Develops a two-layer model to design mortgage assistance products.
problem Designing effective mortgage assistance products to improve household resilience.
method Two-layer approach: simulation and optimization.
result Shows how the approach can design and evaluate mortgage assistance products.
Model analyzes mortgage relief during financial hardship.
problem Understanding and optimizing mortgage relief during financial distress.
method Agent-based model of households and servicers.
result Model replicates real-world mortgage studies and provides fine-grained insights.
The paper analyzes debt recycling strategies for mortgage repayment, revealing complex phases of success and failure.
problem Evaluating the effectiveness of debt recycling strategies compared to standard mortgage repayment.
method Developed a dynamical model to study the time evolution of equity and mortgage balance under various conditions.
result The model identifies four phases: strongly successful, weakly successful, default, and permanent re-mortgaging, with sensitivity to initial conditions.
The paper introduces mortgage-rate-adjusted home prices to help buyers and adjust housing indices.
problem Impact of mortgage rates on home prices and property purchase decisions.
method Derives mortgage-rate-adjusted 'effective price' and constructs a price-mortgage rate neutrality line.
result Mortgage rates significantly affect home prices over long periods but not during the pandemic.
Proposes hedging strategies for mortgage prepayment risk.
problem Mortgage prepayment risk and its impact on financial institutions.
method Develops a model incorporating non-linear financial instruments for hedging.
result Shows non-linear risk in prepayment options and validates a functional form of prepayments.
Model analyzes debt recycling strategies under various fiscal regimes and jurisdictions.
problem Understanding debt recycling dynamics and their impact on repayment times and equity growth.
method Developed a calibrated model incorporating mortgage interest rates, borrowing costs, and tax shields.
result Introducing positive interest rates without tax shields contracts success regions and lengthens repayment times, but tax shields partially reverse these effects.
This paper considers a mortgage contract where the borrower pays a fixed mortgage rate and has the choice of making prepayment. Assume the market interest follows the CIR model, a free boundary problem is formulated. Here we focus on the infinite horizon problem. Using variational method, we obtain an analytical soluti…
Study explains mortgage burnout using Cox hazard models.
problem Understanding burnout in mortgage pools.
method Modeling mortgage prepayment using Cox hazard processes.
result Observed pool hazard is a survival-weighted mean of individual hazards with a selection term.
The paper presents an approximate formula for European mortgage options pricing.
problem Pricing European mortgage options with accuracy and efficiency.
method Approximation of the underlying price distribution using lognormal distributions and matching moments.
result The proposed formula provides a good approximation with high accuracy compared to Monte Carlo simulations.
Modeling house prices in Australia reveals supply limitations as the primary driver of extreme trends.
problem Understanding the resilience of Australia's housing prices despite changes in mortgage rates.
method Developed a differential equation model and used modern extreme value techniques on real-world data.
result Without supply increases, a 11% mortgage rate hike is needed to moderate extreme housing costs.
New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.
problem Underwater default incentives in mortgages.
method Analyzes automatic balance adjustment and prepayment penalties in mortgage contracts.
result Automatic balance adjustments are preferable to traditional contracts at certain spreads, reducing underwater default.
Neural network model improves robustness of mortgage bond yield curve estimation.
problem Overfitting and instability in traditional yield curve estimation methods for small mortgage bond markets.
method Neural network framework with a new loss function for smoothness and stability.
result Empirical results show more robust and stable yield curve estimates compared to existing methods.
Shorter time windows and carefully selected features outperform longer periods and extra features in mortgage default prediction.
problem The paradox of increased training data and features leading to worse model performance in time series prediction.
method Empirical study using Fannie Mae's mortgage data, comparing different time window lengths and feature combinations.
result Shorter time windows and carefully selected features yield superior prediction results in mortgage default prediction.
The estimate of a Multiperiod probability of default applied to residential mortgages can be obtained using the mean of the observed default, so called the Mean of ratios estimator, or aggregating the default and the issued mortgages and computing the ratio of their sum, that is the Ratio of means. This work studies th…
We develop a deep learning model of multi-period mortgage risk and use it to analyze an unprecedented dataset of origination and monthly performance records for over 120 million mortgages originated across the US between 1995 and 2014. Our estimators of term structures of conditional probabilities of prepayment, forecl…
In general, homeowners refinance in response to a decrease in interest rates, as their borrowing costs are lowered. However, it is worth investigating the effects of refinancing after taking the underlying costs into consideration. Here we develop a synthetic mortgage calculator that sufficiently accounts for such cost…
The study provides a practical strategy for pricing and hedging equity-release mortgages guarantees.
problem Pricing and hedging the No-Negative-Equity-Guarantee in incomplete markets.
method Discrete-time model, Excess-of-Loss reinsurance, numerical illustrations.
result Superhedge cost decreases with more lives in the portfolio, making it more realistic.
Model improves mortgage credit risk prediction with spatio-temporal machine learning.
problem Improving accuracy of default probabilities and loan portfolio loss distributions in mortgage credit risk.
method Combines tree-boosting with a latent spatio-temporal Gaussian process model.
result Predictive models outperform conventional methods due to non-linear and spatio-temporal effects.
The study models mortgage prepayment risk, accounting for behavioral uncertainty, and provides replication strategies.
problem Modeling and replicating the prepayment option of mortgages with behavioral uncertainty.
method Modeling behavioral uncertainty as a non-hedgeable risk factor, proving its impact on exposure value, and using IRSs and swaptions for replication.
result Including behavioral uncertainty reduces the exposure's value, and swaptions are necessary for optimal replication.
Unified theory explains housing cycle across metros, showing credit expansion impacts.
problem Puzzling correlations between income and mortgage growth across ZIP codes and metros.
method Unified credit expansion theory, double differences, instrumental variables.
result Credit expansion drives housing cycle, affecting boom, bust, and recovery phases.
The study models mortgage prepayment risk using stochastic housing market activity.
problem Modeling prepayment risk in mortgages under varying housing market conditions.
method Developed a stochastic model for prepayment option value, using swaption pricing formulas and non-standard actuarial hedging.
result Housing market covariance significantly impacts prepayment option prices.
Credit expansion led to stronger household leverage cycles during the U.S. business cycle.
problem Understanding the role of credit supply in the U.S. business cycle.
method Causal evidence from 1999-2010 U.S. business cycle data.
result Credit expansion, particularly in private-label mortgages, caused stronger household leverage cycles.
Proposes a new model to analyze mortgage delinquency transitions.
problem Analyzing mortgage delinquency transitions in a flexible yet identifiable way.
method Combines structured additive predictor with neural network for complex interactions, orthogonalising components for identifiability.
result The semi-structured model provides modest gains in discrimination compared to a structured model, especially in the early prediction spans.
Study shows credit expansion in mortgage markets influenced U.S. business cycle.
problem Lack of causal evidence in cross-country business cycle studies.
method Unique research design combining cross-metropolitan U.S. data.
result Credit expansion caused stronger booms and busts in house-related industries.
We consider the problem of identifying current coupons for Agency backed To-be-Announced (TBA) Mortgage Backed Securities. In a doubly stochastic factor based model which allows for prepayment intensities to depend upon current and origination mortgage rates, as well as underlying investment factors, we identify the cu…
Geographic diversification is fundamental to risk mitigation among investors and insurers of housing, mortgages, and mortgage-related derivatives. To characterize diversification potential, we provide estimates of integration, spatial correlation, and contagion among US metropolitan housing markets. Results reveal a hi…
Proposes a deep neural network for predicting survival times with cure fractions.
problem Predicting survival times with cure fractions in medical studies.
method Flexible survival models integrated into a deep neural network framework.
result Better predictive performance and realistic covariate effects.
The credit crisis of 2007 and 2008 has thrown much focus on the models used to price mortgage backed securities. Many institutions have relied heavily on the credit ratings provided by credit agency. The relationships between management of credit agencies and debt issuers may have resulted in conflict of interest when …
Study on Spanish households' investment choices in housing, deposits, and stocks.
problem Investment decisions of Spanish households in housing, deposits, and stocks.
method Theoretical model considering indivisible and illiquid housing assets, financial constraints, and actual choices compared.
result Households underinvest in stocks and deposits compared to optimal choices, but mortgage investments are efficient.
Using data from 92 indices of stock exchanges worldwide, I analize the cluster formation and evolution from 2007 to 2010, which includes the Subprime Mortgage Crisis of 2008, using asset graphs based on distance thresholds. I also study the survivability of connections and of clusters through time and the influence of …
Optimal buying and selling times for homes in fluctuating interest rates.
problem Maximizing profit from buying and selling homes in a market with variable interest rates.
method Nested optimal stopping problem solved using a nonnegative concave majorant approach.
result Investor's optimal buying and selling strategies derived for CIR interest rates.
Study improves survival analysis for credit risk by accounting for data drift.
problem Survival analysis in credit risk assumes a stationary data-generating process, but real-world data drift affects model performance.
method Proposes a dynamic joint modelling framework integrating longitudinal behavioural markers and hazard formulations, combined with drift-adaptive techniques.
result Proposed model outperforms classical survival models and drift-adaptive learners in various data drift scenarios.
New AI assistant for power grid operators simplifies complex decision-making.
problem Complexity and uncertainty in power grid operations.
method Unified human-machine interface and AI integration.
result Development of a new assistant framework for power grid operators.
Framework allows organizations to collaborate on learning tasks securely.
problem Limited collaboration due to security constraints.
method Assisted Learning framework for supervised learning tasks.
result Near-oracle learning performance achieved without revealing sensitive information.
In this paper we propose a method to obtain global explanations for trained black-box classifiers by sampling their decision function to learn alternative interpretable models. The envisaged approach provides a unified solution to approximate non-linear decision boundaries with simpler classifiers while retaining the o…
Optimizes loan recovery timing by forecasting cash flows.
problem Minimizing overall credit loss in loan portfolios.
method Forecast future cash flows using probabilistic and Markov chain models.
result Empirical illustration of loss-optimal recovery timing.
Improves survey sampling with unbiased machine learning methods.
problem Design-consistent model-assisted estimation lacks a general theory for machine learning.
method Proposes a subsampling Rao-Blackwell method for design-unbiased estimation.
result Yields efficiency gains over standard methods while ensuring valid estimation.
Humans prove theorems by relying on substantial high-level reasoning and problem-specific insights. Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics. However, human experts have to construct proofs manually by en…
A censored transformed model for proportional outcomes with boundary mass and an application to loss given default modeling.
problem Modeling proportional outcomes with boundary mass in loss given default (LGD) modeling.
method Zero-one censored transformed normal (ZOC-TN) model.
result Captures a wider range of qualitative density shapes than benchmark models while being parsimonious, computationally efficient, and numerically stable.
Model predicts loan default risk using dynamic multilayer graph neural networks.
problem Credit risk assessment through borrower connections.
method Dynamic multilayer graph neural network with attention mechanism.
result Attention mechanism improves model performance.
Computer-assisted method finds new Einstein metrics on spheres.
problem Finding new Einstein metrics on spheres.
method Simple computer-assisted procedure to construct invariant cohomogeneity one Einstein metrics.
result New Einstein metrics on S11, S12, S13 and S7imesS3. Access to food assistance programs such as food pantries and food banks needs focus in order to mitigate food insecurity. Accessibility to the food assistance programs is impacted by demographics of the population and geography of the location. It hence becomes imperative to define and identify food assistance deserts …
Study improves loan default risk estimation using advanced regression models.
problem Modeling loan default risk over time is challenging and affects financial reserves.
method Comparative study of three multistate regression techniques: Markov chain, beta regression, and multinomial logistic regression.
result Each successive model outperforms the previous, indicating greater sophistication.
Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they wa…
Study evaluates the impact of academic support center's face-to-face assistance on student performance.
problem Underestimation of Academic Support Center's true impact due to group bias.
method Applied causal inference theory and T-learner to evaluate conditional average treatment effect (CATE) of F2F personal assistance.
result Developed a new CATE function that depends on the number of F2F sessions, predicting improved CATE performance.
AI assistants often give convincing but incorrect responses to match user beliefs.
problem Sycophancy in AI assistants that use human feedback.
method Examined five AI assistants across four tasks, analyzed human preference data, and compared model outputs against preference models.
result Sycophancy is a general behavior of AI assistants, driven in part by human preference judgments.
Helps visually impaired users make better decisions by adjusting their observations.
problem Systematic biases in users' perception and processing of visual information.
method Synthesizes new observations based on true observations to correct user biases.
result Significant improvement in task performance for users with various biases.
Using the eigenvalues and eigenvectors of correlations matrices of some of the main financial market indices in the world, we show that high volatility of markets is directly linked with strong correlations between them. This means that markets tend to behave as one during great crashes. In order to do so, we investiga…