Loss Of Expectation Research Articles

Is there a dark side to financial inclusion? Understanding the relationship between financial inclusion and market risk

This study delves into the connection between market risk and financial inclusion. Market risk is gauged through primary risk metrics commonly used in industry and academia, such as Standard Deviation (SD), Value at Risk (VaR), Expected Shortfall (ES), and Expected Loss (EL). In exploring financial inclusion, we consider twelve specific indicators that span three crucial dimensions: penetration, availability, and usage. Our analysis covers data from 49 countries spanning the period from 2004 to 2021, encompassing G20 nations and markets hosting major global stock exchanges. Our findings reveal a positive correlation between various indicators of financial inclusion and certain risk characteristics. Notably, distinctions emerge between underdeveloped and developed countries. In the case of developed nations, for instance, the quantity of active mobile money accounts and mobile money agent outlets exhibits a robust correlation with market risk. Through quantile correlation analysis, we discern that the relationship between risk and financial inclusion undergoes shifts based on the level of inclusion. Therefore, despite the documented advantages of financial inclusion, our study sheds light on its potential adverse implications.

Simulating Credit Loss Distributions: Empirical Versus the Vasicek Model

Because credit losses can be substantial, managing credit risk is a focus area of risk measurement and management. It is important for financial institutions to select credit risk models that accurately forecast losses. The Basel Committee on Banking Supervision (BCBS) chose the closed-form single risk factor Vasicek model for regulatory capital calculations. In this article, its forecast accuracy is compared with empirical loss distributions using simulated probabilities of default and losses given default. The effect of altering probabilities of default on asset correlations was analysed and how this affects credit portfolio loss distributions. The robustness of the Vasicek model against five different portfolios with unique compositions was explored: results highlight two key findings. Firstly, the Vasicek model is a good approximation of credit losses for a portfolio that does not contain dominating loans (it is, after all, based on the assumption of large-scale homogeneity). Secondly, the Vasicek model is a good approximation for expected loss (ELs) but lacks accuracy when determining extreme unexpected losses (ULs). Finally, credit capital requirements as a function of two variables are presented which reveals novel ways of viewing these values.

Fragility functions for fiber-reinforced polymers strengthened reinforced concrete beam-column joints

Post-earthquake field inspections have outlined the poor seismic performance of non-conforming reinforced concrete (RC) beam-column joints (BCJs) of existing RC buildings. Experimental and analytical studies have demonstrated that the application of externally bonded fiber-reinforced polymers (FRPs) is an efficient and cost-effective strengthening technique to improve the seismic performance of deficient BCJs. However, seismic losses for FRP-strengthened BCJs at different damage states (DSs) are currently not quantified. A potential seismic damage assessment tool is critical to obtain reliable estimations of the effects of FRP strengthening on reducing the expected damage and losses in existing buildings. Therefore, experimental-based fragility functions for FRP-strengthened corner and exterior BCJs at different limit states are proposed in this study. Experimental tests on 70 corner and 28 exterior FRP-strengthened BCJs are first collected. Then, DSs with increasing severity (i.e., light, moderate, and heavy) are defined according to widely recognized studies. These DSs are quantified on the basis of inter-story drift ratios (IDRs). After retrofitting, IDR-based fragility functions are generated for strengthened BCJs classified in terms of failure mode and achieved ductility level. The proposed functions are also compared with fragility functions available for non-conforming and conforming joints obtained from the literature. Finally, an application of the proposed fragility functions within a performance-based earthquake engineering (PBEE) framework for quantifying expected losses (ELs) and the benefits of FRP strengthening of BCJs is showed.

Credit risk in infrastructure PPP projects under the real options approach

The purpose of the paper is to provide a method to estimate the credit risk in infrastructure public–private partnership (PPP) projects by using a structural model, the Real Options approach, and the Monte Carlo simulation technique. To do that, previous models are extended under a structural framework for credit risk where the embedded options in the credit agreement such as the option to renegotiate and the option to exit are introduced as well as the uncertainty of the cash flows. In that sense, all the components of expected loss (EL) such as the probability of default, the exposure, and the recovery rate for lenders are modelled and estimated in a PPP toll road project by considering the embedded options as well as the default events. Consequently, it is found that the embedded options improve the recovery rate for lenders and their EL. Additionally, practical insights about the effects of the embedded options in the credit agreement and the probability of default are provided.

Trading Binary Options Using Expected Profit and Loss Metrics

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.

Risk measure index tracking model

We take a standard index tracking (IT) optimization model and include seven risk measures as constraints, as we seek to estimate portfolios that not only aim at tracking the market benchmark but also have their risk constrained by the market risk. We consider seven risk measures: Expected Loss (EL), Semi-deviation (SD−), Expected Loss Deviation (ELD), Expected Shortfall (ESα), Shortfall Deviation (SDα), Shortfall Deviation Risk (SDRα), and Maximum Loss (ML), so that we have measures that assess the loss, deviation, and both risk concepts simultaneously. We use the S&P 500 and a sample of 501 stocks with daily returns from 2006 to 2019. Our findings show that some risk measures result in portfolios with tighter risk (mainly ESα and SDRα) than others (mainly EL, ELD, SDα and SD−), and that such risk reduction is followed systematically by larger tracking error. Then, we propose two performance metrics to consolidate both tracking error and risk in one single result. This analysis show that SD−, SDα, and ESα stand out as the most prominent risk measures to be used as constraints in the optimization, as they combined low tracking error and risk.

Research on the financing income of supply chains based on an E-commerce platform

Rapid economic development has brought about the expansion of the supply chain. In the context of the demand for finance and emerging financial technology tools, supply chain finance on e-commerce platforms is developing rapidly. It not only strengthens the ability to serve the real economy, but also brings market risks caused by excessive supply chains. In the Internet era, IoT technology promotes the exchange of information, while it also has certain risk characteristics. This research implements the peaks over threshold (POT) model to investigate the value at risk (VaR) and expected loss (ES) in the supply chain of e-commerce platforms under the risk of unexpected changes in the market. The study finds that the supply chain of e-commerce platforms based on Internet of Things (IoT) technology suffers less risk in losses. The application and expansion of this technology will effectively lower the market risk of supply chain finance and better serve economic development.

CreditMetrics Methodology and Credit Value at Risk (Credit VaR)

Described by Hull (2011, 2012) as ‘a procedure for calculating credit value at risk’, CreditMetrics methodology (RiskMetrics Group 2007) is used for assessing portfolio risk due to changes in bond or debt value caused by credit quality changes including credit migration (upgrades and downgrades), as well as, default. It measures the uncertainty in forward value of the bond portfolio at the risk horizon caused by such credit events. Changes in debt value could be small in case of credit quality ratings change; however, they could be enormous, 50% to 90%, in case of default. Characterized by a long downside tail, credit-returns are highly-skewed and fat-tailed and thus far from the Gaussian normal distribution assumptions about market risk in VaR (Fig. 1). In the portfolio context, based upon correlation of credit quality moves across obligors, CreditMetrics assesses both value-at-risk (VaR), i.e., the volatility of value, as well as expected losses (EL). By distinguishing high quality well-diversified portfolios from low-quality concentrated portfolios, it offers better understanding of credit risk in terms of diversification benefits and concentration risk compared to mandated standard capital adequacy measures.

Two-stage consumer credit risk modelling using heterogeneous ensemble learning

Modelling consumer credit risk is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. To model the overall credit risk of a consumer loan in terms of expected loss (EL), three key credit risk parameters must be estimated: probability of default (PD), loss given default (LGD) and exposure at default (EAD). Research to date has tended to model these parameters separately. Moreover, a neglected area in the field of LGD/EAD modelling is the application of ensemble learning, which by benefitting from diverse base learners reduces the over-fitting problem and enables modelling diverse risk profiles of defaulted loans. To overcome these problems, this paper proposes a two-stage credit risk model that integrates (1) class-imbalanced ensemble learning for predicting PD (credit scoring), and (2) an EAD prediction using a regression ensemble. Furthermore, multi-objective evolutionary feature selection is used to minimize both the misclassification cost (root mean squared error) of the PD and EAD models and the number of attributes necessary for modelling. For this task, we propose a misclassification cost metric suitable for consumer loans with fixed exposure because it combines opportunity cost and LGD. We show that the proposed credit risk model is not only more effective than single-stage credit risk models but also outperforms state-of-the-art methods used to model credit risk in terms of prediction and economic performance.

Construction of Supply Chain Financial Risk Management Mode Based on Internet of Things

The objective of this paper is to analyze the problems of information asymmetry, mode lag, and high operational risk between banks and enterprises in the practice of supply chain finance business. In this paper, first, the concept and function of supply chain finance were elaborated, and the theoretical basis of supply chain financial risk control was studied. The implement of principal agent, information asymmetry theory, and self-compensated trade finance theory on realizing the risk avoidance in the actual development process was analyzed. Then, the business processes of the three basic financing models of supply chain finance and the shortcomings in the operation of each model were proposed. On this basis, risk sources and influencing factors of supply chain finance were analyzed, and operational risks were emphasized. Finally, a new financing mode of inventory pledge was designed based on the special function of Internet of Things (IoT) technology and the business process of inventory financing mode. The results showed that there was a big gap in the value of risk loss caused by various types of loss events in the operation risk of supply chain finance inventory pledge financing mode; among them, the biggest losses were from external fraud. By comparing the expected loss (ES) value of inventory pledge financing mode based on the IoT with the operation risk of traditional mode, it could be found that the supply chain finance mode based on the IoT technology effectively reduced the operation risk. Therefore, the system designed in this paper has certain reference and practical significance for financial risk management.

Evaluation of Two Approaches to Defining Extinction Risk under the U.S. Endangered Species Act.

The predominant definition of extinction risk in conservation biology involves evaluating the cumulative distribution function (CDF) of extinction time at a particular point (the "time horizon"). Using the principles of decision theory, this article develops an alternative definition of extinction risk as the expected loss (EL) to society resulting from eventual extinction of a species. Distinct roles are identified for time preference and risk aversion. Ranges of tentative values for the parameters of the two approaches are proposed, and the performances of the two approaches are compared and contrasted for a small set of real-world species with published extinction time distributions and a large set of hypothetical extinction time distributions. Potential issues with each approach are evaluated, and the EL approach is recommended as the better of the two. The CDF approach suffers from the fact that extinctions that occur at any time before the specified time horizon are weighted equally, while extinctions that occur beyond the specified time horizon receive no weight at all. It also suffers from the fact that the time horizon does not correspond to any natural phenomenon, and so is impossible to specify nonarbitrarily; yet the results can depend critically on the specified value. In contrast, the EL approach has the advantage of weighting extinction time continuously, with no artificial time horizon, and the parameters of the approach (the rates of time preference and risk aversion) do correspond to natural phenomena, and so can be specified nonarbitrarily.

On the Procyclicality of Expected Loss Provisioning

The new accounting standards of IFRS 9 and US GAAP adopt the Expected Loss (EL) approach for loan loss recognition. We investigate the effect of the EL approach on bank loan supply and stability. When a bank is unable to anticipate a downturn in the business cycle, it ends up recognizing the bulk of expected losses after the arrival of a contraction. This aggravates lending procyclicality and can potentially worsen bank stability. We develop a dynamic model of a bank to quantitatively assess these effects and show that they are economically significant.

Forecasting loss given default of bank loans with multi-stage model

Probability of default (PD) and loss given default (LGD) are key risk parameters in credit risk management. The majority of LGD research is based on the corporate bond market and few studies focus on the LGD of bank loans even in Japan because of the lack of available public data on bank loan losses. Consequently, knowledge concerning Japanese bank loan LGD is scarce. This study uses Japanese bank loan data to analyze the influencing factors of LGD and to develop a (multi-stage) model to predict LGD and expected loss (EL). We found that collateral, guarantees, and loan size impact LGD. Further, we confirmed that our multi-stage LGD model has superior predictive accuracy than the corresponding OLS model, Tobit model and Inflated beta regression model.

Estimating Unbiassed Expected Loss, with Application to Consumer Credit

The credit risk measure, Expected Loss (EL) is defined as the product of the three risk parameters: probability of default (PD), loss given default (LGD) and exposure at default (EAD). EL is central to risk management, profit estimation, calculating regulatory capital requirements and the standard accounting rules for credit (IFRS 9). Although correlations between the three risk parameters is evident, there is limited published work exploring these correlations and their impact on estimating EL accurately and without bias. Often EL is calculated simply assuming independence. In this study, EL is derived from first principles, without assuming independence between the three risk parameters. The main results are, firstly, that correlation between PD and LGD has no consequence on the calculation of EL, if LGD is treated as conditional on default. However, correlation between LGD and EAD does have an impact, requiring an adjustment to enable an accurate and unbiassed estimate. Additionally, there is no selection bias resulting from using LGD and EAD models built conditional on default, when applied across the total credit population. These results are demonstrated through a simulation study and by application to a real credit card data set.

A comparison of risk aggregation estimates using copulas and Fleishman distributions

ABSTRACTDetermining banks’ expected losses (EL) is straightforward because they are calculated using a linear combination of credit risk-related measures. Non-linear metrics, like economic capital (EC), pose considerable implementation challenges including computation complexity and a lack of adequate risk aggregation and attribution techniques when multiple portfolios and/or product segmentations are involved. Copulas have been used to overcome these problems, but the Fleishman procedure, which uses a polynomial transformation to generate non-normal data, may provide a more tractable alternative. In this article, EC simulation estimates using the extended (multivariate) Fleishman method and the Gumbel copula are compared. The Fleishman approach is found to be easier to implement than the Gumbel approach and provides comparable results when the correlation and concordance between losses are low. The Fleishman method preserves the first four moments and two measures of dependence (Pearson’s and Kendal’s ); the copula approach preserves only the first two moments of the empirical loss distributions.

Loan Loss Reserves (LLR), Expected Loss (EL), and Value at Risks (VaR)

Loan Loss Reserves (LLR), Expected Loss (EL), and Value at Risks (VaR)

Expected loss and Impact of Risk: backtesting parameter-based expected loss in a Basel II framework

The dependency structure of credit risk parameters is a key driver for capital consumption and receives regulatory and scientific attention. The impact of parameter imperfections on the quality of expected loss (EL) in the sense of a fair, unbiased estimate of risk expenses, however, is barely covered. So far there are no established backtesting procedures for EL that quantify its impact with regards to pricing or risk-adjusted profitability measures. In this paper, a practically oriented, top-down approach to assessing the quality of EL by backtesting with a properly defined risk measure is introduced. In a first step, the concept of risk expenses ("Cost of Risk") has to be extended beyond the classical provisioning view, toward a more adequate capital consumption approach ("Impact of Risk"). On this basis, the difference between parameter-based EL and actually reported Impact of Risk is decomposed into its key components. The proposed method will deepen the understanding of the practical properties of EL, reconcile the EL with a clearly defined and observable risk measure and provide a link between upcoming IFRS 9 accounting standards for loan loss provisioning and the regulatory capital requirements under the internal ratings-based approach (IRBA). The method is robust irrespective of whether parameters are simple, expert-based values or highly predictive and perfectly calibrated IRBA-compliant methods, as long as the parameters and default identification procedures are stable.

FVA and A New Capital Model - Slides

These slides describe a capital allocation model based on Expected Loss (EL) rather than the market standard Probability of Default (PD). Under the model, a derivative can be funded with a mixture of debt and equity for the CVA-DVA adjusted price with no need for a Funding Value Adjustment (FVA).The paper summarized in these slides can be found here: http://ssrn.com/abstract=2268062

Funding Cost and a New Capital Model

In asset and derivative pricing, funding costs and capital costs are usually considered separately. A derivative will be funded at a given rate such as OIS, LIBOR or the bank’s cost of borrowing, and a cost of capital will be added separately. This paper presents a model that combines the two, using funding attributions from a capital model based on the bank’s Expected Loss (EL) rather than the market standard Probability of Default (PD). The basic idea is: A bank could fund a new asset with the combination of debt and equity that leaves its EL constant. The debt-equity mix gives a funding cost that reflects the risk of the asset rather than the bank, so is a more appropriate rate for assessing the asset than the bank’s Weighted Average Cost of Capital (WACC). In this way, the model facilitates decisions consistent with the Modigliani and Miller theorem (i.e. decisions based on the risk of the asset rather than the bank’s cost of funding). A result of the model is that, in accordance with the view of Hull and White (2012), the cost of funding a derivative is given by its CVA-DVA adjusted price and does not require an additional Funding Value Adjustment (FVA). Some of the funding ideas produced by the model have already been suggested by others, such as Piterbarg (2010) and Burgard and Kjaer (2011).

The Cyclical Effects of Accounting Rules on Basel III Liquidity Regulation

Basel III introduces the Liquidity Coverage Ratio (LCR) and the Net Stable Funding Ratio (NSFR) to enhance bank liquidity regulation. This paper investigates whether accounting rules have pro-cyclical effects on these liquidity requirements. By rearranging and simplifying the formulae for the LCR and the NSFR, the paper analyses the linkages among accounting rules, capital rules and liquidity ratios. Due to these linkages, recurring fair value measurements are found to have pro-cyclical effects on both the LCR and the NSFR whereas the provision for loan losses is found to have positive effects on the NSFR although it has little impact on the LCR. The expected loss (EL) model for loan loss provisioning will introduce a new pro-cyclical impact on the NSFR even though it will lessen the pro-cyclical impact of the incurred-loss impairment model on the leverage ratio. Therefore, the contribution of accounting rules to the pro-cyclicality in the regulatory system will be enhanced as the paper also finds that the Basel III liquidity rules and the Basel III capital rules will reinforce each other’s pro-cyclicality.