024). Monetary policy shocks and the high-frequency network connectedness of stock markets. International Review of Economics & Finance. https://doi.org/10.1016/j.iref.2024.103558
4. Miftachov, R., Keilbar, G., & Härdle, W. K. (2024). Shapley Curves: A Smoothing Perspective. Journal of Business & Economic Statistics, 1–12. https://doi.org/10.1080/07350015.2024.2365781
5. Häusler, K., & Härdle, W. (2025). ETF construction on CRIX. Financial Innovation, 11(1), 92. https://doi.org/10.1186/s40854-025-00762-3
6. Teng, H.-W., Härdle, W. K., Osterrieder, J., Pele, D. T., Baals, L. J., Papavassiliou, V., Bolesta, K., Kabašinskas, A., Filipovska, O., Thomaidis, N. S., Moukas, A.-I., Goundar, S., Nasir, J. A., Weinberg, A. I., Arakelian, V., Truică, C.-O., Akar, M., Kabaklarlı, E., Apostol, E.-S., Iannario, M., Bȩdowska-Sójka, B., Skaftadóttir, H. K., Yildirim, O., Shala, A., Pisoni, G., Coita, I. F., Korba, S., Hafner, C. M., Schwendner, P., Molnár, B., & Xhumari, E. (2026). Digital assets: Risks, regulations, mitigation. Financial Innovation. https://doi.org/10.1186/s40854-025-00848-y
7. Grecu, R. A., Cramer, A. A., Pele, D. T., & Lessmann, S. (2025). The link between energy prices and stock markets in European Union countries. The North American Journal of Economics and Finance, 78, 102420. https://doi.org/10.1016/j.najef.2025.102420
8. Pele, D. T., Bolovăneanu, V., Lin, M.-B., Ren, R., Ginavar, A. T., Spilak, B., Andrei, A.-V., Toma, F.-M., Lessmann, S., & Härdle, W. K. (2025). In the beginning was the word: LLM-VaR and LLM-ES. Expert Systems with Applications, 128676. https://doi.org/10.1016/j.eswa.2025.128676
9. Gurgul, V., Lessmann, S., & Härdle, W. K. (2025). Deep learning and NLP in cryptocurrency forecasting: Integrating financial, blockchain, and social media data. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2025.02.0
10. Fang, Y., Chen, C. Y.-H., & Jiang, C. (2025). A flight-to-safety from Bitcoin to stock markets: Evidence from cyber attacks. International Review of Financial Analysis, 103, 104093. https://doi.org/10.1016/j.irfa.2025.104093
11. Xu, X., Klochkov, Y., Chen, L., & Härdle, W. K. (2027). Localizing multivariate CAViaR. Statistica Sinica, 37(1). https://doi.org/10.5705/ss.202022.0397
12. Härdle, W. K., & Ling, C. (2026). How sensitive are tail-related risk measures in a contamination neighbourhood? Statistica Sinica, 36(2). https://doi.org/10.5705/ss.202023.0019
13. Będowska-Sójka, B., Wójcik, P., & Pele, D. T. (2025). Early warning systems for cryptocurrency markets: Predicting “zombie” assets using machine learning. The North American Journal of Economics and Finance, 102, 102543. https://doi.org/10.1016/j.najef.2025.102543
14. Teng, H.-W., Huang, H.-P., & Shih, Y.-C. (2025). Tail risk in Bitcoin under the Basel framework. Finance Research Letters, 86(Part C), 108528. https://doi.org/10.1016/j.frl.2025.108528
15. Anagnoste, S., Andrei, A.-V., Bolovăneanu, V., Cepoi, C.-O., Clodnitchi, R., Cramer, A.-A., Grecu, R.-A., Lessmann, S., Pele, D. T., Petukhina, A., & Strat, V. A. (2025). The role of AI in (re)shaping energy finance: A systematic literature review. Energy Strategy Reviews, 61, 101833. https://doi.org/10.1016/j.esr.2025.101833
16. Chaffard, O., Mollá, P., Cavazza, M., & Prendinger, H. (2025). Enhancing large language models for bitcoin time series forecasting. Knowledge-Based Systems, 330(Part A), 114449. https://doi.org/10.1016/j.knosys.2025.114449
17. Caporin, M., Caraiani, P., Cepni, O., & Gupta, R. (2025). Predicting the conditional distribution of US stock market systemic stress: The role of climate risks. Journal of 2International Financial Markets, Institutions and Money, 101, 102156. https://doi.org/10.1016/j.intfin.2025.102156
18. Kitagawa, T., Wang, W., & Xu, M. (2025). Nonlinearity in Dynamic Causal Effects: Making the Bad into the Good, and the Good into the Great? Journal of Business & Economic Statistics, 43(4), 770–777. https://doi.org/10.1080/07350015.2025.2529327
19. Chernozhukov, V., Huang, C., & Wang, W. (2025). Uniform Inference on High-Dimensional Spatial Panel Networks. Journal of Business & Economic Statistics, 1–12. https://doi.org/10.1080/07350015.2025.2530122
20. SHU MF, ZHANG CH, CHEN QQ, Härdle WK (2025) Modeling SSE 50 ETF Returns and Option Pricing: Evidence from a Score-Driven GARCH-Jump Approach. Mathematics, https://doi.org/10.3390/math13203332
21. Härdle, W. K., Ren, R., Wang, Z., & Wu, W. B. (2026). A Network View on Portfolio Risk. Journal of Business & Economic Statistics, 1–21. https://doi.org/10.1080/07350015.2026.2634827
22. Chen, C. Y.-H., Kapetanios, G., & Wu, W.-B. (2025). Text-term selection and analysis: Frequentist and Bayesian strategies and interpretations. Journal of Econometrics, 106163. https://doi.org/10.1016/j.jeconom.2025.106163
23. Yang, F., Chen, C. Y.-H., & Jiang, C. (2025). A flight-to-safety from Bitcoin to stock markets: Evidence from cyber attacks. International Review of Financial Analysis, 103, 104093. https://doi.org/10.1016/j.irfa.2025.104093
24. Keilbar, G., Rodriguez-Poo, J. M., Soberón, A., & Wang, W. (2026). A projection-based approach for interactive fixed effects panel data models. Econometric Reviews, 45(1), 93–110. https://doi.org/10.1080/07474938.2025.2556702
Nu a fost prevăzut în cererea de finanțare
1. MSCA Digital Finance Doctoral Network – The DIGITAL FINANCE Pathway to Industry and Academia – Home | MSCA Digital programul Horizon 2.1 – Marie Sklodowska-Curie Actions (MSCA) sub topicul HORIZON-MSCA-2022-DN-01-01 – MSCA Doctoral Networks 2022, https://cordis.europa.eu/project/id/101119635
2. Call HORIZON-EIC-2024-TRANSITION-01, Type of action HORIZON-EIC, proposal ID 101215242, proiect Next-Guard, partener
CoEx – 42 PN-IV-P6-6.1-CoEx-2024-0085 / 1. Digitalizare, industrie și spațiu / 1.2. Economie atractivă la nivel regional și global, sigură și dinamică, agilă din punct de vedere al datelor / Dezvoltare Durabilă prin formarea de Comunități Inteligente în jurul Ecosistemelor Deschise de Date / Virgil Baran Universitatea Bucuresti / Academia de Studii Economice (Daniel Traian Pele a făcut parte din echipa care a depus proiectul); Institutul Național de Cercetare-Dezvoltare pentru Fizica și Inginerie Nucleara “Horia Hulubei” – IFIN – HH; Institutul Național de Cercetare-dezvoltare pentru Fizica Pamantului – INCDFP RA; Institutul de Stiinte Spatiale-filiala INFLPR; Institutul Național de Cercetare-Dezvoltare Pentru Fizica Materialelor Bucuresti RA; Universitatea de Medicina si Farmacie “Carol Davila”
6 doctoranzi = echivalent ENI 1,58
4 post-doctoranzi = echivalent ENI 1,42
Nr. total cercetatori in proiect: 20 (4,91 ENI), din care 5 femei și 15 barbati (1,55 ENI femei si 3,91 ENI barbati)
Director proiect: 1, echivalent ENI 0,76.
Cercetători cu experiență angajati in proiect: 6, echivalent ENI 1,15.
Drd și post-doc angajați în proiect: 6 doctoranzi = echivalent ENI 1,58 și 4 cercetatori post-doc echivalent ENI 1,42.
1 pozitie asistent cercetare
1 pozitie cercetator masterand
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