pwc-archive/papers-with-abstracts huggingface dataset:
MAFin: Motif Detection in Multiple Alignment Files
Authors: Michail Patsakis, Kimonas Provatas, Fotis A. Baltoumas, Nikol Chantzi, Ioannis Mouratidis, Georgios A. Pavlopoulos, Ilias Georgakopoulos-Soares
Date: 2024-10-14 00:00:00

Motivation: Genome and Proteome Alignments, represented by the Multiple Alignment File (MAF) format, have become a standard approach in the field of comparative genomics and proteomics. However, current approaches lack a direct method for motif detection within MAF files. To address this gap, we present MAFin, a novel tool that enables efficient motif detection and conservation analysis in MAF files, streamlining genomic and proteomic research. Results: We developed MAFin, the first motif detection tool for Multiple Alignment Format files. MAFin enables the multithreaded search of conserved motifs using three approaches: 1) by using user-specified k-mers to search the sequences. 2) with regular expressions, in which case one or more patterns are searched, and 3) with predefined Position Weight Matrices. Once the motif has been found, MAFin detects the motif instances and calculates the conservation across the aligned sequences. MAFin also calculates a conservation percentage, which provides information about the conservation levels of each motif across the aligned sequences, based on the number of matches relative to the length of the motif. A set of statistics enable the interpretation of each motif's conservation level, and the detected motifs are exported in JSON and CSV files for downstream analyses. Availability: MAFin is released as a Python package under the GPL license as a multi-platform application and is available at: https://github.com/Georgakopoulos-Soares-lab/MAFin. Contact: izg5139@psu.edu
Mechanical and suture-holding properties of a UV-cured atelocollagen membrane with varied crosslinked architecture
Authors: Ruya Zhang, Charles Brooker, Laura L. E. Whitehouse, Neil H. Thomson, David Wood, Giuseppe Tronci
Date: 2024-10-17 00:00:00

The mechanical competence and suturing ability of collagen-based membranes are paramount in Guided Bone Regeneration (GBR) therapy, to ensure damage-free implantation, fixation and space maintenance in vivo. However, contact with the biological medium can induce swelling of collagen molecules, yielding risks of membrane sinking into the bone defect, early loss of barrier function, and irreversibly compromised clinical outcomes. To address these challenges, this study investigates the effect of the crosslinked network architecture on both mechanical and suture-holding properties of a new atelocollagen (AC) membrane. UV-cured networks were obtained via either single functionalisation of AC with 4-vinylbenzyl chloride (4VBC) or sequential functionalisation of AC with both 4VBC and methacrylic anhydride (MA). The wet-state compression modulus (Ec), Atomic Force Microscopy elastic modulus (EAFM) and swelling ratio (SR) were significantly affected by the UV-cured network architecture, leading up to a three-fold reduction in SR, and about two-fold increase in both Ec and EAFM, in the sequentially functionalised, compared to the single-functionalised, samples. Electron microscopy, dimensional analysis and compression testing revealed the direct impact of the ethanol series dehydration process on membrane microstructure, yielding densification of the freshly synthesised porous samples and a pore-free microstructure with increased Ec. Noteworthy, the single-functionalised, but not the sequentially functionalised, samples displayed higher suture retention strength in both the dry state and following 1 hour in Phosphate Buffered Saline (PBS), compared to Bio-Gide(r). These structure-property relationships confirm the key role played by the molecular architecture of covalently crosslinked collagen, aimed towards long-lasting resorbable membranes for predictable GBR.
European Option Pricing in Regime Switching Framework via Physics-Informed Residual Learning
Authors: Naman Krishna Pande, Puneet Pasricha, Arun Kumar, Arvind Kumar Gupta
Date: 2024-10-14 00:00:00

In this article, we employ physics-informed residual learning (PIRL) and propose a pricing method for European options under a regime-switching framework, where closed-form solutions are not available. We demonstrate that the proposed approach serves an efficient alternative to competing pricing techniques for regime-switching models in the literature. Specifically, we demonstrate that PIRLs eliminate the need for retraining and become nearly instantaneous once trained, thus, offering an efficient and flexible tool for pricing options across a broad range of specifications and parameters.
Can GANs Learn the Stylized Facts of Financial Time Series?
Authors: Sohyeon Kwon, YongJae lee
Date: 2024-10-13 00:00:00
Tasks: Time Series

In the financial sector, a sophisticated financial time series simulator is essential for evaluating financial products and investment strategies. Traditional back-testing methods have mainly relied on historical data-driven approaches or mathematical model-driven approaches, such as various stochastic processes. However, in the current era of AI, data-driven approaches, where models learn the intrinsic characteristics of data directly, have emerged as promising techniques. Generative Adversarial Networks (GANs) have surfaced as promising generative models, capturing data distributions through adversarial learning. Financial time series, characterized 'stylized facts' such as random walks, mean-reverting patterns, unexpected jumps, and time-varying volatility, present significant challenges for deep neural networks to learn their intrinsic characteristics. This study examines the ability of GANs to learn diverse and complex temporal patterns (i.e., stylized facts) of both univariate and multivariate financial time series. Our extensive experiments revealed that GANs can capture various stylized facts of financial time series, but their performance varies significantly depending on the choice of generator architecture. This suggests that naively applying GANs might not effectively capture the intricate characteristics inherent in financial time series, highlighting the importance of carefully considering and validating the modeling choices.
Exploring the Interplay of Skewness and Kurtosis: Dynamics in Cryptocurrency Markets Amid the COVID-19 Pandemic
Authors: Ariston Karagiorgis, Antonis Ballis, Konstantinos Drakos, Christos Kallandranis
Date: 2024-09-30 00:00:00

We examine how skewness interacts with kurtosis within the cryptocurrency market. We show that during the COVID-19 pandemic there are more clusters of observations around the two flanks, highlighting the presence of a volatile behavior. Moreover, we document the evolvement of the interrelationship as the pandemic progresses, identifying the domination of the extremes. Our findings advance the thinking that by exploiting the interrelationship between the two higher moments of cryptocurrencies, investors and researchers can have in their arsenal an additional analytic tool.
Price impact and long-term profitability of energy storage
Authors: Roxana Dumitrescu, Redouane Silvente, Peter Tankov
Date: 2024-10-16 00:00:00
Tasks: RTE

We study the price impact of storage facilities in electricity markets and analyze the long-term profitability of these facilities in prospective scenarios of energy transition. To this end, we begin by characterizing the optimal operating strategy for a stylized storage system, assuming an arbitrary exogenous price process. Following this, we determine the equilibrium price in a market comprising storage systems (acting as price takers), renewable energy producers, and conventional producers with a defined supply function, all driven by an exogenous demand process. The price process is characterized as a solution to a fully coupled system of forward-backward stochastic differential equations, for which we establish existence and uniqueness under appropriate assumptions. We finally illustrate the impact of storage on intraday electricity prices through numerical examples and show how the revenues of storage agents may evolve in prospective energy transition scenarios from RTE, the French energy electricity network operator, taking into account both the increasing penetration of renewable energies and the self-cannibalization effect of growing storage capacity. We find that both the average revenues and the interquantile ranges increase in all scenarios, highlighting higher expected profits and higher risk for storage assets.
Backtesting Framework for Concentrated Liquidity Market Makers on Uniswap V3 Decentralized Exchange
Authors: Andrey Urusov, Rostislav Berezovskiy, Yury Yanovich
Date: 2024-10-13 00:00:00

Decentralized finance (DeFi) has revolutionized the financial landscape, with protocols like Uniswap offering innovative automated market-making mechanisms. This article explores the development of a backtesting framework specifically tailored for concentrated liquidity market makers (CLMM). The focus is on leveraging the liquidity distribution approximated using a parametric model, to estimate the rewards within liquidity pools. The article details the design, implementation, and insights derived from this novel approach to backtesting within the context of Uniswap V3. The developed backtester was successfully utilized to assess reward levels across several pools using historical data from 2023 (pools Uniswap v3 for pairs of altcoins, stablecoins and USDC/ETH with different fee levels). Moreover, the error in modeling the level of rewards for the period under review for each pool was less than 1\%. This demonstrated the effectiveness of the backtester in quantifying liquidity pool rewards and its potential in estimating LP's revenues as part of the pool rewards, as focus of our next research. The backtester serves as a tool to simulate trading strategies and liquidity provision scenarios, providing a quantitative assessment of potential returns for liquidity providers (LP). By incorporating statistical tools to mirror CLMM pool liquidity dynamics, this framework can be further leveraged for strategy enhancement and risk evaluation for LPs operating within decentralized exchanges.
Cross-Currency Basis Swaps Referencing Backward-Looking Rates
Authors: Yining Ding, Ruyi Liu, Marek Rutkowski
Date: 2024-10-11 00:00:00

The financial industry has undergone a significant transition from the London Interbank Offered Rate (LIBOR) to Risk Free Rates (RFR) such as, e.g., the Secured Overnight Financing Rate (SOFR) in the U.S. and the AUD Overnight Index Average (AONIA) in Australia, as the primary benchmark rate for borrowing costs. The paper examines the pricing and hedging method for SOFR-related financial products in a cross-currency context with the special emphasis on the Compound SOFR vs Average AONIA cross-currency basis swaps. While the SOFR and AONIA serve as a particular case of a cross-currency basis swap (CCBS), the approach developed is able to handle backward-looking term rates for any two currencies. We give explicit pricing and hedging results for collateralized cross-currency basis swaps using interest rate and currency futures contracts as hedging tools within an arbitrage-free multi-curve setting.
Variance-Hawkes Process and its Application to Energy Markets
Authors: Joshua McGillivray, Anatoliy Swishchuk
Date: 2024-10-10 00:00:00

We define a new model using a Hawkes process as a subordinator in a standard Brownian motion. We demonstrate that this Hawkes subordinated Brownian motion or more succinctly, variance-Hawkes process can be fit to 2018 and 2019 natural gas and crude oil front-month futures log returns. This variance-Hawkes process allows financial models to easily have clustering effects encoded into their behaviour in a simple and tractable way. We also compare the simulations of a square of a variance Hawkes process with its Ito formula. We simulate both processes and compare their distributions, trajectories, and percent errors across multiple runs. We derive the generator relating to this Hawkes subordinated Brownian motion, calculate several moments, and conjecture its distribution. We also provide explicit solutions to the second moments of the Hawkes process and its intensity as well as the cross moment between the Hawkes process and its intensity in the case of an exponential kernel.
Optimal mutual insurance against systematic longevity risk
Authors: John Armstrong, James Dalby
Date: 2024-10-10 00:00:00

We mathematically demonstrate how and what it means for two collective pension funds to mutually insure one another against systematic longevity risk. The key equation that facilitates the exchange of insurance is a market clearing condition. This enables an insurance market to be established even if the two funds face the same mortality risk, so long as they have different risk preferences. Provided the preferences of the two funds are not too dissimilar, insurance provides little benefit, implying the base scheme is effectively optimal. When preferences vary significantly, insurance can be beneficial.
Aproximación práctica a los métodos de selección de portafolios de inversión
Authors: Carlos Minutti-Martinez
Date: 2024-10-14 00:00:00
Tasks: Portfolio Optimization

This paper explores the practical approach to portfolio selection methods for investments. The study delves into portfolio theory, discussing concepts such as expected return, variance, asset correlation, and opportunity sets. It also presents the efficient frontier and its application in the Markowitz model, which employs mean-variance optimization techniques. An alternative approach based on the mean-semivariance model is introduced. This model accounts for the skewness and kurtosis of the asset return distribution, providing a more comprehensive view of risk and return. The study also addresses the practical implementation of these models, including the use of genetic algorithms to optimize portfolio selection. Additionally, transaction costs and integer constraints in portfolio optimization are considered, demonstrating the applicability of the Markowitz model. -- Este documento explorar la aproximaci\'on pr\'actica a los m\'etodos de selecci\'on de portafolios para inversiones. El estudio profundiza en la teor\'ia de los portafolios, discutiendo conceptos como el rendimiento esperado, la varianza, la correlaci\'on entre activos y los conjuntos de oportunidades. Tambi\'en presenta la frontera eficiente y su aplicaci\'on en el modelo de Markowitz, que utiliza t\'ecnicas de optimizaci\'on media-varianza. Se introduce un enfoque alternativo basado en el modelo media-semivarianza. Este modelo tiene en cuenta la asimetr\'ia y la curtosis de la distribuci\'on de retornos de los activos, proporcionando una visi\'on m\'as completa de riesgo y rendimiento. El estudio tambi\'en aborda la implementaci\'on pr\'actica de estos modelos, incluyendo el uso de algoritmos gen\'eticos para optimizar la selecci\'on de portafolios. Adem\'as, se consideran los costos de transacci\'on y las restricciones enteras en la optimizaci\'on del portafolio.
Sample Average Approximation for Portfolio Optimization under CVaR constraint in an (re)insurance context
Authors: Jérôme Lelong, Véronique Maume-Deschamps, William Thevenot
Date: 2024-10-14 00:00:00
Tasks: Portfolio Optimization

We consider optimal allocation problems with Conditional Value-At-Risk (CVaR) constraint. We prove, under very mild assumptions, the convergence of the Sample Average Approximation method (SAA) applied to this problem, and we also exhibit a convergence rate and discuss the uniqueness of the solution. These results give (re)insurers a practical solution to portfolio optimization under market regulatory constraints, i.e. a certain level of risk.
On the valuation of life insurance policies for dependent coupled lives
Authors: Kira Henshaw, Cedric H. A. Koffi, Olivier Menoukeu Pamen, Raghid Zeineddine
Date: 2024-10-01 00:00:00

In this paper, we investigate a complex variation of the standard joint life annuity policy by introducing three distinct contingent benefits for the surviving member(s) of a couple, along with a contingent benefit for their beneficiaries if both members pass away. Our objective is to price this innovative insurance policy and analyse its sensitivity to key model parameters, particularly those related to the joint mortality framework. We employ the $QP$-rule (described in Section \ref{secgenset}), which combines the real-world probability measure $P$ for mortality risk with risk-neutral valuation under $Q$ for financial market risks. The model enables explicit pricing expressions, computed using efficient numerical methods. Our results highlight the interdependent risks faced by couples, such as broken-heart syndrome, providing valuable insights for insurers and policyholders regarding the pricing influences of these factors.
Impact of social factors on loan delinquency in microfinance
Authors: Cedric H. A. Koffi, Viani Biatat Djeundje, Olivier Menoukeu Pamen
Date: 2024-10-17 00:00:00

This paper develops multistate models to analyse loan delinquency in the microfinance sector, using data from Ghana. The models are designed to account for both partial repayments and the short repayment durations typical in microfinance, focusing on estimating the probability of transitions between two or three repayment states, including delinquency. Social variables, such as religious and cultural factors, were found to play a statistically significant role in influencing repayment behavior, highlighting the impact of societal dynamics on financial outcomes. We explored both time-independent and time-dependent frailty models to capture unobserved heterogeneity. Overall, the findings emphasize the importance of social factors in delinquency but suggest limited predictive gains from incorporating frailties into multistate models.
Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A Confidence-Driven Gating Mechanism
Authors: Mehdi Hosseini Chagahi, Niloufar Delfan, Saeed Mohammadi Dashtaki, Behzad Moshiri, Md. Jalil Piran
Date: 2024-10-01 00:00:00
Tasks: Fraud Detection, Graph Neural Network

The rapid expansion of e-commerce and the widespread use of credit cards in online purchases and financial transactions have significantly heightened the importance of promptly and accurately detecting credit card fraud (CCF). Not only do fraudulent activities in financial transactions lead to substantial monetary losses for banks and financial institutions, but they also undermine user trust in digital services. This study presents a new stacking-based approach for CCF detection by adding two extra layers to the usual classification process: an attention layer and a confidence-based combination layer. In the attention layer, we combine soft outputs from a convolutional neural network (CNN) and a recurrent neural network (RNN) using the dependent ordered weighted averaging (DOWA) operator, and from a graph neural network (GNN) and a long short-term memory (LSTM) network using the induced ordered weighted averaging (IOWA) operator. These weighted outputs capture different predictive signals, increasing the model's accuracy. Next, in the confidence-based layer, we select whichever aggregate (DOWA or IOWA) shows lower uncertainty to feed into a meta-learner. To make the model more explainable, we use shapley additive explanations (SHAP) to identify the top ten most important features for distinguishing between fraud and normal transactions. These features are then used in our attention-based model. Experiments on three datasets show that our method achieves high accuracy and robust generalization, making it effective for CCF detection.
Sensitivity Analysis of Ruin of an Insurance Company in Ghana
Authors: Daniel Tawiah Pabifio
Date: 2024-09-29 00:00:00
Tasks: Sensitivity

An insurance company, as a risk bearer, is exposed to the likelihood of running into ruin. This is the situation where the initial surplus falls below zero. There is the need to find the required start-up capital to hedge against insolvency. Most researchers, irrespective of whether the test for claim dependency holds or not, assume claim independence in their computing of ruin probabilities to start up their initial capital. The objective of this study is to carry out comparative sensitivity analysis of ruin probability under both assumptions of dependence and independence, irrespective of whether the data exhibits independence or not, based on data from an insurance company in Ghana. Secondary data from an insurance company was obtained from the National Insurance Commission (NIC) for the period of 2013 to 2017. The study employed copulas to determine the claim dependence among the various insurance products and the company in general. The study concluded that when there is dependence in the claim data, computing the ruin probability based on the assumption of independence results in underestimation. Among the various insurance products, the most profitable insurance product was motor insurance, and Fire and Allied insurance exhibited the highest dependency. At a higher start-up capital, when claims are dependent, assuming independence in calculating the ruin probability results in a significant difference. Hence, it was recommended that insurance companies should adopt the assumption of dependence between the claims data as the initial reserves become larger, particularly for larger insurance companies, to avoid misleading results. Also, awareness of the perils and consequences of fire outbreaks and disasters should be raised to the general public to reduce the risk of frequent occurrence.
Detecting Structural breakpoints in natural gas and electricity wholesale prices via Bayesian ensemble approach, in the era of energy prices turmoil of 2022 period: the cases of ten European markets
Authors: Panayotis G. Papaioannou, George P. Papaioannou, George Evangelidis, George Gavalakis
Date: 2024-09-30 00:00:00
Tasks: Time Series

We investigate the impact of several critical events associated with the Russo Ukrainian war, started officially on 24 February 2022 with the Russian invasion of Ukraine, on ten European electricity markets, two natural gas markets (the European reference trading hub TTF and N.Y. NGNMX market) and how these markets interact to each other and with USDRUB exchange rate, a financial market. We analyze the reactions of these markets, manifested as breakpoints attributed to these critical events, and their interaction, by using a set of three tools that can shed light on different aspects of this complex situation. We combine the concepts of market efficiency, measured by quantifying the Efficient market hypothesis (EMH) via rolling Hurst exponent, with structural breakpoints occurred in the time series of gas, electricity and financial markets, the detection of which is possible by using a Bayesian ensemble approach, the Bayesian Estimator of Abrupt change, Seasonal change and Trend (BEAST), a powerful tool that can effectively detect structural breakpoints, trends, seasonalities and sudden abrupt changes in time series. The results show that the analyzed markets have exhibited different modes of reactions to the critical events, both in respect of number, nature, and time of occurrence (leading, lagging, concurrent with dates of critical events) of breakpoints as well as of the dynamic behavior of their trend components.