Stress Testing and Calibration of Macroprudential Policy Tools

We present a semi-structural model of default risk, which is a function of loan and borrower characteristics, economic conditions, and the regulatory environment. We use this model to simulate bank credit losses for stress-testing purposes and to calibrate borrower-based macroprudential tools. The proposed approach is very flexible and is particularly useful when there is limited history of crisis episodes, when crises bring unanticipated shocks where past tail events offer little guidance and when structural shocks or changes in financial regulations have altered the loan default process. We apply the model to quantify mortgage lending risk in two distinct mortgage markets. For each application, we show a range of modeling adjustments that can be made to capture country-specific institutional features. The model uses bank portfolio data broken down by risk bucket and vintage, which enables us to take explicit account of the loan life cycle and to incorporate the housing and economic cycles. This feature facilitates a timely assessment of banks’ loss-absorbing capacity and the buildup of systemic risk conditional on policy. It also enables counterfactual analysis and the evaluation of macroprudential policy interventions.

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France

IMF Staff Country Reports ◽

10.5089/9781513517797.002 ◽

2019 ◽

Vol 19 (263) ◽

Author(s):

Keyword(s):

Default Risk ◽

Quantitative Method ◽

Structural Model ◽

Macroprudential Policy ◽

Policy Makers ◽

Actual Performance ◽

Macroeconomic Variables ◽

Financial Variables ◽

Risk Dynamics ◽

Policy Setting

Macroprudential policy setting faces the challenge of identifying growth of financial and macroeconomic variables above and below potential. The gaps between actual performance and potential are crucial for policy makers but are unobserved. This is especially true for financial variables such as capital and risk of default of borrowers (firms and banks) and lenders (banks and households). Against this backdrop, a macrofinancial structural model is presented that captures (i) sectoral dynamics of firms and banks and feedbacks between them, (ii) capital and default risk dynamics of each sector, (iii) capital and risk gaps i.e., deviations of capital and default risk from potential (the welfare maximizing optimum), and it provides (iv) a quantitative method for measurement.

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Thermodynamics of Mg–Al Order-Disorder Reaction in MgAl2O4-Spinel: Constrained by Prolonged Annealing Experiments at 773–1123 K

Molecules ◽

10.3390/molecules26040872 ◽

2021 ◽

Vol 26 (4) ◽

pp. 872

Author(s):

Yunlu Ma ◽

Xinjian Bao ◽

Xi Liu

Keyword(s):

Spinel Structure ◽

Structural Model ◽

Molar Fraction ◽

Thermodynamic Models ◽

Mgal2o4 Spinel ◽

Cation Order ◽

Prolonged Annealing ◽

Wide Range ◽

History Of ◽

Annealing Experiments

MgAl2O4-spinel has wide industrial and geological applications due to its special structural and physical–chemical features. It is presumably the most important endmember of complex natural spinel solid solutions, and therefore provides a structural model for a large group of minerals with the spinel structure. There exists a well known but still inadequately understood phenomenon in the structure of MgAl2O4-spinel, the Mg–Al cations readily exchanging their positions in response to variations of temperature, pressure, and composition. A large number of experiments were performed to investigate the Mg–Al cation order-disorder process usually quantified by the inversion parameter x (representing either the molar fraction of Al on the tetrahedral T-sites or the molar fraction of Mg on the octahedral M-sites in the spinel structure), and some thermodynamic models were thereby constructed to describe the x-T relation. However, experimental data at some key T were absent, so that the different performance of these thermodynamic models could not be carefully evaluated. This limited the interpolation and extrapolation of the thermodynamic models. By performing some prolonged annealing experiments with some almost pure natural MgAl2O4-spinel plates and quantifying the x values with single-crystal X-ray diffraction technique, we obtained some critical equilibrium x values at T down to 773 K. These new x-T data, along with those relatively reliable x values at relatively high T from early studies, clearly indicate that the CS94 Model (a model constructed by Carpenter and Salje in 1994) better describes the Mg–Al cation order-disorder reaction in MgAl2O4-spinel for a wide range of T. On the basis of the CS94 Model, a geothermometer was established, and its form is T-closure = 21362 × x3 − 12143 × x2 + 6401 × x − 10 (T-closure standing for the closure temperature of the Mg–Al cation exchange reaction). This geothermometer can be used to constrain the thermal history of the geological bodies containing MgAl2O4-spinel.

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A deep metric learning approach for weakly supervised loan default prediction1

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189987 ◽

2021 ◽

pp. 1-13

Author(s):

Kai Zhuang ◽

Sen Wu ◽

Xiaonan Gao

Keyword(s):

Default Risk ◽

Metric Learning ◽

Real Life ◽

Risk Scores ◽

Loan Default ◽

Default Prediction ◽

Heterogeneous Features ◽

Deep Metric Learning ◽

Weakly Supervised ◽

Decision Boundaries

To deal with the systematic risk of financial institutions and the rapid increasing of loan applications, it is becoming extremely important to automatically predict the default probability of a loan. However, this task is non-trivial due to the insufficient default samples, hard decision boundaries and numerous heterogeneous features. To the best of our knowledge, existing related researches fail in handling these three difficulties simultaneously. In this paper, we propose a weakly supervised loan default prediction model WEAKLOAN that systematically solves all these challenges based on deep metric learning. WEAKLOAN is composed of three key modules which are used for encoding loan features, learning evaluation metrics and calculating default risk scores. By doing so, WEAKLOAN can not only extract the features of a loan itself, but also model the hidden relationships in loan pairs. Extensive experiments on real-life datasets show that WEAKLOAN significantly outperforms all compared baselines even though the default loans for training are limited.

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Corporate Loan Default Risk Prediction Based on Machine Learning Method

Modeling and Simulation ◽

10.12677/mos.2021.103088 ◽

2021 ◽

Vol 10 (03) ◽

pp. 890-897

Author(s):

旭岚陈

Keyword(s):

Machine Learning ◽

Risk Prediction ◽

Default Risk ◽

Machine Learning Method ◽

Learning Method ◽

Loan Default ◽

Corporate Loan

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Default Risk Premium in Credit and Equity Market: A New Approach for Structural Model Estimation

SSRN Electronic Journal ◽

10.2139/ssrn.2611984 ◽

2015 ◽

Cited By ~ 1

Author(s):

Alessandro Beber ◽

Raffaele Corvino ◽

Gianluca Fusai

Keyword(s):

Risk Premium ◽

Default Risk ◽

Structural Model ◽

Equity Market ◽

Model Estimation ◽

New Approach

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Effects of Corporate Social Performance on Default Risk: Structural Model-Based Analysis on Japanese Firms

Corporate Social Responsibility and Corporate Finance in Japan - Advances in Japanese Business and Economics ◽

10.1007/978-981-10-8986-2_8 ◽

2018 ◽

pp. 179-199

Author(s):

Megumi Suto ◽

Hitoshi Takehara

Keyword(s):

Default Risk ◽

Structural Model ◽

Corporate Social Performance ◽

Social Performance ◽

Model Based ◽

Japanese Firms ◽

Corporate Social ◽

Model Based Analysis

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Improved Empirical Identification of the Inverse Model of a Squeeze-Film Damper Bearing Based on a Recurrent Neural Network

Volume 7A: Structures and Dynamics ◽

10.1115/gt2018-75777 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ghaith Ghanim Al-Ghazal ◽

Philip Bonello ◽

Sergio G. Torres Cedillo

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Structural Model ◽

Solution Process ◽

Time History ◽

Inverse Model ◽

Training Data ◽

Squeeze Film ◽

Squeeze Film Damper ◽

History Of

Most recently proposed techniques for inverse rotordynamic problems seek to identify the unbalance on a rotor using a known structural model and measurements from externally mounted sensors only. Such non-intrusive techniques are important for balancing rotors that cannot be accessed under operational conditions because of temperature or space restrictions. The presence of nonlinear bearings, like squeeze-film damper (SFD) bearings used in aero-engines, complicates the solution process of the inverse rotordynamic problem. In certain practical aero-engine configurations, the solution process requires a substitute for internal instrumentation to quantify the SFD journal vibration. This can be provided by an inverse model of the SFD bearing which outputs the time history of the relative vibration of the SFD journal relative to its housing, for a given input time history of the SFD force. This paper focuses on the inverse model of the SFD and presents an improved methodology for its identification via a Recurrent Neural Network (RNN) trained using experimental data from a purposely designed rig. The novel application of chirp excitation via two orthogonal shakers considerably improves both the quality of the training data and the efficiency of its generation, relative to an earlier preliminary work. Validation test results show that the RNNs can predict the journal displacement time history with reasonable accuracy. It is therefore expected that such an inverse SFD model would serve as a reliable component in the solution of the wider inverse problem of a rotordynamic system.

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Abstract 359: Lack Of Blood Pressure Augmentation During Stress Testing Is Associated With Increased Mortality.

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.6.suppl_1.a359 ◽

2013 ◽

Vol 6 (suppl_1) ◽

Author(s):

Geoffrey D’Cruz ◽

Ashish Rastogi ◽

Neil Yager ◽

Amarinder Bindra ◽

Steven A Fein ◽

...

Keyword(s):

Blood Pressure ◽

Chest Pain ◽

Stress Echocardiography ◽

Stress Testing ◽

Systolic Function ◽

Stress Test ◽

Care Center ◽

Tertiary Care ◽

History Of ◽

Response To Exercise

Objective: We investigated long-term outcomes associated with hypertensive response to exercise in patients with chest pain referred for stress echocardiography. Methods: Records of 404 patients with normal baseline LV systolic function (45% females, mean age 60+/-11 years, baseline SBP 136+/-20 mmHg, 26% with CAD, 4% with CHF, 39% with hypertension, 13% with diabetes mellitus, 5% with peripheral vascular disease, 21% with history of smoking or active smoking, 43% on beta-blockers, 23% on ACE-inhibitors/ARBs) referred for chest pain evaluation with stress echocardiography at a single tertiary care center were reviewed. Demographics, clinical data, and outcomes were collected. Median length of followup was 35+/-0.3 months. Patients were divided into four groups depending on their maximum blood pressure during exercise (greater or less than 180mmHg) and whether they achieved their age-adjusted target heart rate. Results: Contrary to the expectations, hypertensive response to exercise was not associated with the increased mortality (Table). Instead, lack of blood pressure augmentation during exercise and low double product were predictive of increased mortality. History of CHF (p=0.0003) and/or PVD (p=0.001) were the strongest predictors of failure to augment blood pressure during stress testing. Conclusions: Failure to augment systolic blood pressure during exercise appears to be associated with increased mortality. Although ischemia on echocardiography and reduced exercise capacity are the stress test outcomes traditionally associated with poor prognosis, failure to augment blood pressure during exercise may be an important predictor of mortality as well. Additional studies of this subject are needed.

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Constraints to Economic Growth in South Africa

10.1093/oxfordhb/9780192894199.013.6 ◽

2021 ◽

pp. 110-134

Author(s):

Kenneth Creamer

Keyword(s):

Economic Growth ◽

South Africa ◽

Industrial Structure ◽

Commodity Price ◽

Structural Factors ◽

Weak State ◽

Policy Interventions ◽

Macroeconomic Imbalances ◽

History Of ◽

The Impact

This chapter analyses the drivers and constraints on the rate of economic growth in South Africa from the 1950’s apartheid-era through to the democratic period post-1994. Key structural factors identified as impacting on the rate and composition of economic growth include the country’s history of racial injustice and exclusion, its industrial structure and linkages to the global commodity price cycle, the evolution of macroeconomic imbalances and related infrastructure investment failures, and the impact of weak state capacity and corruption. Thereafter, the chapter outlines a number of strategic policy interventions for overcoming constraints to inclusive economic growth in South Africa.

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