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When trading in the call and put contracts of option chains, the portfolios of strikes must be selected. The trader must also decide whether to take long or short positions at the selected strikes. Dynamic strategies for making these decisions are discussed in this paper. On any day, the strategies estimate the drift and volatility parameters of the future probability distribution of the price of the underlying asset. From this distribution, the trader can further estimate the future expected profit and expected loss that may be experienced for any portfolio of strikes of the call and put contracts. Expected profit and expected loss are the reward and risk metrics of such portfolios. An optimal portfolio can then be selected by making the reward as high as possible under the risk tolerance set by the trader. Extensive back-testing applications to historical data of SPY option chains illustrate the effectiveness of these strategies, particularly when dealing with short-term expiry options and when acting as a seller of put and call options. [ABSTRACT FROM AUTHOR]

This paper showcases the effectiveness of the discrete generalized Burr-Hatke distribution in analyzing insurance claims data, specifically focusing on scenarios with over-dispersed and zero-inflated claims. Key contributions include presenting foundational statistical theories with mathematical proofs to enrich the paper's mathematical and statistical aspects. Through the application of this discrete distribution, the study conducted a thorough risk analysis across five diverse sets of insurance claims data, evaluating critical risk indicators at specified quantiles. These indicators provided detailed insights into potential losses across different risk levels, supporting effective risk management strategies. The research emphasizes the importance of selecting appropriate probability distributions when analyzing zero-inflated data, as commonly observed in insurance claims. The discrete distribution accommodated these unique data characteristics and facilitated a robust analysis of risk metrics, enhancing the accuracy of potential loss assessments and reducing associated uncertainties. Furthermore, the study highlights the practical relevance of the discrete distribution in addressing specific challenges inherent to insurance claims data. By leveraging this distribution, insurers and risk analysts can improve their risk modeling capabilities, leading to more informed decision-making and enhanced financial exposure management. [ABSTRACT FROM AUTHOR]

Severe earthquakes possess formidable destructive potential, leading to substantial damage, widespread disruptions, and tragic loss of life. In response, researchers and policymakers strive to develop innovative solutions to quantify the seismic resilience of structures. This study aims to evaluate the post‐earthquake functional recovery of two types of braced frames: Self‐Centering Piston‐Based Braced Frames (SC‐PBBFs) and Buckling Restrained Braced Frames (BRBFs). The SC‐PBBFs are designed to mitigate structural damage by incorporating Self‐Centering Piston‐Based Bracings (SC‐PBBs) equipped with Shape Memory Alloy (SMA) bars and Friction Springs (FS). A set of building prototypes, representing low‐, mid‐, and high‐rise archetypes in seismic regions, are subjected to comprehensive analyses under 44 far‐field ground motions. This thorough examination incorporates seismic hazards, structural demands, and component damages to quantify the earthquake‐induced losses and repair timelines. The study findings indicate that BRBFs suffer more significant collapse losses than SC‐PBBFs. However, BRBF systems incur lower repair costs, mainly due to limited damage to acceleration‐sensitive non‐structural components. Additionally, the FEMA P‐58/ATC‐138 framework is employed to estimate re‐occupancy, functional recovery, and full repair time, identifying systems and components that exert a significant impact on functional recovery. [ABSTRACT FROM AUTHOR]

"If there were no losses; there would be no premiums," Insurance proverb. This paper analyzes the history of natural catastrophe Insurance-Linked Securities (ILS), or Cat Bonds (CB), from 2001 to 2020. Preliminary analyses summarize the annual character of issuance during that period, providing context for the principal focus of the paper, which is losses. A detailed loss record is provided, including why and when losses occurred. This record, when set against the historic issuance, allows us to address several important questions, unaddressed in the literature but constantly posed by practitioners. Does the cumulative loss over 20 years equal what catastrophe models led to us expect? Were the relative sizes of actual losses reflective of expected losses? Most importantly, does the loss record support the idea that natural catastrophe models are accurate and useful? This paper is the first to specifically address these fundamental forensic questions against the loss record. It thereby makes an important contribution to the growing literature about ILS markets. [ABSTRACT FROM AUTHOR]

When trading in the call and put contracts of option chains, the portfolios of strikes must be selected. The trader must also decide whether to take long or short positions at the selected strikes. Dynamic strategies for making these decisions are discussed in this paper. On any day, the strategies estimate the drift and volatility parameters of the future probability distribution of the price of the underlying asset. From this distribution, the trader can further estimate the future expected profit and expected loss that may be experienced for any portfolio of strikes of the call and put contracts. Expected profit and expected loss are the reward and risk metrics of such portfolios. An optimal portfolio can then be selected by making the reward as high as possible under the risk tolerance set by the trader. Extensive back-testing applications to historical data of SPY option chains illustrate the effectiveness of these strategies, particularly when dealing with short-term expiry options and when acting as a seller of put and call options.

A time-domain wind-induced damage evaluation for the risk- and resilience-targeted design of building structures requires nonlinear dynamic analysis (NDA) solvers to handle long duration input loads. Moreover, post-construction damage identification based on real-time vibration monitoring depends on accurate damage simulation from the design stage and available artificial intelligence (AI)-based technology. In this paper, an integrated time-domain approach for the performance-based analysis and design for wind is presented. The nonlinear dynamic analysis (NDA) is performed with the Force Analogy Method (FAM) and the state–space formulation to solve the nonlinear equations of motion. Then, the Incremental Dynamic Analysis (IDA) is used to perform probabilistic fragility analyses for a typical reinforced concrete (RC) frame structure. The analysis also provides estimates of expected losses, gives the building owners business support data to compare, e.g., the final annual building income (FABI), the cost of repair and the function recovery duration for various wind events in an integrated resilience-based building design framework. This provides the basis for mapping structural damage on large urban scales and for straightforward quantification of wind-induced damage from available response acceleration time-series acquired from real-time vibration monitoring. [ABSTRACT FROM AUTHOR]

Standard models of portfolio investment rely on various statistical measures of dispersion. Such measures favor returns smoothed over all states of the world and penalize abnormally low as well as abnormally high returns. A model of portfolio investment based on the tradeoff between expected return and expected loss considers only abnormally low returns as undesirable. Such a model has a comparative advantage over other existing models in that a first-order stochastically dominant portfolio always has a higher expected return and a lower expected loss. Expected return—expected loss model of portfolio investment can rationalize the equity premium puzzle. Two random variables are not comoving if there is at least one state of the world in which one random variable yields a positive return and the other—a negative return. Such random variables provide hedging benefits from diversification in portfolio investment according to the expected return—expected loss model. A special case of this model, when an investor linearly trade-offs expected returns and expected losses, is also a special case of the prospect theory when a decision-maker has a piece-wise linear value function without any probability weighting. [ABSTRACT FROM AUTHOR]

Roll rates and net flow rates can be seen as the evolution of ageing of accounts receivable and Markov chains. They are accepted methodologies to model the behavior of non-performing consumer loans by buckets and to predict losses, but we find that quite often they are wrongly used as interchangeable concepts, although roll rates track individual accounts across buckets in consecutive months and net flow rates just compare consecutive buckets in consecutive months. We determine their matrices of transition probabilities and analyze them in both stationary and steady-state conditions. Net flow rates have many advantages over roll rates, but a quite important finding for financial institutions and supervisors is that historical flow rates are not conservative for forecasting: when the level of new delinquencies soars, contemporary flow rates will tend to be lower than they would be in steady-state conditions, creating a feeling of false confidence and leading to the underestimation of future losses. [ABSTRACT FROM AUTHOR]

The concept of Risk Appetite gained new significance in the aftermath of the Global Financial Crisis (GFC). In its guidance on Risk Appetite in 2013, the Financial Stability Board (FSB) elucidated the features of an ideal Risk Appetite Framework (RAF), statements, limits etc., but stopped short of providing details for operationalizing Risk Appetite (RA), particularly in terms of limits. This paper attempts not only to define RA more precisely in quantitative terms, but also suggests a mechanism to operationalize the RA based on certain quantitative anchors. This addresses the crucial question of allocating the RA along business lines and provides a mechanism to track the risk profile of the portfolio against the limits. The paper uses a hypothetical corporate portfolio to demonstrate a practical translation of credit risk appetite into operational limits. Since the appropriateness of RA is often tested during macroeconomic downturns, this paper links the appetite setting process to stress testing. [ABSTRACT FROM AUTHOR]

SYNOPSIS: After the financial crisis of the late 2000s, concern about delayed credit-loss recognition under the incurred-loss method prompted the FASB and the IASB to develop expected-loss methods. We review the development of these methods, including through comment-letter analysis. Initially, the FASB recommended immediate full recognition of expected losses, including at day one, and the IASB recommended spreading the recognition of initially expected losses across time. After unsuccessful attempts to converge based on proposals that partly reflected initial recommendations of each board, the boards eventually adopted different methods. We report that U.S. respondents largely opposed the FASB's final method, which required day-one recognition of all expected losses, and that non-U.S. respondents largely supported the IASB's final method, which required day-one recognition of 12-month expected losses. Day-one loss was controversial and impeded convergence. Our comment-letter analysis suggests that a day-one-loss-free more forward-looking incurred-loss method might provide a route to a more converged solution. JEL Classifications: G21; M41. [ABSTRACT FROM AUTHOR]

This study proposed a method for constructing rating tools using logistic regression and linear discriminant analysis to determine the risk profile of SME portfolios. The objective, firstly, is to evaluate the impact of the crisis due to the Covid-19 by readjusting the profile of each company by using the expert opinion and, secondly, to evaluate the efficiency of the measures taken by the Moroccan state to support the companies during the period of the pandemic. The analysis in this paper showed that the performance of the logistic regression and linear discriminant analysis models is almost equivalent based on the ROC curve. However, it was revealed that the logistic regression model minimizes the risk cost represented in this study by the expected loss. For the support measures adopted by the Moroccan government, the study showed that the failure rate (critical situation) of the firms benefiting from the support is largely lower than that of the non-beneficiaries.

El artículo desarrolla el modelo de estimación de pérdida esperada como soporte al Sistema de Administración del Riesgo de Crédito para una entidad de economía solidaria. Describe la construcción de un modelo de regresión logística para la estimación de la probabilidad de incumplimiento de sus asociados y valora el desempeño del modelo desde el poder de discriminación entre cumplidos e incumplidos mediante el método Hold Out repetido. Definido el modelo y estimada la probabilidad de incumplimiento de los asociados, se determina la tasa de recuperación de acuerdo con el tipo de garantía que presente cada crédito, permitiendo así estimar la pérdida esperada y las provisiones de cartera necesarias para la entidad. The article develops an estimation model for expected loss as support to the Credit Risk Management System for a corporation of social economy. it describes the setting of a logistic binary regression model to estimate the probability the non-compliance of its affiliates and, it evaluates the model’s capacity to discriminate between compliance and non-compliance using the Hold Out recurrent method. Once the model is fixed and the probability is estimated, the recovery rate is determinate according to the guarantee offer by each credit. so, is possible the estimation of expected loss as well as the portfolio provisions required by each fund.

A time-domain wind-induced damage evaluation for the risk- and resilience-targeted design of building structures requires nonlinear dynamic analysis (NDA) solvers to handle long duration input loads. Moreover, post-construction damage identification based on real-time vibration monitoring depends on accurate damage simulation from the design stage and available artificial intelligence (AI)-based technology. In this paper, an integrated time-domain approach for the performance-based analysis and design for wind is presented. The nonlinear dynamic analysis (NDA) is performed with the Force Analogy Method (FAM) and the state–space formulation to solve the nonlinear equations of motion. Then, the Incremental Dynamic Analysis (IDA) is used to perform probabilistic fragility analyses for a typical reinforced concrete (RC) frame structure. The analysis also provides estimates of expected losses, gives the building owners business support data to compare, e.g., the final annual building income (FABI), the cost of repair and the function recovery duration for various wind events in an integrated resilience-based building design framework. This provides the basis for mapping structural damage on large urban scales and for straightforward quantification of wind-induced damage from available response acceleration time-series acquired from real-time vibration monitoring.

Copyright of Revista Fatec Zona Sul (REFAS) is the property of Revista Fatec Zona Sul (REFAS) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Trading in binary options is discussed using an approach based on expected profit (EP) and expected loss (EL) as metrics of reward and risk of trades. These metrics are reviewed and the role of the EL/EP ratio as an indicator of quality of trades, taking risk tolerance into account, is discussed. Formulas are derived for the EP and EL of call and put binaries assuming that the price of the underlying asset follows a geometric Brownian motion. The results are illustrated with practical data from the Nadex trading platform. The Black–Scholes notion of implied volatility is extended to wider notions of implied drift and volatility of the price process of the underlying asset. Illustrations show how these notions can be used to identify attractive binary trades, taking anticipated price movement into account. The problem of selecting portfolios of call and put binary options which maximize portfolio EP while constraining the portfolio EL to satisfy risk tolerance and diversification requirements, is formulated and solved by linear programming. This is also illustrated with the Nadex data under various scenarios.

Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results. [ABSTRACT FROM AUTHOR]

Coastal cities in the Southeast and Gulf Coast of the United States are at an increased risk of tropical cyclones (hurricanes) due to the combined effects of urbanization, rapid economic development, and climate change. Current building codes and standards focus on minimum performance criteria for individual buildings exposed to severe hazard events to ensure occupant safety. However, they do not consider the resilience of buildings and building portfolios, which are key factors in determining whether a community can respond to and recover from a severe natural hazard event. Light-frame wood residential buildings dominate the residential market in the US, represent a significant percentage of the investment in the built environment, and are especially vulnerable to hurricane winds and storm surge in coastal areas. Our study of the impact of various hurricane and climate change scenarios on the performance of coastal residential communities reveals that decision-making at the community level is needed to develop rational engineering and urban planning policies, to mitigate the impact of hurricane wind and storm surge, and to adapt to climate change. The results suggest that fundamental changes in the current building regulatory process may be necessary.

In many areas of science, including business disciplines, statistical decisions are often made almost exclusively at a conventional level of significance. Serious concerns have been raised that this contributes to a range of poor practices such as p‐hacking and data‐mining that undermine research credibility. In this paper, we present a decision‐theoretic approach to choosing the optimal level of significance, with a consideration of the key factors of hypothesis testing, including sample size, prior belief, and losses from Type I and II errors. We present the method in the context of testing for linear restrictions in the linear regression model. From the empirical applications in accounting, economics, and finance, we find that the decisions made at the optimal significance levels are more sensible and unambiguous than those at a conventional level, providing inferential outcomes consistent with estimation results, descriptive analysis, and economic reasoning. Computational resources are provided with two R packages. [ABSTRACT FROM AUTHOR]

In 2016 Allan D. Grody and Peter J. Hughes proposed a method and system termed 'Risk Accounting', an integrated financial and risk accounting framework. Risk Accounting incorporates a novel operational risk exposure quantification technique based on the Risk Unit (RU), a new common additive metric designed to express all forms of operational risk in banks. In this paper, we report on initial tests of the inherent predictiveness of the RU. The test focused on the period leading up to the global financial crisis of 2007-8 and involved the restatement into RUs of publicly available accounting data in the United States relative to a subset of large US banks. We contend that the RU's inherent predictiveness could be concluded if it is demonstrated that an accelerated increase in trended operational risk RUs and subsequent material unexpected losses are positively correlated. We further describe how a monetary value can be stochastically derived and assigned to the RU over time. The inclusion of valued RUs in accounting systems will potentially enable the systematic adjustment of financial performance and condition relative to accepted nonfinancial risks to complement the accounting treatment already applied to financial (credit and market) risks. The resulting harmonisation of the accounting treatment applied to both financial and nonfinancial risks based on stochastic modelling will enable risk-adjusted economic profit to be adopted as the primary business performance metric and economic capital as the primary method of determining both operating and regulatory capital requirements. The real-time or near-real-time production of portfolio views of operational risk exposures based on the RU adds analytical rigour to their management and causes risk mitigation to become both a risk reduction and a profit optimisation initiative. The more effective management, oversight and governance of exposures to operational risks is the anticipated outcome. [ABSTRACT FROM AUTHOR]

• A systematic approach has been developed to design multilayer standby mechanisms. • A superstructure is proposed for standby mechanisms under varying loads. • A generalized event tree is presented to enumerate all loss-causing scenarios. • A Matlab code has been developed for optimization via genetic algorithm. • Optimal standby configurations and maintenance policies can be obtained. In modern chemical plants, there is a class of continuous processes operated under varying loads. In these processes, the standby mechanisms are needed to ensure that the downstream demand is always satisfied during the operations. Although a few related studies have been reported in literature, a comprehensive analysis of multilayer standby mechanisms in processes under varying loads has not been carried out. In this paper, a generalized mathematical model is developed to automatically generate the optimal standby mechanisms for any given processes by minimizing the total expected lifecycle expenditure. A Matlab code has been developed to perform these optimization tasks via genetic algorithm. The feasibility and effectiveness of the proposed model are demonstrated with the case study concerning a fan system for providing instrument air in a typical chemical plant. From the optimization results, one can determine the optimum configurations of the standby mechanisms, which include: (1) the number of protection layers, (2) the number of both online and spare sensors in the measurement channels, (3) the corresponding voting-gate logic in each channel, (4) the inspection intervals of switch and (5) the number of spares for switch. [ABSTRACT FROM AUTHOR]

This article is a primer for a decision-theoretic approach to hypothesis testing for students and teachers of basic statistics. Using three examples at an introductory level, this article demonstrates how decision-theoretic hypothesis testing can be taught to the students of basic statistics. It also demonstrates that students and researchers can make more sensible and unambiguous decisions under uncertainty by employing this particular approach. The examples are illustrated using R and its package "OptSig." [ABSTRACT FROM AUTHOR]

In this paper, we search for optimal portfolio strategies in the presence of various risk measure that are common in financial applications. Particularly, we deal with the static optimization problem with respect to Value at Risk, Expected Loss and Expected Utility Loss measures. To do so, under the Black-Scholes model for the financial market, Martingale method is applied to give closed-form solutions for the optimal terminal wealths; then via representation problem the optimal portfolio strategies are achieved. We compare the performances of these measures on the terminal wealths and optimal strategies of such constrained investors. Finally, we present some numerical results to compare them in several respects to give light to further studies. [ABSTRACT FROM AUTHOR]

The wave of fraud in Nigerian banks has continued to be on the rise. There were 20,768 reported cases of fraud and forgery (attempted and successful) valued at N19.77 billion in 2018, compared with 16,762 cases, involving N5.52 billion and US$ 0.12 million in the period of 2017. The amount lost to fraud and forgeries in the first six months of 2018 total N12.06 billion, compared with the N0.78 billion and US$0.03 million suffered in the first half of the year 2017. This study therefore, sought to investigate the effect of fraud on the financial performance of banks in Nigeria. The secondary data on Total Amount of Bank Fraud (TABF), Percent of Expected Loss to Amount of Bank Fraud (BFRD), Foreign Exchange Malpractices (FEM), Return on Assets (ROA) and Return on Equity (ROE) spanning the period 2008-2018 utilized in the study were extracted from the CBN and the NDIC Statistical Bulletins. To determine the effect of fraud on financial performance, the multivariate regression model was employed. However, the analyses were done via windows software - STATA 13.0 version. The findings showed that TABF and FEM significantly affects the reported figures for RETOA and RETOE. However, BFRD does not have effect on the RETOA and RETOE of banks in Nigeria. Thus, the study concludes that fraud significantly and negatively influences financial performance of banks in Nigeria. Based on the findings, the study recommends among others, that in order to improve on the Return on Asset (ROA) and Return on Equity (ROE), Nigerian banks should strengthen and institute effective and efficient internal control in recording, processing and authorization of business financial transactions that would mitigate the incidence of fraud. [ABSTRACT FROM AUTHOR]

Copyright of Comunicaciones en Estadística is the property of Universidad Santo Tomas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results.

Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks that may occur during product design and manufacturing phases. However, use of the risk priority number (RPN) in traditional FMEA results in difficulties being encountered with regard to quantification of the degree of risk involved. This study proposes the use of a probabilistic time-dependent FMEA (TD-FMEA) approach to overcome limitations encountered during implementation of traditional FMEA approaches. To this end, the proposed method defines the risk priority metric (RPM) as a priority decision value. RPM corresponds to the product of the expected loss and occurrence rate of the failure-cause. By assuming exponential and case functions for each occurrence and detection time instant, the expected loss corresponding to each failure-cause can be evaluated. Utility of the proposed approach has been described in the light of results obtained via its implementation during an automotive-manufacturing case study performed for the purpose of illustration. [ABSTRACT FROM AUTHOR]

Failure mode and effect analysis (FMEA) is a structured technique for identifying risks that may occur during a given stage of a system's life cycle. However, the use of the risk priority number (RPN) in traditional FMEA results in difficulties with regard to quantification of the degree of risk in the hierarchical failure structure. This study proposes the use of a hierarchical time-dependent FMEA approach to overcome the limitations encountered during the implementation of traditional FMEA approaches. In place of the RPN, a probabilistic loss model is developed under a hierarchical structure considering the elapsed time from the failure-cause (FC) to the system failure. By assuming exponential and case functions for each occurrence and detection time instant, the expected loss corresponding to each FC can be evaluated. As a result of the practical application of the time-dependent probabilistic model through the numerical example, we could reasonably evaluate the risk from the cause of failure in the hierarchical structure in terms of economic loss. [ABSTRACT FROM AUTHOR]

After the financial and banking crisis of the late 2000s, the FASB and the IASB aimed to develop methods of accounting for credit losses that would give more timely recognition of those losses. The IASB (in 2009) and the FASB (in 2010) each initially issued its own exposure draft proposing separate approaches to achieving this. They then attempted to agree a converged expected-loss-based method for accounting for credit losses, but failed to achieve convergence. They then each issued an accounting standard that included its own expected-loss method, with effective dates of 2018 for the IASB and 2020/21 for the FASB. This paper provides an overview of the development of proposals and standards in relation to accounting for credit losses issued by the standard setters from 2009 to 2016. It then offers reflections on difficulties that the standard setters faced in this area and on problems that might arise after the new standards become effective. It raises the question of whether a route based on 'expected loss', which in relation to credit losses is a concept that originally became prominent for the purpose of setting banks' capital requirements, was helpful to the process of improving the accounting for credit losses. [ABSTRACT FROM AUTHOR]

• Credit risk migration rates matrices with entries, migrations of stayers and exits are simulated. • A mathematical procedure to extrapolate future dynamics conditional to a macroeconomic scenario is developed; • IFRS9 segmentation into buckets and prospective estimates of Expected Loss are introduced. In 2014 the International Accounting Standards Board (IASB) promulgated the current International Financial Reporting Standards 9 – Financial Instruments (IFRS9) that draw new lines for an ex-ante , reliable, unified and well-balanced credit risk assessment. Among others, two principles are of interest to this paper: that of segmented and prospective estimation of expected credit losses. Within the frame of a micro-simulation approach, this paper focuses on these issues while considering the evolution of a bank portfolio. The paper presents an algorithmic procedure developed on a realistic dynamic credit risk migration rates modelling of a portfolio as an open system with entries and exits that is consistent with the segmented and prospective IFRS9 principles. Although operating at the aggregate level of the migration matrix, combining accounting principles inspired to those of the IFRS9-baseline with the open systems modelling, the main conclusion is that it allows for a more reliable provision and ex-ante and forward-looking estimation of expected losses. [ABSTRACT FROM AUTHOR]

Abstract Bayesian hypothesis testing procedures are constructed by means of test statistics which are functions of the posterior distribution. Usually, the whole sample vector is selected to form the sufficient empirical part of the posterior distribution. But, in certain problems, one may prefer to use well-established one-dimensional sufficient statistics in place of the sample vector. This paper introduces a Bayesian Monte Carlo procedure specially designed for such cases. It is shown that the performance of this new approach is arbitrarily close to the exact Bayesian test. In addition, for arbitrary desired precisions, we develop a theoretical rule of thumb for choosing the minimum number m 0 of Monte Carlo simulations. Surprisingly, m 0 does not depend on the shape of loss/cost functions when those are used to compound the test statistic. The method is illustrated for testing mean vectors in high-dimension and for detecting spatial clusters of diseases in aggregated maps. [ABSTRACT FROM AUTHOR]

Gardening products, like apple, are exposed to a variety of risks caused by unfavorable weather conditions. This kind of risk is unavoidable, but manageable. Agricultural insurance is an effective scheme in weather risk management. Nevertheless, current insurance schemes have challenges, such as high transaction costs, and problems caused by asymmetric information, i.e. adverse selection and moral hazard. Therefore, this study aimed to present an appropriate insurance scheme for apple production in Damavand, the so-called “weather-based index insurance”. In this regard, the information on apple yield and weather variables was collected between 1987-2016, from Iranian Agriculture Jihad Organization and the local meteorological station. The dependency structure between apple yield and weather variables was investigated by C-Vine Copula as a joint distribution to compute the expected loss. Then, according to the expected loss, weatherbased index insurance premium was measured. The premium amount was equal to Thousand Rials 32,546.11 in the crop year 2016-17, which is different from the current insurance premium. This difference is because of the distinct nature of the two insurance schemes and the imperative and official mode of current insurance scheme. [ABSTRACT FROM AUTHOR]