https://bura.brunel.ac.uk/handle/2438/8627 Tue, 25 Aug 2026 15:33:25 GMT 2026-08-25T15:33:25Z https://bura.brunel.ac.uk/handle/2438/33707 Title: Modelling Defaultable Loan Repayment Cashflows Using Neural Hawkes Processes Authors: Deol, Jugraj; Date, Paresh Abstract: ... Description: ... Thu, 01 Jan 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33707 2026-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33700 Title: A family of coherent risk measures: an infinite weighted average of Value-at-Risk Authors: Jiang, Rong; Jones, MC; Wang, Jiangfeng; Yu, Keming Abstract: Value-at-Risk (VaR) remains one of the most widely used risk measures in finance due to its simplicity, interpretability, and regulatory prominence, particularly within the Basel framework. However, VaR is not a coherent risk measure, as it generally fails to satisfy the subadditivity property. This paper demonstrates that, although finite weighted combinations of VaR-type functionals do not necessarily yield coherent risk measures, appropriately constructed infinite weighted averages of quantiles can generate coherent risk measures. Motivated by this observation, we introduce a two-parameter weight function that jointly depends on the quantile level and a position parameter, and propose a novel coherent risk measurement framework termed generalized quantile regression (GQR). In particular, we establish explicit and verifiable conditions on the weight function under which the resulting risk functional satisfies coherence, reversibility, monotonicity, and comparability across quantile levels. The proposed framework is flexible, interpretable, and unifying: it encompasses a broad class of existing coherent risk measures as special cases while also generating several new and economically meaningful risk measures. These results provide a characterization that is not explicitly available in conventional distortion or spectral risk measure frameworks. We further develop corresponding nonparametric estimators, including in multi-dimensional settings, and investigate their asymptotic properties. Empirical studies demonstrate the effectiveness of the proposed GQR framework in both risk assessment and portfolio optimization. Description: Accepted author version posted online: 10 Aug 2026.; Supplemental material is available online at: https://www.tandfonline.com/doi/full/10.1080/07350015.2026.2714065# . Mon, 10 Aug 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33700 2026-08-10T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33660 Title: Semi-supervised learning for linear extremile regression Authors: Jiang, Rong; Wang, Jiangfeng; Yu, Keming Abstract: Extremile regression, as a least squares analog of quantile regression, is potentially a useful tool for modeling and understanding the extreme tails of a distribution. However, existing extremile regression methods, as nonparametric approaches, may face challenges in high-dimensional settings due to data sparsity, computational inefficiency, and the risk of overfitting. While linear regression, particularly in high-dimensional settings, serves as the foundation for many other statistical and machine learning models due to its simplicity, interpretability, and relatively easy implementation, this paper introduces a novel definition of linear extremile regression along with an accompanying estimation methodology. The regression coefficient estimators of this method achieve root n consistency, which nonparametric extremile regression may not provide. In particular, while semi-supervised learning can leverage unlabeled data to make more accurate predictions and avoid overfitting to small labeled datasets in high-dimensional spaces, we propose a semi-supervised learning to enhance estimation efficiency, even when the specified linear extremile regression model may be misspecified. Both simulation studies and real data analyses demonstrate the finite sample performance of our proposed methods. Sat, 16 May 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33660 2026-05-16T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33561 Title: Enhanced indexation using both equity assets and index options Authors: Valle, CA; Beasley, JE Abstract: In this paper we consider how we can include index options in enhanced indexation. We present the concept of an “option strategy” which enables us to treat options as equivalent to an asset. An option strategy for a known set of options is a specified set of rules which detail how these options are to be traded (i.e. bought, rolled over, sold) depending upon market conditions. We consider option strategies in the context of enhanced indexation, but we highlight how they have much wider applicability in terms of portfolio optimisation. We use an enhanced indexation approach based on second-order stochastic dominance (SSD). We show that a SSD cutting plane solution approach can be extended to solve, to proven optimality, cardinality constrained SSD problems with limitations on the proportion of the portfolio invested in any asset. We consider monthly index options for the S&P 500, using a dataset of daily stock prices over the period 2017–2025 that has been manually adjusted to account for index composition. This dataset is made publicly available for use by future researchers. Our computational results indicate that introducing option strategies in an enhanced indexation setting offers clear benefits in terms of improved out-of-sample performance. This applies whether we use equities or an exchange-traded fund as part of the enhanced indexation portfolio. Sat, 27 Jun 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33561 2026-06-27T00:00:00Z