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73 records from EconBiz based on author Name
1. The fundamental review of the trading book : implications for portfolio and risk management in the banking sector
McCullagh, Orla; Cummins, Mark; Killian, Sheila;2023
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link
2. Decoupling VaR and regulatory capital : an examination of practitioners' experience of market risk regulation
McCullagh, Orla; Cummins, Mark; Killian, Sheila;2023
Type: Aufsatz in Zeitschrift; Article in journal;
Availability:
Link 3. Commodity risk in European dairy firms
Bagnarosa, Guillaume; Cummins, Mark; Dowling, Michael; Kearney, Fearghal;2022
Type: Aufsatz in Zeitschrift; Article in journal;
Availability:
Link 4. Beyond Accuracy in Artificial Intelligence Based Credit Scoring Systems : Explainability and Sustainability in Decision Support
abstractThe use of artificial intelligence (AI) and machine learning (ML) in decision support has become vital for financial services companies to remain competitive. Appraisal of AI/ML has largely focused on prediction accuracy. We argue the need to look beyond accuracy as the sole determinant for model choice. Explainable artificial intelligence (XAI) and model transparency are vital in financial services to fulfill not only regulatory requirements, but also to foster trust in the overall system. Also, in an era where the urgency of climate action is paramount, the carbon impact of AI is emerging as an important environmental concern. More broadly, digital sustainability is increasingly being seen as a social responsibility and an economic imperative. Two modeling approaches currently receiving attention in comparative studies in the credit risk management space are deep learning (DL) and gradient boosting (GB). We compare those two algorithms in the context of credit scoring but provide new dimensions to the comparative analysis. While we consider accuracy for benchmark purposes, we add to the existing literature by considering (i) explainability and (ii) digital sustainability. We show that the GB model produces explanations consistent with extant theory and empirical evidence, while highlighting inconsistencies in explainability under the DL model. Furthermore, the GB model has the advantage of computational speed, thus lowering its environmental impact for comparable operations. We contend that this makes GB the preferred choice for credit scoring, extending existing evidence on the preference of GB over DL from an accuracy perspective
Schmitt, Marc; Cummins, Mark;2023
Availability: Link Link
5. How Do Corporate Factors Affect Price Discovery Process between Equity and Credit Markets?
abstractWe conduct price discovery analysis to investigate the lead-lag relationship between equity and CDS (Credit Default Swap) markets within a corporate finance framework. Based on a sample of 89 firms covering investment grade and high yield firms, we detect stationarity and cointegration within a panel framework associated to nine corporate financial characteristics factors related to price volatility, default risk, and company capital structures. With an Expectation Maximisation (EM) Kalman filter applied to deal with nonsynchronicity and microstructure noise in one minute data, we verify its practicability in addressing the issue of missing data in a high frequency modeling setting. Contributing to the ongoing debate over the price discovery process between equity and CDS markets, we detect credit-driven price discovery in equity markets. We demonstrate that price discovery process is more credit market driven when a company’s credit risk increases, which is significantly more prominent for small-sized firms with highly volatile equity price, and increasing default probability
Zhou, Xinquan; Bagnarosa, Guillaume; Cummins, Mark;2022
Availability: Link
6. A dynamic Cholesky data imputation method for correlation structure consistency"
Atkins, Philip J.; Cummins, Mark;2022
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link Link
Citations: 1 (based on OpenCitations)
7. A real options based decision support tool for R&D investment : application to CO2 recycling technology
Deeney, Peter; Cummins, Mark; Heintz, Katharina; Pryce, Mary T.;2021
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 19 (based on OpenCitations)
8. Institutional investment in online business lending markets
Cummins, Mark; Mac an Bhaird, Ciarán; Rosati, Pierangleo; Lynn, Theo;2020
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 5 (based on OpenCitations)
9. Exploring open banking and banking-as-a-platform : opportunities and risks for emerging markets
Lynn, Theo; Rosati, Pierangelo; Cummins, Mark;2020
Type: Aufsatz im Buch; Book section;
Availability: Link
Citations: 4 (based on OpenCitations)
10. A Review of Real Options Analysis Methods for R&D Valuation in Renewable Energy Research
abstractWe propose a new real options analysis method for evaluating R&D investments using a novel Poisson process to simulate the discrete progress typical of R&D breakthroughs. We take explicit account of the technical risk of an R&D project, while the market risk and the effect of learning-by-doing in operational technologies are also explicitly modelled. We present a compound real option structure, where a European real option is used to model the fixed length term typical of early phase research, which is exercisable into an American real option to model later phase R&D. In this later phase, a successful outcome will be acted on immediately to operationalise the technology. We propose a Monte Carlo simulation approach, which models R&D progress in a stylised logistic function or 'S-bend' form, capturing the typically slow rate of R&D progress at the start of the early phase, through to more rapid improvement as the R&D advances, which then slows again as the limitations of the R&D are approached. We demonstrate that this method is applicable for evaluating the R&D investment potential in CO2 recycling technology, where an energy commodity (such as methane) is produced, using appropriate modelling for the price of the energy commodity. The method may be applied widely to R&D technology projects
Deeney, Peter; Cummins, Mark;2019
Availability: Link Link