Modeling the Credit Risk in Agricultural Mortgages: A Critical Review of the Farm Credit Administration’s Credit Risk Model for Farmer Mac

Farmer Mac is the GSE charged with creating a secondary market in loans backed by agricultural real estate. The Farm Credit Administration (FCA) has estimated a credit risk model for agricultural mortgages. This model is a key determinant of Farmer Mac’s risk based capital (RBC) requirement. This paper reviews both the structure of FCA’s credit risk model, and the data used by FCA’s contractors to estimate the model. Serious concerns are raised about both data quality and the econometric specification in use. Under Basle II, RBC models will proliferate. Assessing the validity of credit risk models will become increasingly important.Basle II, risk based capital, credit risk, agricultural mortgage finance

National legislative systems and foreign standards and regulations: The case of International Financial Reporting Standards adoption

This study is focused on the linkages between the legislative families as descriptors of national legislative systems and International Financial Reporting Standards (IFRSs) issued by the International Accounting Standards Board (IASB). We consider such analysis as a case study for the more general issue of explaining the preferences of national regulators in the adoption of foreign norms, rules, standards and practices. By using a dataset of 162 jurisdictions and dummy variables designed to capture the current stage of IFRSs adoption and, respectively, the taxonomy of their legislative systems, we find that a full IFRSs adoption is more likely to occur in countries which have principles-based on legislative mono-systems. In addition, we observe that a strong rule of law, with an effective mechanism of property rights reinforcement, as well as the pre-adoption existence of a pro-growth set of public policies can contribute to the encouragement of IFRSs adoption.IFRSs adoption• Legislative families

Essays on Measuring (Credit-)Risk in Banks, Financial Accounting and Auditing - Theory and Practice

Requirements of regulatory authorities significantly determine the environment of capital market-oriented companies, especially in the areas of risk management, accounting and auditing of financial statements. The regulations are usually elaborated by committees or standard-setting associations and regularly leave room for interpretation and implementation options. Moreover, they may even fail to achieve the intended goal of encouraging economic agents to act in a socially desirable manner. The models and methods introduced in this doctoral thesis refine risk measurement within the framework of capital requirements regulation, offer innovative implementation options for the expected loss approach of international financial reporting standards, and analyze the effectiveness of regulatory provisions concerning the audit of financial statements. This enriches the theoretical basis in these three areas of regulation and provides hands-on solutions for its implementation in practice

An empirical investigation of the determinants and impact of bank credit ratings

The role of credit rating agencies has come under severe scrutiny following the recent financial crisis, due to their claim, ab initio, of assigning true creditworthiness in the form of rating notches to financial institutions and their instruments. The over reliance of the market, and in particular of traders, investors and regulators, on external credit ratings contribute to the laxity and the herd behaviour of many of these market participants. The credit rating agencies have become a central theme of academic research and in particular, in the investigation of the ratings they assign and the effects of their rating actions on the market.This thesis investigates three empirical issues in the field of bank credit rating in an international setting. First, it models the financial and non-financial determinants of bank credit ratings. Second, it examines the impact of news announcements concerning bank credit rating changes, that is, upgrades and downgrades, on the performance of bank stock. Lastly, the thesis examines the trends in bank credit rating over time by focusing on rating migration within the historical pattern of both ratings and rating changes.The thesis reveals that the assignment of credit ratings to international banks is driven heavily by the CAMELS. The inclusion of non-financial variables, such as the too-big-to-fail, adds to the explanatory power of the rating determinant models. In addition, the thesis reveals that there is asymmetry in the reaction of the market to rating actions. It finds significant positive market reactions to subsamples of bank upgrades. Downgrades generally elicit significant negative market reactions. Finally, the results provide evidence of downward momentum in the rating migration of banks over time. In addition, it reveals the importance of duration in a rating notch on the likelihood of a bank migrating to another state. Generally, the longer a rated bank stays in a particular rating notch, the lower its probability of transiting to another rating notch

Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks.

Política de acceso abierto tomada de: https://www.aimspress.com/index/news/solo-detail/openaccesspolicyThe blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict “buy” or “sell” decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.This research was funded by the Universitat de Barcelona, under the grant UB-AE-AS017634

Real Option Valuation of a Portfolio of Oil Projects

Various methodologies exist for valuing companies and their projects. We address the problem of valuing a portfolio of projects within companies that have infrequent, large and volatile cash flows. Examples of this type of company exist in oil exploration and development and we will use this example to illustrate our analysis throughout the thesis. The theoretical interest in this problem lies in modeling the sources of risk in the projects and their different interactions within each project. Initially we look at the advantages of real options analysis and compare this approach with more traditional valuation methods, highlighting strengths and weaknesses ofeach approach in the light ofthe thesis problem. We give the background to the stages in an oil exploration and development project and identify the main common sources of risk, for example commodity prices. We discuss the appropriate representation for oil prices; in short, do oil prices behave more like equities or more like interest rates? The appropriate representation is used to model oil price as a source ofrisk. A real option valuation model based on market uncertainty (in the form of oil price risk) and geological uncertainty (reserve volume uncertainty) is presented and tested for two different oil projects. Finally, a methodology to measure the inter-relationship between oil price and other sources of risk such as interest rates is proposed using copula methods.Imperial Users onl

TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start

This paper proposes an interpretable two-stream transformer CORAL networks (TransCORALNet) for supply chain credit assessment under the segment industry and cold start problem. The model aims to provide accurate credit assessment prediction for new supply chain borrowers with limited historical data. Here, the two-stream domain adaptation architecture with correlation alignment (CORAL) loss is used as a core model and is equipped with transformer, which provides insights about the learned features and allow efficient parallelization during training. Thanks to the domain adaptation capability of the proposed model, the domain shift between the source and target domain is minimized. Therefore, the model exhibits good generalization where the source and target do not follow the same distribution, and a limited amount of target labeled instances exist. Furthermore, we employ Local Interpretable Model-agnostic Explanations (LIME) to provide more insight into the model prediction and identify the key features contributing to supply chain credit assessment decisions. The proposed model addresses four significant supply chain credit assessment challenges: domain shift, cold start, imbalanced-class and interpretability. Experimental results on a real-world data set demonstrate the superiority of TransCORALNet over a number of state-of-the-art baselines in terms of accuracy. The code is available on GitHub https://github.com/JieJieNiu/TransCORALN .Comment: 13 pages, 7 figure

Aligning capital with risk

The interaction of capital and risk is of primary interest in the corporate governance of banks as it links operational profitability and strategic risk management. Senior executives understand that their organization's monitoring system strongly affects the behaviour of managers and employees. Typical instruments used by senior executives to focus on strategy are balanced scorecards with objectives for performance and risk management, including an according payroll process. A top-down capital-at-risk concept gives the executive board the desired control of the operative behaviour of all risk takers. It guarantees uniform compensations for business risks taken in any division or business area. The standard theory of cost-of-capital assumes standardized assets. Return distributions are equally normalized to a one-year risk horizon. It must be noted that risk measurement and management for any individual risk factor has a bottom-up design. The typical risk horizon for trading positions is 10 days, 1 month for treasury positions, 1 year for operational risks and even longer for credit risks. My contribution to the discussion is as follows: in the classical theory, one determines capital requirements and risk measurement using a top-down approach, without specifying market and regulation standards. In my thesis I show how to close the gap between bottom-up risk modelling and top-down capital alignment. I dedicate a separate paper to each risk factor and its application in risk capital management

Will Sentiment Analysis Need Subculture? A New Data Augmentation Approach

The renowned proverb that "The pen is mightier than the sword" underscores the formidable influence wielded by text expressions in shaping sentiments. Indeed, well-crafted written can deeply resonate within cultures, conveying profound sentiments. Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human feelings by ardently pursuing the allure of novelty, a fact that cannot be disregarded in the sentiment analysis. This paper strives to enrich data through the lens of subculture, to address the insufficient training data faced by sentiment analysis. To this end, a new approach of subculture-based data augmentation (SCDA) is proposed, which engenders six enhanced texts for each training text by leveraging the creation of six diverse subculture expression generators. The extensive experiments attest to the effectiveness and potential of SCDA. The results also shed light on the phenomenon that disparate subculture expressions elicit varying degrees of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting the linear reversibility of certain subculture expressions. It is our fervent aspiration that this study serves as a catalyst in fostering heightened perceptiveness towards the tapestry of information, sentiment and culture, thereby enriching our collective understanding.Comment: JASIS

Application of Climatic Data in Hydrologic Models

Over the past few decades, global warming and climate change have impacted the hydrologic cycle. Many models have been developed to simulate hydrologic processes. Obtaining accurate climatic data on local/meso, and global scales is essential for the realistic simulation of hydrologic processes. However, the limited availability of climatic data often poses a challenge to hydrologic modeling efforts. Hydrologic science is currently undergoing a revolution in which the field is being transformed by the multitude of newly available data streams. Historically, hydrologic models that have been developed to answer basic questions about the rainfall–runoff relationship, surface water, and groundwater storage/fluxes, land–atmosphere interactions, have been optimized for previously data-limited conditions. With the advent of remote sensing technologies and increased computational resources, the environment for water cycle researchers has fundamentally changed to one where there is now a flood of spatially distributed and time-dependent data. The bias in the climatic data is propagated through models and can yield estimation errors. Therefore, the bias in climatic data should be removed before their use in hydrologic models. Climatic data have been a core component of the science of hydrology. Their intrinsic role in understanding and managing water resources and developing sound water policies dictates their vital importance. This book aims to present recent advances concerning climatic data and their applications in hydrologic models