A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper introduces mortgage-rate-adjusted home prices to help buyers and adjust housing indices.
The paper analyzes debt recycling strategies for mortgage repayment, revealing complex phases of success and failure.
Develops a two-layer model to design mortgage assistance products.
The study models mortgage prepayment risk using stochastic housing market activity.
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…
The study models mortgage prepayment risk, accounting for behavioral uncertainty, and provides replication strategies.
Neural network model improves robustness of mortgage bond yield curve estimation.
Proposes hedging strategies for mortgage prepayment risk.
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.
The paper presents an approximate formula for European mortgage options pricing.
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…
Modeling house prices in Australia reveals supply limitations as the primary driver of extreme trends.
Study shows credit expansion in mortgage markets influenced U.S. business cycle.
Shorter time windows and carefully selected features outperform longer periods and extra features in mortgage default prediction.
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…
Model analyzes mortgage relief during financial hardship.
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…
Model analyzes debt recycling strategies under various fiscal regimes and jurisdictions.
Study explains mortgage burnout using Cox hazard models.
Model assesses loan profitability under changing credit conditions.
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 …
New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.
Using a modified damped harmonic oscillator model equivalent to a model of market dynamics with price expectations, we analyze the reaction of financial markets to shocks. In order to do this, we gather data from indices of a variety of financial markets for the 1987 Black Monday, the Russian crisis of 1998, the crash …
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…
Optimal buying and selling times for homes in fluctuating interest rates.
Model improves mortgage credit risk prediction with spatio-temporal machine learning.
Unified theory explains housing cycle across metros, showing credit expansion impacts.
Credit expansion led to stronger household leverage cycles during the U.S. business cycle.
New measure predicts Dutch housing market downturns.
The dual crises of the sub-prime mortgage crisis and the global financial crisis has prompted a call for explanations of non-equilibrium market dynamics. Recently a promising approach has been the use of agent based models (ABMs) to simulate aggregate market dynamics. A key aspect of these models is the endogenous emer…
Proposes a new model to analyze mortgage delinquency transitions.
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…
Pearson correlation and mutual information based complex networks of the day-to-day returns of US S&P500 stocks between 1985 and 2015 have been constructed in order to investigate the mutual dependencies of the stocks and their nature. We show that both networks detect qualitative differences especially during (recent)…
This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metrop…
Proposes a deep neural network for predicting survival times with cure fractions.
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 …
GPU computing has become popular in computational finance and many financial institutions are moving their CPU based applications to the GPU platform. Since most Monte Carlo algorithms are embarrassingly parallel, they benefit greatly from parallel implementations, and consequently Monte Carlo has become a focal point …
The 2008 mortgage crisis is an example of an extreme event. Extreme value theory tries to estimate such tail risks. Modern finance practitioners prefer Expected Shortfall based risk metrics (which capture tail risk) over traditional approaches like volatility or even Value-at-Risk. This paper provides a quantum anneali…
The writers propose a mathematical Method for deriving risk weights which describe how a borrower's income, relative to their debt service obligations (serviceability) affects the probability of default of the loan. The Method considers the borrower's income not simply as a known quantity at the time the loan is made, …
Study on Spanish households' investment choices in housing, deposits, and stocks.
In the aftermath of the burst of the ``new economy'' bubble in 2000, the Federal Reserve aggressively reduced short-term rates yields in less than two years from 6.5% to 1.25% in an attempt to coax forth a stronger recovery of the US economy. But, there is growing apprehension that this is creating a new bubble in real…
Study improves survival analysis for credit risk by accounting for data drift.
Study shows stock price interactions increase during crises due to external stimulus.
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.
A censored transformed model for proportional outcomes with boundary mass and an application to loss given default modeling.
Model predicts loan default risk using dynamic multilayer graph neural networks.