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DrAdamaria Perrotta

Lecturer/Assistant Professor

School of Mathematics and Statistics

Orcid identifier0000-0001-5611-1103 (opens in a new tab)⁠
  • Lecturer/Assistant Professor
    School of Mathematics and Statistics
  • 00 353 1 716 2590
  • University College Dublin, School of Mathematics and Statistics, Science Centre - South Belfield Dublin 4

RESEARCH INTERESTS

  • Early detection of new financial crisis is fundamental to ensure the economic wellbeing of all EU citizens, it can give the possibility of the timely implementation of new regulatory financial policies to avoid such crises, strengthening the capital requirements and mitigating potential death spiral effects. Yearly, Basel Regulation asks all financial institutions to price their early-prepayment risk. Prepayment risk is essentially the risk that a mortgage will be repaid earlier than the originally agreed termination date. Nowadays, the assessment of prepayment risk is not only of fundamental importance for players in the MBS market that need to price these instruments and hedge their risk, but also for commercial banks that do not deal with such trades but nevertheless must comply with the regulation on interest rate risk in the banking book (IRRBB). However, traditional financial models can't forecast properly such risk, providing only approximate estimates. So, I am leveraging new digital technologies to contribute to this problem: I am investigating what macroeconomics factors beyond interest rates are responsible for the prepayment risk premium via Machine Learning and AI techniques.

  • One of the main questions with respect to the Non Performing Loans (NPL) is whether and if the facility will start to perform again. Specifically, the owners of such instruments (usually banks) will assess the risk of keeping them in the medium to long term against maintenance and recovery costs while potential buyers / investors will weigh their decision in view of both the probability that the loan will recover and on the time to recovery. The core idea of this project should be to identify the Net Present Value (NPV) of the NPL in view of a set of specific parameters, which include not only the interest rate of the loan, the obligor's specific risk, the probability that each of the remaining cash flows will be payed but also the phase of the economy or business cycle. By assessing the consumers’ repayment behavior across business cycles, the project is aimed to provide more information about the creditworthiness of consumer loans in portfolio, especially at extreme phases of the business cycle, which is very useful for credit risk management.

  • Nowadays, Science and Mathematics increasingly rely on computer-based methodologies. Indeed, there is a growing interest in the integration of computational methods within the scientific field, as it is recognized that they can play a significant role in enhancing
    knowledge, understanding and sensemaking. This phenomenon serves as the driving force behind a burgeoning body of the literature that has delved into the role of computation in STEM subjects and computational thinking practices. In this scenario, Computational Finance emerges as a novel interdisciplinary field within Applied Mathematics, carrying significance in the mastery of Financial Mathematics and computational thinking. While the proliferation of Financial Mathematics programs has been extraordinary in recent years, it is crucial to recognize that research focusing on the Computational Finance curriculum remains limited. To bridge this gap and foster innovation, excellence and creativity in UCD T&L through new technologies, I am conducting cutting-hedge research in Computational Finance Education. In collaboration with the DELTA research group at University of Turin,I am investigating how the new definition of "inclusive computational practices" (which include a wide spectrum of examples like: having students working with simulations and/or algorithms, giving students pieces of code to complete on their own, inspecting and comparing computational outputs, advising students on groups projects where they write code from scratch) and collaborative learning models can contribute to sense-making to Financial Mathematics. Data are collected in class, during the ACM30110 labs.

  • Partial Differential Equations, Sobolev and Hardy-Sobolev inequalities with remainder terms.
  • Sharp estimates and uniqueness results for a class of Dirichlet boundary problems for nonlinear elliptic equations with lower order terms and measure data.

GRANTS

  • PROJECT GRANT
    AfriExchange
    Erasmus+1 Sep 2026 - 1 Sep 2029
  • PROJECT GRANT
    AfriExchange
    Erasmus+1 Sep 2026 - 1 Sep 2029
  • PROJECT GRANT
    Sustainability in Digital Finance: A UCD Living Lab
    University College Dublin (UCD)1 Aug 2026 - 28 Feb 2027
    People funded by this grant:
  • SEED FUNDING
    Rethinking computational practices to boost collaboration and comparing homogeneous and heterogeneous ability grouping in collaborative computational lab activities in Financial Mathematics
    University College Dublin (UCD)1 Apr 2025 - 1 Oct 2026
    People funded by this grant:
    • Perrotta A
  • GRANT
    University Teaching & Learning Award
    University College Dublin (UCD)1 Feb 2025 - 28 Feb 2027
    People funded by this grant:
    • Perrotta A
  • GRANT
    OBRSS Research Support Scheme
    University College Dublin (UCD)1 Nov 2024 - 31 Oct 2027
    People funded by this grant:
    • Perrotta A
  • GRANT
    The ADVANCE Centre for Professional Education
    Department of Further and Higher Education, Research, Innovation and Science, National Training Fund6 Oct 2020 - 6 Oct 2025
    This grant is funding the development and offering of a professional certificate in Financial Mathematics, a postgraduate digital programme, responding directly to professionals’ needs. The programme is offered in collaboration with the UCD ADVANCE Centre for Professional Education team
  • GRANT
    T&L Enhancement Funding 2019
    Higher Education Authority, the National Forum for the Enhancement of Teaching and Learning in Higher Education31 Jan 2020 - 30 May 2021
    How coding languages and FinCad Analytics Suite for Excel can facilitate a sensemaking to quantitative finance: a learner-centered and EBS approach to teaching Computational Finance
  • GRANT
    Centre for Research Training (CRT) in Foundations of Data Science
    Science Foundation Ireland (SFI)1 Sep 2019 - 30 Sep 2027
    People funded by this grant:
  • OTHER GRANT
    Centre for Research Training (CRT) in Foundations of Data Science
    Science Foundation Ireland (SFI)1 Sep 2019 - 30 Sep 2027
    People funded by this grant:
  • OTHER GRANT
    Centre for Research Training (CRT) in Foundations of Data Science
    Science Foundation Ireland (SFI)1 Sep 2019 - 30 Sep 2027
    People funded by this grant: