Default Model Research Articles

How Does a Sport Psychological Intervention Help Professional Cyclists to Cope With Their Mental Health During the COVID-19 Lockdown?

All around the world in March, due to COVID-19, competitive sport calendars were suddenly canceled, jeopardizing the training programs of athletes. Moreover, in Italy, the government banned all non-essential travel across the entire country from the beginning of March. Consequently, Italian cyclists were banned from leaving their homes and therefore unable to perform their ordinary training activities. The Italian Association of Professional Cyclists (ACCPI) early on during that period noticed that several cyclists were experiencing a worrying decrease in their mental well-being and asked the authors to set up an online Sport Psychology Intervention (SPI) during lockdown to enhance the athletes' mental health. Through a number of unprecedented events and considerations, the aim of the current investigation was to assess the Italian cyclists' mental health during the lockdown and its changes after the SPI. We validated the Italian version of the Sport Mental Health Continuum Short Form (Sport MHC-SF)—presented in Study 1—and then applied it to a sample of Italian professional cyclists—presented in Study 2—prior to and after the SPI. To achieve these objectives, the reliability and construct validity of the Italian version of the Sport MHC-SF were tested in Study 1. RM-MANOVA tests were run to evaluate the effect of SPI on cyclists in Study 2. A total of 185 Italian athletes were involved in the validation of the MHC in Study 1 and 38 professional cyclists in Study 2. Results from Study 1 suggested a three-factor higher order model of Sport MHC-SF [Model fit: χ2(df) = 471.252 (252), p < 0.000; CFI = 0.951; RMSEA = 0.049; RMR= 0.048]. MCFA showed that the default model kept invariance among groups of athletes (i.e., female, male, individual, and team sports). Results from Study 2 highlighted that professional cyclists who followed the SPI were able to cope better with psychological stressors, showing improved well-being compared to the athletes that did not. No significant differences were found for emotional and social well-being. The present multi-study paper contributes to the theoretical field with a validated measure of Sport MHC-SF translated in the Italian language and culture. It also provides practical implications related to cases of reduced mental health due to injury, illness, or similar situations of home confinement in the future.

Estimating the CMIP6 Anthropogenic Aerosol Radiative Effects with the Advantage of Prescribed Aerosol Forcing

The prescribed anthropogenic aerosol forcing recommended by Coupled Model Intercomparison Project Phase 6 (CMIP6) was implemented in an atmospheric model. With the reduced complexity of anthropogenic aerosol forcing, each component of anthropogenic aerosol effective radiative forcing (ERF) can be estimated by one or more calculation methods, especially for instantaneous radiative forcing (RF) from aerosol–radiation interactions (RFari) and aerosol–cloud interactions (RFaci). Simulation results show that the choice of calculation method might impact the magnitude and reliability of RFari. The RFaci—calculated by double radiation calls—is the definition-based Twomey effect, which previously was impossible to diagnose using the default model with physically based aerosol–cloud interactions. The RFari and RFaci determined from present-day simulations are very robust and can be used as offline simulation results. The robust RFari, RFaci, and corresponding radiative forcing efficiencies (i.e., the impact of environmental properties) are very useful for analyzing anthropogenic aerosol radiative effects. For instance, from 1975 to 2000, both RFari and RFaci showed a clear response to the spatial change of anthropogenic aerosol. The global average RF (RFari + RFaci) has enhanced (more negative) by ~6%, even with a slight decrease in the global average anthropogenic aerosol, and this can be explained by the spatial pattern of radiative forcing efficiency.

Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model

Abstract. A stochastic deep convection parameterization is implemented into the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1.0 (EAMv1). This study evaluates its performance in simulating precipitation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. The well-known problem of “too much light rain and too little heavy rain” is alleviated, especially over the tropics. As a result, the contribution from different rain rates to the total precipitation amount is shifted toward heavier rain. The less frequent occurrence of convection contributes to suppressed light rain, while more intense large-scale and convective precipitation contributes to enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. The sensitivity of the rainfall intensity PDF to the model vertical resolution is examined. The relationship between precipitation and dilute convective available potential energy in the stochastic simulation agrees better with that in the Atmospheric Radiation Measurement (ARM) observations compared with the standard model simulation. The annual mean precipitation is largely unchanged with the use of the stochastic scheme except over the tropical western Pacific, where a moderate increase in precipitation represents a slight improvement. The responses of precipitation and its extremes to climate warming are similar with or without the stochastic deep convection scheme.

Forecasting corporate financial distress in the Southeast Asian countries: A market-based approach

This study is conducted to investigate the prediction of corporate financial distress based on the Merton (1974) market-based Distance to Default (DD) model over the period from 1997 to 2016 which covers a range of economic financial circumstances, including the Asian Financial Crisis (AFC) and Global Financial Crisis (GFC). The study focusses on the six largest countries in the ASEAN Economic Community (AEC), comprising of Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. Unlike previous studies which focus mainly on bankruptcy, this paper focusses on early warning distress indicators that signal distress well before bankruptcy. This is when firms experience difficulty in servicing debt as measured by interest coverage ratio (ICR) at a firm level and non-performing loans (NPLs) at a country level. Key empirical findings from this paper indicate that the market-based distance-to-default (DD) model is generally a good early warning indicator of financial distress in the following year, particularly for ICR, but that prediction accuracy varies between individual countries in the Southeast Asian region.

A comparison of the performance of alternative Machine Learning algorithms on a credit risk dataset

We compare alternative methodologies for Credit Risk estimation. Here we introduce a Bayesian Model Averaging (BMA) of Logistic Regressions for Probability of Default modeling where the model space is sampled via Markov Chain Monte Carlo methods making use of the approximation of the posterior probability introduced in [Schwarz, 1978] and used in the context of sparse problems in [Sala-i Martin et al., 2004], [Gross and Poblacion, 2004] and [Torresetti, 2021]. We will compare this methodology against some popular Machine Learning ensemble methods for regression trees: Bagging (Bootstrap Aggregating) and Boosting (Adaptive Boosting, Gradient Boosting). The results on the credit risk dataset at our disposal show how Gradient Boosting proves to be the top performer but with a slow convergence speed given the cooling coecient that was used to make it less greedy. As not too distant seconds we see the performance of BMA and Adaptive Boosting where in particular BMA proves to have a faster convergence speed among the specific versions of the algorithms implemented. Finally a simple average of estimates as in Bagging proves to have the lowest performance of the ensemble methods considered.

The Role of Financial Analysis in Mitigating Banking Default " Study on Yemeni Islamic Banks"

The study problem was that Yemeni Islamic banks did not rely on the results and outputs of financial analysis to predict failure before it occurs. Accordingly, the study aimed to find out to what extent Yemeni banks depend on financial analysis as a tool for predicting the default, and the extent of its ability to mitigate bank default at these banks. It also aimed to identify whether these banks use all financial analysis tools for making credit decisions. The researchers used the descriptive analytical method and distributed a questionnaire to the sample (50) of the credit employees at Yemeni Islamic banks. The data were analyzed using SPSS. The findings revealed that the Yemeni Islamic banks depend on financial analysis as a basis for disclosure of default, which has a main role in mitigating banking default. The banks usually use multi financial analysis tools when evaluating the credit status of clients; they do not use predicting financial default models as a main tool to predict failure before it occurs; and they do not have early warning systems to predict any possibility of customers’ default.

The Role of Financial Analysis in Mitigating Banking Default " Study on Yemeni Islamic Banks"

The study problem was that Yemeni Islamic banks did not rely on the results and outputs of financial analysis to predict failure before it occurs. Accordingly, the study aimed to find out to what extent Yemeni banks depend on financial analysis as a tool for predicting the default, and the extent of its ability to mitigate bank default at these banks. It also aimed to identify whether these banks use all financial analysis tools for making credit decisions. The researchers used the descriptive analytical method and distributed a questionnaire to the sample (50) of the credit employees at Yemeni Islamic banks. The data were analyzed using SPSS. The findings revealed that the Yemeni Islamic banks depend on financial analysis as a basis for disclosure of default, which has a main role in mitigating banking default. The banks usually use multi financial analysis tools when evaluating the credit status of clients; they do not use predicting financial default models as a main tool to predict failure before it occurs; and they do not have early warning systems to predict any possibility of customers’ default.

Uncertainty Premia, Sovereign Default Risk, and State-Contingent Debt

We analyze how concerns for model misspecification on the part of international lenders affect the desirability of issuing state-contingent debt instruments in a stan- dard sovereign default model a la Eaton and Gersovitz (1981). We show that for the commonly used threshold state-contingent bond structure (e.g., the GDP-linked bond issued by Argentina in 2005), the model with robustness generates ambiguity premia in bond spreads that can explain most of what the literature has labeled as novelty premium. While the government would be better off with this bond when facing rational expectations lenders, this additional source of premia leads to welfare losses when facing robust lenders. Finally, we characterize the optimal design of the state-contingent bond and show how it varies with the level of robustness. Our find- ings rationalize the little use of these instruments in practice and shed light on their optimal design.

Considering sex as a biological variable will require a global shift in science culture.

For over half a century, male rodents have been the default model organism in preclinical neuroscience research, a convention that has likely contributed to higher rates of misdiagnosis and adverse side effects from drug treatment in women. Studying both sexes could help to rectify these public health problems, but incentive structures in publishing and career advancement deter many researchers from doing so. Moreover, funding agency directives to include male and female animals and human participants in grant proposals lack mechanisms to hold recipients accountable. In this Perspective, we highlight areas of behavioral, cellular and systems neuroscience in which fundamental sex differences have been identified, demonstrating that truly rigorous science must include males and females. We call for a cultural and structural change in how we conduct research and evaluate scientific progress, realigning our professional reward systems and experimental standards to produce a more equitable, representative and therefore translational body of knowledge.

Corporate Probability of Default (PD) Modelling for Banks in Emerging Economies: A Case Study of Zimbabwe Stock Exchange (ZSE) Listed Counters

Corporate Probability of Default (PD) Modelling for Banks in Emerging Economies: A Case Study of Zimbabwe Stock Exchange (ZSE) Listed Counters

Optimizing Hyperparameters for MINIST Dataset Classification using Deep Learning Techniques

The potential of deep learning to improve the performance of image classification has been demonstrated. In this paper, we present a study on the optimization of the hyperparameters for the classification of the MNIST dataset. We performed a comparison between a standard two-layer perceptron model and a CNN model with different techniques. The optimized hyperparameters for CNN are based on the number of filters, kernel size, and convolutional layers. The optimized CNN model performed better than the default model on the classification task of the MNIST dataset. The various hyperparameters included the learning rate, batch size, the number of hidden layers, the dropout rate, the activation function, and the optimizer. The optimized CNN model was able to achieve an accuracy of 99% on a test, which is significantly better than the 96% accuracy of the default model. The difference between the two models is that the former takes longer to train and has a slightly longer time per image. The study demonstrates the importance of optimizing the hyperparameters in deep learning-focused classification tasks. The findings show how CNN architectures perform well in these applications, and it shows how optimizing these components can yield superior results. These recommendations can help further develop efficient and accurate models for this field.

A New Film Parameter for Rough Surface EHL Contacts with Anisotropic and Isotropic Structures

Numerous tribological contacts worldwide rely on adequate lubrication quality for proper functionality. Despite this, there is no existing approach to accurately predict the state of lubrication. The default model since introduced in the 1960s—the Lambda-ratio, defined as the oil film thickness over the surface roughness height—is unpredictable and may yield erroneous results. Here, we put forward a framework for a new updated film parameter, {Lambda }^{*}, which accounts for the elasto-hydrodynamic lubrication (EHL) effects induced by surface irregularities on the microscopic scale (micro-EHL). This new film parameter was validated in ball-on-disc tribological tests with engineering surfaces comprising isotropic and anisotropic structures. As expected, the new model was found to accurately predict the experimentally measured true mixed and full-film EHL regimes. The ability to accurately predict the mode of lubrication represents a major advance in designing tribological interfaces for optimal efficiency and durability.

Numerical study on combustion characteristics and heat flux distributions of 660‐MW ultra‐supercritical double‐reheat tower‐type boiler

AbstractUltra‐supercritical double‐reheat technology, as one of the most advanced coal‐fired power generation technology, is an important direction for emission reduction and energy saving in the world. In this study, the numerical calculation was executed in a 660‐MW ultra‐supercritical double‐reheat tower‐type boiler under deep‐air‐staging conditions. The refined HCN oxidation model was adopted to substitute the default model implemented by the user‐defined functions to calculate the NOx emission. The influences of the boiler load, over‐fire air (OFA) ratio, and excess air coefficient on temperature, species, and heat flux distributions were investigated. Results show that the decrement of the boiler load from boiler maximum continuous rating to 50% turbine heat acceptance gives rise to an increase of NOx emission. The heat flux distributions along with the furnace width direction present bell shaped. When the OFA ratio rises from 17% to 43%, NOx emission descends from 357.7 to 179.3 mg m−3 at the furnace outlet, and the heat flux distributions become more uniform along with the furnace width direction with lower peaks. Temperatures, species, and heat flux distributions are similar under the three different excess air coefficients. The NOx emission is the lowest when the excess air coefficient is 1.15. The results could provide a reference for combustion characteristics optimization and hydrodynamic calculation of ultra‐supercritical double‐reheat tower‐type boiler.

Downside risk and defaultable bond returns

This paper analyzes the influence of downside risk on defaultable bond returns. By introducing a defaultable bond-trading model, we show that the decline in market risk tolerance and information accuracy leads to trading loss under downside conditions. Our empirical analysis indicates that downside risk can explain a large proportion of the variation in yield spreads and contains almost all valid information on liquidity risk. As the credit level decreases, the explanatory power of downside risk increases significantly. We also investigate the predictive power of downside risk in cross-sectional defaultable bond excess returns using a portfolio-level analysis and Fama-MacBeth regressions. We find that downside risk is a strong and robust predictor for future bond returns. In addition, due to the higher proportion of abnormal transactions in the Chinese bond market, downside risk proxy semi-variance can better explain yield spreads and predict portfolio excess returns than the proxy value at risk.

Comparison of the Coupled Model for Oil spill Prediction (CMOP) and the Oil Spill Contingency and Response model (OSCAR) during the DeepSpill field experiment

An oil spill model is an important tool for environmental risk assessment, strategic planning, and tactical decision making in the event of an oil spill. However, limited data exist to evaluate such models and their performance. During the DeepSpill field campaign, a unique dataset was acquired by monitoring a deliberate deep-water oil blowout. In this work, we evaluate and compare two oil spill models – the Coupled Model for Oil spill Prediction (CMOP) and the Oil Spill Contingency and Response model (OSCAR) against the DeepSpill experiment. We find that the general plume trajectory is captured well with a default model setup for both models. However, to accurately model the surface slick development, it was necessary to alter modeling parameters and incorporate model changes to increase scenario flexibility. Through this work, we build further confidence in the two models and provide suggestions for improvements.

Beyond the contract: client behavior from origination to default as the new set of the loss given default risk drivers

Modeling loss given default has increased in popularity as it has become a crucial parameter for establishing capital buffers under Basel II and III and for calculating the impairment of financial assets under the International Financial Reporting Standard 9. The most recent literature on this topic focuses mainly on estimation methods and less on the variables used to explain the variability in loss given default. In this paper, we expand this part of the modeling process by constructing a set of client-behavior-based predictors that can be used to construct more precise models, and we investigate the economic justifications empirically to examine their potential usage. The main novelty introduced in this paper is the connection between loss given default and the behavior of the contract owner, not just the contract itself. This approach results in the reduction of the values of selected error measures and progressively improves the forecasting ability. The effect is more visible in a parametric method (fractional regression) than in a nonparametric method (regression tree). Our findings support incorporating client-oriented information into loss given default models.

Effects of Improving Evapotranspiration Parameterization Scheme on WOFOST Model Performance in Simulating Maize Drought Stress Process

To solve the problem on poor performance of crop growth model in simulating crop growth process under water stress, three schemes including improving evapotranspiration parameterization scheme with Penman-Monteith method, building dynamic crop coefficient (Kc) and considering simultaneously two above-mentioned solutions which are respectively named as the PM, CC and PMCC schemes are used to improve WOFOST model. Their effects on model performance in simulating maize drought stress process are evaluated based on experiments of different sowing dates on 20 April, 30 April and 10 May conducted in Jinzhou in the year with normal precipitation (2012) and the dry year (2015 and 2018). The results show that compared with the default model, PM scheme plays a role in increasing potential evapotranspiration and transpiration rate in 2012, while CC scheme decreases (increases) transpiration rate as Kc is larger (smaller) than the model default value 1, respectively. Three schemes hardly affect simulation accuracies of soil moisture in rooted zone, total above ground production, leaf area index (ILA) and yield. In 2015, PM scheme makes ILA, total above ground production yield and soil moisture dramatically smaller than those with the default model after the jointing stage of maize. It also increases (decreases) transpiration rate before (after) the whorl stage of maize. After improving the model with CC scheme, ILA, total above ground production, yield and soil moisture are slightly larger than those simulated by the default model after the whorl stage, and the simulated transpiration rates are smaller (larger) than those simulated by the default model before (after) the whorl stage of maize. Nevertheless, the above-mentioned variables simulated by the improved model with PMCC scheme range between those simulated by the model with PM and CC schemes, and ILA, total above ground production and yield are obviously closer to the observations. Specifically, the mean increments of simulation accuracies for all growth periods in three sowing dates are 6%, 21% and 3% for ILA and 8%, 8% and 14% for total above ground production, respectively. The simulation accuracies of yield for three sowing dates increase by 66%, 63% and 66%, respectively. In 2018, the simulation accuracies of total above ground production and yield for the sowing dates on 20 April and 30 April are obviously improved by PMCC scheme, and increase by 5%, 1% in total above ground production as well as 32%, 5% in yield. Therefore, the model performance with PMCC scheme in simulating maize growth is significantly improved under water stress.

Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0

Abstract. The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy, and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site-specific field data focusing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields, as well as water, energy, and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines following Lu et al. (2017) in CLM5; (2) implementing plant-specific parameters for sugar beet, potatoes, and winter wheat, thereby adding the two crop functional types (CFTs) for sugar beet and potatoes to the list of actively managed crops in CLM5; and (3) introducing a cover-cropping subroutine that allows multiple crop types on the same column within 1 year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity as it reduces erosion and improves soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop-specific parameterization and the winter wheat subroutines led to a significant simulation improvement in terms of energy fluxes (root-mean-square error, RMSE, reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange, and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover-cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes, seasonal cycle of LAI, and latent heat flux (reduction of wintertime RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.

Robust optimal investment and benefit payment adjustment strategy for target benefit pension plans under default risk

In this paper, we consider the optimal investment and benefit payment problem for a target benefit plan (TBP) with default risk and model uncertainty. The pension fund is invested in a risk-free asset, a stock and a defaultable bond. The objective is to maximize the wealth and benefit excess from the target value or minimize the wealth and benefit gap from the target value with exponential function. Applying stochastic control approach, we establish the Hamilton–Jacobi–Bellman equations for both the post-default case and the pre-default case, respectively. Robust optimal investment strategies and benefit payment adjustment strategies are derived explicitly for the two cases. We also consider the non-ambiguity model for degenerate case and compare the results under two scenarios. Numerical analysis is provided to illustrate the effects of parameters on the optimal strategies and demonstrate the properties of the strategies.

Limited Liability, Strategic Default and Bargaining Power

In this paper we examine the effects of limited liability on mortgage dynamics. While the literature has focused on default rates, renegotiation, or loan rates individually, we study them together as equilibrium outcomes of the strategic interaction between lenders and borrowers. We present a simple model of default and renegotiation where the degree of limited liability plays a key role in agents' strategies. We then use Fannie Mae loan performance data to test the predictions of the model. We focus on Metropolitan Statistical Areas that are crossed by a State border in order to exploit the discontinuity in regulation around the borders of States. As predicted by the model, we find that limited liability results in higher default rates and renegotiation rates. Regarding loan pricing, while the model predicts higher interest rates for limited liability loans, we find no such evidence in the Fannie Mae data. We further investigate this by using loan application data, which contains the interest rates on loans sold to private vs public investors. We find that private investors do price in the difference in ex-ante predictable default risk for limited liability loans. JEL Classification: D10, E40, G21, R20, R30