1,485 research outputs found

    Payments per claim model of outstanding claims reserve based on fuzzy linear regression

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    There are uncertainties in factors such as inflation. Historical data and variable values are ambiguous. They lead to ambiguity in the assessment of outstanding claims reserves. The payments per claim model can only perform point estimation. But the fuzzy linear regression is based on fuzzy theory and can directly deal with uncertainty in data. Therefore, this paper proposes a payments per claim model based on fuzzy linear regression. The linear regression method and fuzzy least square method are used to estimate the parameters of the fuzzy regression equation. And the estimated results are introduced into the payments per claim model. Then, the predicted value of each accident reserve is obtained. This result is compared with that of the traditional payments per claim model. And we find that the payments per claim model of estimating the fuzzy linear regression parameters based on the linear programming method is more effective. The model gives the width of the compensation amount for each accident year. In addition, this model solves the problem that the traditional payments per claim model cannot measure the dynamic changes in reserves

    Machine and deep learning applications for improving the measurement of key indicators for financial institutions: stock market volatility and general insurance reserving risk

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    Esta tesis trata de lograr mejoras en los modelos de estimación de los riesgo financieros y actuariales a través del uso de técnicas punteras en el campo del aprendizaje automático y profundo (machine y deep learning), de manera que los modelos de riesgo generen resultados que den un mejor soporte al proceso de toma de decisiones de las instituciones financieras. Para ello, se fijan dos objetivos. En primer lugar, traer al campo financiero y actuarial los mecanismos más punteros del campo del aprendizaje automático y profundo. Los algoritmos más novedosos de este campo son de amplia aplicación en robótica, conducción autónoma o reconocimiento facial, entre otros. En segundo lugar, se busca aprovechar la gran capacidad predictiva de los algoritmos anteriormente adaptados para construir modelos de riesgo más precisos y que, por tanto, sean capaces de generar resultados que puedan dar un mejor soporte a la toma de decisiones de las instituciones financieras. Dentro del universo de modelos de riesgos financieros, esta tesis se centra en los modelos de riesgo de renta variable y reservas de siniestros. Esta tesis introduce dos modelos de riesgo de renta variable y otros dos de reservas. Por lo que se refiere a la renta variable, el primero de los modelos apila algoritmos tales como redes neuronales, bosques aleatorios o regresiones aditivas múltiples con árboles con el objetivo de mejorar la estimación de la volatilidad y, por tanto, generar modelos de riesgo más precisos. El segundo de los modelos de riesgo adapta al mundo financiero y actuarial los Transformer, un tipo de red neuronal que, debido a su alta precisión, ha apartado al resto de algoritmos en el campo del procesamiento del lenguaje natural. Adicionalmente, se propone una extensión de esta arquitectura, llamada Multi-Transformer y cuyo objetivo es mejorar el rendimiento del algoritmo inicial mediante el ensamblaje y aleatorización de los mecanismos de atención. En lo relativo a los dos modelos de reservas introducidos por esta tesis el primero de ellos trata de mejorar la estimación de reservas y generar modelos de riesgo más precisos apilando algoritmos de aprendizaje automático con modelos de reservas basados en estadística bayesiana y Chain Ladder. El segundo modelo de reservas trata de mejorar los resultados de un modelo de uso habitual, como es el modelo de Mack, a través de la aplicación de redes neuronales recurrentes y conexiones residuales

    Machine Learning in Insurance

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    Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure

    Macro-Prudential Assessment of Colombian Financial Institutions’ Systemic Importance

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    Three metrics are designed to assess Colombian financial institutions’ size, connectedness and non-­substitutability as the main drivers of systemic importance: (i) centrality as net borrower in the money market network; (ii) centrality as payments originator in the large-value payment system network, and (iii) asset value of core financial services. Two systemic importance indexes are calculated based on two different aggregation methods for the three metrics: fuzzy logic and principal component analysis. The resulting indexes are complementary and provide a comprehensive relative assessment of each financial institution’s systemic importance in the Colombian case, in which the choice of metrics pursues the macro-­prudential perspective of financial stability. They both (i) agree on the skewed (i.e. inhomogeneous) nature of systemic importance and its approximate scale-­free distribution; (ii) on the preeminence of credit institutions as the main contributors to systemic importance, and (iii) on the non-­‐trivial importance of a few non-­‐banking institutions

    Towards a General Complex Systems Model of Economic Sanctions with Some Results Outlining Consequences of Sanctions on the Russian Economy and the World

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    The main purpose of this paper is to present a complex nonlinear modelling approach to analyzing mixed capitalist economic systems. An application of a more elaborate version of this model is to explore the consequences of sanctions on the Russian economy and evaluate the model’s predictive successes or failures. Furthermore, the formal expanded nonlinear model presented in the appendix may be seen as an initial step to put the analysis of economic sanctions within a formal complex socio-economic systems framework. The results obtained from this structural complex multisectoral model so far seem fairly accurate in terms of agreement with measured values of observable economic variables. The political consequences are uncertain and are to be explored separately in a companion paper and ultimately in a book length treatment. Methodologically, the paper also presents the case for using Social Accounting Matrix (SAM)-based models for understanding problems of analyzing sanctions in an economywide context. Linear as well as Nonlinear models are presented in the appendix. The nonlinear modelling approach might prove to be especially relevant for studying the properties of multiple equilibria and complex dynamics

    FINES, FEES, RACE, AND SOCIOECONOMIC DISADVANTAGE

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    Fines and fees for legal violations finance American criminal justice systems but often at a severe cost to those incurring fines and fees. While fines and fees are a long-standing feature of the United States criminal justice system, the use of fines and fees recently captured attention of scholars in the wake of questions prompted by recent social, political, and legal developments. The central question is: What, if any, association is there between race, socioeconomic disadvantage, and county fine and fee issuance? The main hypothesis is: Fine and fee issuance of the most populous counties positively and significantly associate with race and socioeconomic disadvantage. To test this hypothesis, census data and multivariate regressions are exploited to examine associations between county fine and fee issuance, race, and socioeconomic disadvantage. Conflict-oriented theory serves to rationalize findings. A conflict theorist would expect areas with comparatively low socioeconomic status and high concentrations of certain minorities to fine relatively heavily. The findings from this study indicate confirmation that counties with a higher percentage of Black residents issue more fines and fees on a per capita basis than counties with a lower percentage of Black residents. Yet, the findings from this study fall short of indicating counties with comparatively low socioeconomic status are more likely to issue fines and fees

    Essays on Risk Pricing in Insurance

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    Pricing risks in the insurance business is an essential task for actuaries. Implementing the appropriate pricing techniques to improve risk management and optimize its financial gain requires a thorough understanding of underlying risks and their interactions. In this dissertation, I address risk pricing in the context of insurance company by reviewing methods applied in practice, proposing new models, and also exploring different aspects of insurance risks. This dissertation consists of three chapters. The first chapter provides a survey of existing capital allocation methods, including common approaches based on the gradients of risk measures and “economic” allocation arising from counterparty risk aversion. All methods are implemented in two example settings: binomial losses and using loss realizations from a catastrophe reinsurer. The stability of allocations is assessed based on sensitivity analysis with regards to losses. The results show that capital allocations appear to be intrinsically (geometrically) related, although the stability varies considerably. Stark differences exist between common and “economic” capital allocations. The second chapter develops a dynamic profit maximization model for a financial institution with liabilities of varying maturity, and uses it for determining the term structure of capital costs. iii As a key contribution, the theoretical, numerical, and empirical results show that liabilities with different terms are assessed differently, depending on the company’s financial situation. In particular, for a financially constrained firm, value-adjustments due to financial frictions for liabilities in the far future are less pronounced than for short-term obligations, resulting in a strongly downward sloping term structure. The findings provide guidance for performance measurement in financial institutions. The third chapter estimates a flexible affine stochastic mortality model based on a set of US term life insurance prices using a generalized method of moments approach to infer forward-looking, market-based mortality trends. The results show that neither mortality shocks nor stochasticity in the aggregate trend seem to affect the prices. In contrast, allowing for heterogeneity in the mortality rates across carriers is crucial. The major conclusion is that for life insurance, rather than aggregate mortality risk, the key risks emanate from the composition of the portfolio of policyholders. These findings have consequences for mortality risk management and emphasize important directions of mortality-related actuarial research

    Public-Private Partnerships in the United States: The Relevance of Public Policy and Finance

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    The infrastructure deficit is among the most significant challenges facing the United States. The Trump and Biden administrations called for using public-private partnerships (PPPs) to rebuild the nation’s crumbling infrastructure. As distinct arrangements that are part of both the public and private sectors, PPPs pose critical questions to public policy and administration. They have also gained popularity as a result of the New Public Management and Collaborative Governance movements. By synthesizing the theories of the economics of hybrid organizations, public choice, and public value, my research suggests that PPP formation, management, and performance evaluation require the strategic interactions of both sectors, without one dominating the other. Moreover, it addresses the gap in the literature on public-private financial interactions by examining private capital engagement and its interactions with the government’s motivations, strategies, and performances. My dissertation makes three main contributions. First, my analysis of state-level data between 2000 and 2019 demonstrates that governments propose and use PPPs, with or without private capital engagement, for different reasons. Second, through a fuzzy-set qualitative comparative analysis of 33 PPPs, I show that the effectiveness of governmental strategies for leveraging private capital is mixed, and the configuration of strategies matters. Third, I suggest a public value framework to evaluate PPP performance and use a comparative case study to examine the effects of private capital engagement on PPP accountability, manageability, and substantive outcomes. Using those results, I explain how private capital engagement can threaten and strengthen PPP public value delivery depending on the public value dimensions and the project’s characteristics. Given the state of its infrastructure, the U.S. has the potential to be the world’s largest PPP market. However, governments at all levels still struggle with complex PPP structures and practices. My research provides important policy recommendations on how to structure and govern private investment, and how to ensure the public value of PPPs

    A Survey of Systemic Risk Analytics

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    We provide a survey of 31 quantitative measures of systemic risk in the economics and finance literature, chosen to span key themes and issues in systemic risk measurement and management. We motivate these measures from the supervisory, research, and data perspectives in the main text and present concise definitions of each risk measure—including required inputs, expected outputs, and data requirements—in an extensive Supplemental Appendix. To encourage experimentation and innovation among as broad an audience as possible, we have developed an open-source Matlab® library for most of the analytics surveyed, which, once tested, will be accessible through the Office of Financial Research (OFR) at http://www.treasury.gov/initiatives/wsr/ofr/Pages/default.aspx.United States. Dept. of the Treasury. Office of Financial ResearchMassachusetts Institute of Technology. Laboratory for Financial EngineeringNational Science Foundation (U.S.) (Grant ECCS-1027905
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