1,720,988 research outputs found
Designing sound deposit insurances
Full text linkDeposit insurances were blamed for encouraging the excessive risk taking behavior during the 2008 financial crisis. The main reason for this destructive behavior was “moral hazard risk”, usually caused by inappropriate insurance policies. While this concept is known and well-studied for ordinary insurance contracts, yet needs to be further studied for insurances on financial positions. In this paper, we set up a simple theoretical framework for a bank that buys an insurance policy to protect its position against market losses. The main objective is to find the optimal insurance contract that does not produce the risk of moral hazard, while keeping the bank’s position solvent. In a general setup we observe that an optimal policy is a multi-layer policy. In particular, we obtain a close form solution for the optimal insurance contracts when a bank measures its risk by either Value at Risk or Conditional Value at Risk. We show the optimal solutions for these two cases are two-layer policies
Representation and approximation of convex dynamic risk measures with respect to strong-weak topologies
Full text linkWe provide a representation for strong-weak continuous dynamic risk measures from Lp into Lpt spaces where these spaces are equipped respectively with strong and weak topologies and p is a finite number strictly larger than one. Conversely, we show that any such representation that admits a compact (with respect to the product of weak topologies) sub-differential generates a dynamic risk measure that is strong--weak continuous. Furthermore, we investigate sufficient conditions on the sub-differential for which the essential supremum of the representation is attained. Finally, the main purpose is to show that any convex dynamic risk measure that is strong-weak continuous can be approximated by a sequence of convex dynamic risk measures which are strong--weak continuous and admit compact sub-differentials with respect to the product of weak topologies. Throughout the arguments, no conditional translation invariance or monotonicity assumptions are applied
On some aspects of quantitative risk management: theoretical and empirical implications for agricultural goods
Full text linkTo the best of our knowledge, there are only a few papers on the area of quantitative risk management that is applied to agricultural markets. This thesis aims to fill this gap by studying different aspects of the agricultural datasets within three main chapters
Living and Future Tools for Risk Assessment-An Examination of the Possibilities for Fusion
Full text linkThe process of risk assessment is crucial across a wide range of institutions, sectors and industries. Regulatory bodies worldwide are confronted with a plethora of challenges in assessing risks and uncertainties. The sources of those challenges are diverse, but all have a common association with the degree of confidence in predicting and quantifying risks. When the level of confidence is high, regulators tend to specify the outputs and take quantitatively informed preventive measures. However, when levels of confidence are lower, regulators may favour leaning towards more qualitative considerations of risks to concentrate on resilience building and absorption of the adverse consequences of risks. This challenge is also echoed by complex scientific risk assessment methods by generating more information which makes the process questionable about the ways of addressing issues such as multiple risks, multiple exposures, and sensitivity or susceptibility.
In order to obtain a holistic approach to striking a balance between scientific, economics and safety measures, this thesis has employed a qualitative method to map and evaluate risk assessment methodologies and decision support tools used in regulatory agencies to: investigate and evaluate the main challenges associated with the implementation and practice of risk assessment and decision-making processes; investigate the range of presently in use risk assessment tools and techniques and evaluate their efficacy; assess the risk evaluation and decision-making strategies/criteria in regulatory agencies; examine the possibility for integration of social and natural science modes of risk assessment; and develop and validate a framework for facilitating future assessment of risk, tailored to the requirements of regulatory agencies.
The study involved semi-structured interviews with 36 professionals and practitioners working in risk regulatory organisations from the UK, Germany, France, Belgium, the Netherlands and New Zealand. The results obtained from the study indicate that sources of identified challenges are not only related to the organisational factors, but they are also linked to rational, technical and expert parameters. This determined that assessing risk is an integrated and complex process founded on organisational, social, ethical, political and economic systems and mechanisms. The result of the study also specified that the selection and implementation of tools and techniques in risk identification and analysis are highly influenced by organisations’ characteristics and their field of activities, which can lead to dissimilar decision responses by regulatory agencies. Further, the study highlighted lack of consistency in risk assessment practices among risk regulatory agencies. It explained that the diversity of information, opinion, and actor perspectives are the key issues in risk evaluation and impact assessment amongst regulatory agencies. To address all the identified challenges within risk regulatory organisations, this study designed and developed the Enhanced Risk Assessment (ERA) framework to: support regulatory bodies to reflect on and conceptualise risk assessment in an integrative manner; determine the scope and limits of risk assessment processes; deliver more consistent and systematic risk analysis and evaluation; and transform underestimated or overestimated risk management plans to realistic approaches. This framework facilitates risk assessment and management through four stages; namely risk information, analysis, evaluation, and decision-making. It was tested and validated by experts in a number of regulatory agencies in the UK and EU
Quantitative Risk Management in Agricultural Business
Full text linkThis open access volume explores the cutting edge of quantitative methods in agricultural risk management and insurance. Composed of insightful articles authored by field experts, focusing on innovation, recent advancements, and the use of technology and data sciences, it bridges the gap between theory and practice through empirical studies, concrete examples and case analyses. Evolving challenges in risk management have called for the development of new, groundbreaking models. Beyond presenting the theoretical foundations of these models, this book discusses their real-world applications, providing tangible insights into how innovative modeling can elevate risk management strategies in the agricultural sector. The latest risk management tools incorporate novel concepts such as index insurance, price index risk management frameworks and risk pools. The practical implications of these approaches are investigated, and their impact on contemporary agricultural risk mitigation and insurance practices is examined. Field experiences illustrate the implementation of these tools and their resulting outcomes. Modern data analysis techniques in agricultural risk and insurance include machine learning, spatial analysis, text analysis, and deep learning. In addition to scrutinizing these ideas, the authors introduce an economic perspective towards risk, highlighting areas that have developed thanks to technological progress. Examples illustrate how these combined methodologies contribute to informed decision-making in agriculture, and their potential benefits and challenges are considered. This carefully compiled volume will be a valuable reference for researchers, practitioners, and students intrigued by the dynamic intersection of agricultural risk management and insurance practices
On some aspects of coherent risk measures and their applications
No full textLe sujet principal de cette thèse porte sur les mesures de risque. L'objectif général est d'investiguer certains aspects des mesures de risque dans les applications financières. Le cadre théorique de ce
travail est celui des mesures cohérentes de risque telle que définie dans Artzner et al (1999). Mais ce n'est pas la seule classe de mesure du risque que nous étudions. Par exemple, nous étudions aussi quelques aspects des "statistiques naturelles
de risque" (en anglais natural risk statistics) Kou et al (2006) et des mesures convexes du risque Follmer and Schied(2002). Les contributions principales de cette
thèse peuvent être regroupées selon trois axes: allocation de capital, évaluation des risques et capital requis et solvabilité.
Dans le chapitre 2 nous caractérisons les mesures de risque avec la propriété de Lebesgue sur l'ensemble des processus bornés càdlàg (continu à droite, limité à gauche). Cette caractérisation nous permet de présenter deux applications dans l'évaluation des risques et l'allocation de
capital. Dans le chapitre 3, nous étendons la notion de statistiques naturelles de risque à l'espace des suites infinies.
Cette généralisation nous permet de construire de façon cohérente des mesures de risque pour des bases de données de n'importe quelle taille. Dans le chapitre 4, nous discutons le concept de "bonnes affaires" (en anglais Good Deals), pour notamment caractériser les situations du marché où ces positions pathologiques
sont présentes. Finalement, dans le chapitre 5, nous essayons de relier les trois chapitres en étendant la définition de "bonnes affaires" dans un cadre plus
large qui comprendrait les mesures de risque analysées dans les chapitres 2 et 3.The aim of this thesis is to study several aspects of risk measures particularly in the context of financial applications. The primary framework that we use is that of coherent risk measures as defined in Artzner et al (1999). But this is not the only class of risk measures that we study here. We also investigate the concepts of natural risk statistics Kou et al (2006) and convex risk measure Follmer/ and Schied (2002). The main contributions of this Thesis can be classified in three main axes: Capital allocation, risk measurement and capital requirement and solvency. In chapter 2, we characterize risk measures with the Lebesgue property on bounded càdlàg processes. This allows to present two applications in risk assessment and capital allocation. In chapter 3, we extend the concept of natural risk statistics to the space of infinite sequences. This has been done in order to introduce a consistent way of constructing risk measures for data bases of any size. In chapter 4, we discuss the concept of Good Deals and how to deal with a situation where these pathological positions are present in the market. Finally, in chapter 5, we try to relate all three chapters by extending the definition of Good Deals to a larger set of risk measures that somehow includes the discussions in chapters 2 and 3
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